A dynamic locality multi-objective salp swarm algorithm for feature selection
•A novel multi-objective SSA algorithm is proposed.•Two essential components; dynamic time-varying strategy and the local fittest solutions are integrated with SSA.•The proposed approach (MODSSA-lbest) is tested on 13 benchmark datasets.•The MODSSA-lbest achieved significant promising results versus...
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| Veröffentlicht in: | Computers & industrial engineering Jg. 147; S. 106628 |
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| Abstract | •A novel multi-objective SSA algorithm is proposed.•Two essential components; dynamic time-varying strategy and the local fittest solutions are integrated with SSA.•The proposed approach (MODSSA-lbest) is tested on 13 benchmark datasets.•The MODSSA-lbest achieved significant promising results versus its counterpart algorithms.
Developing intelligent analytical tools requires pre-processing data and finding relevant features that best reinforce the performance of the predictive algorithms. Feature selection plays a significant role in maximizing the accuracy of machine learning algorithms since the presence of redundant and irrelevant attributes deteriorates the performance of the learning process and increases its complexity. Feature selection is a combinatorial optimization problem that can be formulated as a multi-objective optimization problem with the purpose of maximizing the classification performance and minimizing the number of irrelevant features. It is considered an NP hard optimization problem since having a number of (n) features produces a large search space of size (2n) of different permutations of features. An eminent type of optimizer for tackling such an exhausting search process is evolutionary, which mimic evolutionary processes in nature to solve problems in computers. Salp Swarm Algorithm (SSA) is a well-established metaheuristic that was inspired by the foraging behavior of salps in deep oceans and has proved to be beneficial in estimating global optima for optimization problems. The objective of this article is to promote and boost the performance of the multi-objective SSA for feature selection. Therefore, it proposes an enhanced multi-objective SSA algorithm (MODSSA-lbest) that adopts two essential components: the dynamic time-varying strategy and local fittest solutions. These components assist the SSA algorithm in balancing exploration and exploitation. Thus, it converges faster while avoiding locally optimal solutions. The proposed approach (MODSSA-lbest) is tested on 13 benchmark datasets and compared with the well-regarded Multi-Objective Evolutionary Algorithms (MOEAs). The results show that the MODSSA-lbest achieves significantly promising results versus its counterpart algorithms. |
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| AbstractList | •A novel multi-objective SSA algorithm is proposed.•Two essential components; dynamic time-varying strategy and the local fittest solutions are integrated with SSA.•The proposed approach (MODSSA-lbest) is tested on 13 benchmark datasets.•The MODSSA-lbest achieved significant promising results versus its counterpart algorithms.
Developing intelligent analytical tools requires pre-processing data and finding relevant features that best reinforce the performance of the predictive algorithms. Feature selection plays a significant role in maximizing the accuracy of machine learning algorithms since the presence of redundant and irrelevant attributes deteriorates the performance of the learning process and increases its complexity. Feature selection is a combinatorial optimization problem that can be formulated as a multi-objective optimization problem with the purpose of maximizing the classification performance and minimizing the number of irrelevant features. It is considered an NP hard optimization problem since having a number of (n) features produces a large search space of size (2n) of different permutations of features. An eminent type of optimizer for tackling such an exhausting search process is evolutionary, which mimic evolutionary processes in nature to solve problems in computers. Salp Swarm Algorithm (SSA) is a well-established metaheuristic that was inspired by the foraging behavior of salps in deep oceans and has proved to be beneficial in estimating global optima for optimization problems. The objective of this article is to promote and boost the performance of the multi-objective SSA for feature selection. Therefore, it proposes an enhanced multi-objective SSA algorithm (MODSSA-lbest) that adopts two essential components: the dynamic time-varying strategy and local fittest solutions. These components assist the SSA algorithm in balancing exploration and exploitation. Thus, it converges faster while avoiding locally optimal solutions. The proposed approach (MODSSA-lbest) is tested on 13 benchmark datasets and compared with the well-regarded Multi-Objective Evolutionary Algorithms (MOEAs). The results show that the MODSSA-lbest achieves significantly promising results versus its counterpart algorithms. |
| ArticleNumber | 106628 |
| Author | Heidari, Ali Asghar Mafarja, Majdi Aljarah, Ibrahim Habib, Maria Al-Madi, Nailah Faris, Hossam Elaziz, Mohamed Abd Mirjalili, Seyedali |
| Author_xml | – sequence: 1 givenname: Ibrahim orcidid: 0000-0002-9265-9819 surname: Aljarah fullname: Aljarah, Ibrahim email: i.aljarah@ju.edu.jo organization: King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan – sequence: 2 givenname: Maria surname: Habib fullname: Habib, Maria organization: King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan – sequence: 3 givenname: Hossam orcidid: 0000-0003-4261-8127 surname: Faris fullname: Faris, Hossam email: hossam.faris@ju.edu.jo organization: King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan – sequence: 4 givenname: Nailah surname: Al-Madi fullname: Al-Madi, Nailah email: n.madi@psut.edu.jo organization: Department of Computer Science, Princess Sumaya University for Technology, Amman, Jordan – sequence: 5 givenname: Ali Asghar surname: Heidari fullname: Heidari, Ali Asghar email: as_heidari@ut.ac.ir, aliasghar68@gmail.com, aliasgha@comp.nus.edu.sg, t0917038@u.nus.edu organization: School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran – sequence: 6 givenname: Majdi surname: Mafarja fullname: Mafarja, Majdi email: mmafarja@birzeit.edu organization: Department of Computer Science, Birzeit University, Birzeit, Palestine – sequence: 7 givenname: Mohamed Abd orcidid: 0000-0002-7682-6269 surname: Elaziz fullname: Elaziz, Mohamed Abd email: meahmed@zu.edu.eg organization: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt – sequence: 8 givenname: Seyedali orcidid: 0000-0002-1443-9458 surname: Mirjalili fullname: Mirjalili, Seyedali email: ali.mirjalili@gmail.com organization: Centre for Artificial Intelligence Research and Optimisation Torrens University Australia, Fortitude Valley, Brisbane, 4006 QLD, Australia |
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| Cites_doi | 10.1155/2018/9751783 10.1016/j.compbiomed.2011.02.004 10.1016/j.sigpro.2012.10.022 10.1186/1687-5281-2013-47 10.1016/j.asoc.2017.06.029 10.1016/j.advengsoft.2017.07.002 10.3233/IDA-1997-1302 10.1109/72.572104 10.1016/j.compeleceng.2013.11.024 10.1109/CSIEC.2016.7482135 10.1093/biomet/52.1-2.203 10.1016/j.advengsoft.2013.12.007 10.1016/j.neucom.2012.12.057 10.1016/j.future.2020.03.055 10.1023/A:1022643204877 10.1016/j.asoc.2018.07.040 10.1007/s12559-017-9542-9 10.1016/j.ins.2017.09.028 10.1109/LGRS.2016.2645710 10.1016/j.asoc.2016.10.032 10.1145/1656274.1656278 10.1007/s10489-016-0767-1 10.1016/j.ins.2015.02.031 10.1109/MHS.1995.494215 10.1016/j.eswa.2018.07.013 10.1016/j.procs.2015.09.006 10.1016/j.eswa.2018.04.028 10.1007/s12652-018-1031-9 10.1016/j.future.2019.02.028 10.1016/j.neucom.2018.04.020 10.1109/INTELCIS.2017.8260072 10.1142/S1469026814500096 10.1109/TCBB.2015.2476796 10.1080/08839514.2018.1444334 10.1109/CEC.2000.870311 10.1016/j.knosys.2017.12.037 10.1016/j.swevo.2012.09.002 10.1007/s10489-016-0825-8 10.1016/j.patrec.2015.07.007 10.1007/s11047-007-9050-z 10.1016/j.knosys.2018.05.009 10.1109/CEC.2018.8477975 10.1109/4235.585893 10.1117/1.JATIS.4.3.038001 10.1016/j.eswa.2016.10.015 10.1109/CEC.2002.1004388 10.4018/978-1-4666-5888-2.ch346 10.1016/j.knosys.2018.08.003 10.1109/TEVC.2002.804320 10.1145/347090.347169 10.1109/CEC.2008.4631192 10.1016/j.knosys.2016.10.030 10.1007/3-540-36127-8_27 10.1007/978-1-4615-5725-8_8 10.1016/j.patcog.2009.06.009 10.1007/s00500-016-2063-8 10.1016/j.asoc.2012.11.042 10.1109/TGRS.2017.2748701 10.1186/s13673-019-0174-9 10.1016/j.eswa.2008.07.026 10.1109/TEVC.2007.892759 10.1109/TSMCB.2006.883267 10.1007/s00521-017-2988-6 10.1016/j.eswa.2008.10.013 10.1109/TSMCB.2012.2227469 10.1109/TEVC.2015.2504420 10.1109/CIDM.2009.4938673 10.1007/978-3-319-13563-2_44 10.1016/j.amc.2009.03.090 10.1016/j.advengsoft.2015.01.010 10.1016/S1665-6423(15)30013-4 10.1007/3-540-45356-3_83 10.3390/en11061561 10.1016/j.advengsoft.2016.01.008 10.1016/j.ins.2017.06.039 10.1109/MCI.2019.2919398 |
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| References | Rizk-Allah, Hassanien, Elhoseny, Gunasekaran (b0300) 2019 Reddy, M. J., & Kumar, D. N. (2015). Elitist-mutated multi-objective particle swarm optimization for engineering design. In Yang, J., & Honavar, V. (1998). Feature subset selection using a genetic algorithm. In Han, Kim (b0160) 2002; 6 Ma, Li, Gao, Chen, Ma, Qu (b0235) 2017; 14 Alresheedi, Lu, Elaziz, Ewees (b0015) 2019; 9 (pp. 280–290). Springer. 3rd ed. (pp. 3534–3545). IGI Global. Auer, P., Chiang, C.-K., Ortner, R., & Drugan, M. (2016). Pareto front identification from stochastic bandit feedback. In Xue, Zhang, Browne, Yao (b0405) 2015; 20 Ferranti, Marcelloni, Segatori, Antonelli, Ducange (b0125) 2017; 415 Ibrahim, Elaziz, Lu (b0175) 2018; 108 Kim, Y, Street, W. N., & Menczer, F. (2000). Feature selection in unsupervised learning via evolutionary search. In Bermejo, P., Gámez, J. A., & Puerta, J. M. (2009). Incremental wrapper-based subset selection with replacement: An advantageous alternative to sequential forward selection. In Emary, Yamany, Hassanien, Snasel (b0105) 2015; 65 Schiezaro, Pedrini (b0320) 2013; 2013 Pappa, G. L., Freitas, A. A., & Kaestner, C. A. (2002). Attribute selection with a multi-objective genetic algorithm. In De Souza, R. C. T., dos Santos Coelho, L., De Macedo, C. A., & Pierezan, J. (2018). A v-shaped binary crow search algorithm for feature selection. In Mafarja, Mirjalili (b0230) 2018 Xue, Cervante, Shang, Browne, Zhang (b0385) 2014; 13 IEEE (pp. 1–8). Mirjalili, Lewis (b0265) 2016; 95 Aljarah, Ala’M, Faris, Hassonah, Mirjalili, Saadeh (bib468) 2018; 10 Ibrahim, Ewees, Oliva, Elaziz, Lu (b0180) 2018 Hancer, Xue, Zhang, Karaboga, Akay (b0155) 2018; 422 Mirjalili, Lewis (b0260) 2013; 9 (pp. 849–858). Springer. Mirjalili, Mirjalili, Lewis (b0270) 2014; 69 Sayed, Khoriba, Haggag (b0315) 2018 Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Wang, Wang, Cui, Sun, Zhao, Wang, Xue (b0370) 2018; 69 Mirjalili, Jangir, Saremi (b0255) 2017; 46 (Vol. 1, pp. 309–316). IEEE. 52, 203–224. Mohemmed, A. W., & Zhang, M. (2008). Evaluation of particle swarm optimization based centroid classifier with different distance metrics. In Heidari, Mirjalili, Faris, Aljarah, Mafarja, Chen (bib464) 2019; 97 Ghosh, Datta, Ghosh (b0140) 2013; 13 (pp. 516–528). Springer. Chuang, Yang, Wu, Yang (b0065) 2011; 41 Mafarja, Aljarah, Heidari, Hammouri, Faris, Ala’M, Mirjalili (b0225) 2018; 145 Tian, Cheng, Zhang, Li, Jin (b0350) 2019; 14 Emmanouilidis, C., Hunter, A., & MacIntyre, J., (2000). A multiobjective evolutionary setting for feature selection and a commonality-based crossover operator. In Spolaôr, Lorena, Diana Lee (b0335) 2017; 31 Wang, Huang (b0360) 2009; 36 Zhang, Gong, Sun, Guo (b0435) 2017; 7 Zhu, Liang, Chen, Ming (b0450) 2017; 116 Ibrahim, Elaziz, Ewees, Selim, Lu (b0170) 2018; 4 Wang, Gao, Chen (b0355) 2018; 11 Lichman, M. (2013). UCI machine learning repository. (pp. 39–43). IEEE. Ma, Zhong, He, Zhang (b0240) 2018; 56 Behravan, I., Dehghantanha, O., & Zahiri, S. H. (2016). An optimal svm with feature selection using multi-objective pso. In (pp. 117–136). Springer. Coello, C. C., & Lechuga, M. S. (2002). Mopso: A proposal for multiple objective particle swarm optimization. In Dickson, Wang, Dong, Wen (b0095) 2015 Soliman, Abou-El-Enien, Emary, Khorshid (b0330) 2018; 11 (pp. 315–320). IEEE. (Vol. 2, pp. 1051–1056). IEEE. Langley (b0210) 1994; Vol. 184 Karaboga, Akay (b0190) 2009; 214 Yu, Lu, Yu (b0425) 2018; 2018 Quinlan (b0290) 1986; 1 Chen, Chen, Chen (b0060) 2013; 93 Khan, Baig (b0195) 2015; 13 Aljarah, Mafarja, Heidari, Faris, Zhang, Mirjalili (b0010) 2018; 71 Antonio, Coello (b0025) 2017 Ibrahim, Ewees, Oliva, Elaziz, Lu (b0185) 2019; 10 Mirjalili (b0245) 2015; 83 Yu, L. & Liu, H. (2003). Feature selection for high-dimensional data: A fast correlation-based filter solution. In Guyon, Elisseeff (b0145) 2003; 3 Zhu, Ong, Dash (b0455) 2007; 37 Sarkar, Das, Chaudhuri (b0305) 2017; 50 Setiono, Liu (b0325) 1997; 8 Gheyas, Smith (b0135) 2010; 43 Zhang, Gong, Cheng (b0430) 2015; 14 Chandrashekar, Sahin (b0055) 2014; 40 Coello, Lamont, Van Veldhuizen (b0075) 2007; Vol. 5 (pp. 2929–2932). IEEE. Hussien, A. G., Hassanien, A. E., & Houssein, E. H. (2017). Swarming behaviour of salps algorithm for predicting chemical compound activities. In Tan, Teoh, Yu, Goh (b0345) 2009; 36 Xue, Zhang, Browne (b0395) 2012; 43 Li, Chen, Wang, Heidari, Mirjalili (bib465) 2020; 111 Zhang, Li (b0440) 2007; 11 Wang, Li, Li (b0365) 2015; 307 Yao, Ding, Jin, Hao (b0415) 2017; 21 Dash, Liu (b0080) 1997; 1 Gehan, E. A. (1965). A generalized wilcoxon test for comparing arbitrarily singly-censored samples. Mirjalili, Gandomi, Mirjalili, Saremi, Faris, Mirjalili (b0250) 2017 Wolpert, Macready (b0380) 1997; 1 (pp. 939–947). Bandaru, Ng, Deb (b0035) 2017; 70 Hall, Frank, Holmes, Pfahringer, Reutemann, Witten (b0150) 2009; 11 Amoozegar, Minaei-Bidgoli (b0020) 2018; 113 Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: Nsga-ii. In (pp. 856–863). Witten, Frank, Hall, Pal (b0375) 2016 (pp. 365–369). Sayed, Hassanien, Azar (b0310) 2019; 31 Faris, Aljarah, Mirjalili (b0115) 2016; 45 Tan, Lim, Cheah (b0340) 2014; 125 Xue, B., Fu, W., & Zhang, M. (2014). Multi-objective feature selection in classification: a differential evolution approach. In Mafarja, Aljarah, Heidari, Faris, Fournier-Viger, Li, Mirjalili (b0220) 2018; 161 (pp. 76–81). IEEE. Paul, Das (b0285) 2015; 65 Banks, Vincent, Anyakoha (b0040) 2008; 7 (pp. 367–374). IEEE. Faris, Mafarja, Heidari, Aljarah, Ala’M, Mirjalili, Fujita (b0120) 2018; 154 Xue, Zhang, Browne (b0400) 2013; 43 Kiziloz, Deniz, Dokeroglu, Cosar (b0205) 2018; 306 Yao (10.1016/j.cie.2020.106628_b0415) 2017; 21 Dash (10.1016/j.cie.2020.106628_b0080) 1997; 1 Soliman (10.1016/j.cie.2020.106628_b0330) 2018; 11 Alresheedi (10.1016/j.cie.2020.106628_b0015) 2019; 9 Tian (10.1016/j.cie.2020.106628_b0350) 2019; 14 Li (10.1016/j.cie.2020.106628_bib465) 2020; 111 Mirjalili (10.1016/j.cie.2020.106628_b0250) 2017 Zhang (10.1016/j.cie.2020.106628_b0430) 2015; 14 Ibrahim (10.1016/j.cie.2020.106628_b0185) 2019; 10 Heidari (10.1016/j.cie.2020.106628_bib464) 2019; 97 Ibrahim (10.1016/j.cie.2020.106628_b0180) 2018 10.1016/j.cie.2020.106628_b0110 Hancer (10.1016/j.cie.2020.106628_b0155) 2018; 422 Mirjalili (10.1016/j.cie.2020.106628_b0265) 2016; 95 10.1016/j.cie.2020.106628_b0275 Schiezaro (10.1016/j.cie.2020.106628_b0320) 2013; 2013 10.1016/j.cie.2020.106628_b0030 Ferranti (10.1016/j.cie.2020.106628_b0125) 2017; 415 Aljarah (10.1016/j.cie.2020.106628_b0010) 2018; 71 Tan (10.1016/j.cie.2020.106628_b0345) 2009; 36 Antonio (10.1016/j.cie.2020.106628_b0025) 2017 Zhu (10.1016/j.cie.2020.106628_b0455) 2007; 37 Gheyas (10.1016/j.cie.2020.106628_b0135) 2010; 43 10.1016/j.cie.2020.106628_b0390 10.1016/j.cie.2020.106628_b0070 Khan (10.1016/j.cie.2020.106628_b0195) 2015; 13 Dickson (10.1016/j.cie.2020.106628_b0095) 2015 Setiono (10.1016/j.cie.2020.106628_b0325) 1997; 8 Zhu (10.1016/j.cie.2020.106628_b0450) 2017; 116 Faris (10.1016/j.cie.2020.106628_b0115) 2016; 45 Wang (10.1016/j.cie.2020.106628_b0370) 2018; 69 Chandrashekar (10.1016/j.cie.2020.106628_b0055) 2014; 40 Chuang (10.1016/j.cie.2020.106628_b0065) 2011; 41 Spolaôr (10.1016/j.cie.2020.106628_b0335) 2017; 31 Langley (10.1016/j.cie.2020.106628_b0210) 1994; Vol. 184 10.1016/j.cie.2020.106628_b0165 Sayed (10.1016/j.cie.2020.106628_b0310) 2019; 31 Xue (10.1016/j.cie.2020.106628_b0405) 2015; 20 Mirjalili (10.1016/j.cie.2020.106628_b0270) 2014; 69 Banks (10.1016/j.cie.2020.106628_b0040) 2008; 7 10.1016/j.cie.2020.106628_b0085 Mafarja (10.1016/j.cie.2020.106628_b0220) 2018; 161 10.1016/j.cie.2020.106628_b0200 10.1016/j.cie.2020.106628_b0045 Aljarah (10.1016/j.cie.2020.106628_bib468) 2018; 10 Karaboga (10.1016/j.cie.2020.106628_b0190) 2009; 214 Mirjalili (10.1016/j.cie.2020.106628_b0260) 2013; 9 10.1016/j.cie.2020.106628_b0280 Hall (10.1016/j.cie.2020.106628_b0150) 2009; 11 Coello (10.1016/j.cie.2020.106628_b0075) 2007; Vol. 5 Ma (10.1016/j.cie.2020.106628_b0235) 2017; 14 Mirjalili (10.1016/j.cie.2020.106628_b0255) 2017; 46 Ibrahim (10.1016/j.cie.2020.106628_b0175) 2018; 108 Ibrahim (10.1016/j.cie.2020.106628_b0170) 2018; 4 10.1016/j.cie.2020.106628_b0215 Mafarja (10.1016/j.cie.2020.106628_b0230) 2018 Sarkar (10.1016/j.cie.2020.106628_b0305) 2017; 50 Witten (10.1016/j.cie.2020.106628_b0375) 2016 Mafarja (10.1016/j.cie.2020.106628_b0225) 2018; 145 Faris (10.1016/j.cie.2020.106628_b0120) 2018; 154 10.1016/j.cie.2020.106628_b0130 10.1016/j.cie.2020.106628_b0295 Ghosh (10.1016/j.cie.2020.106628_b0140) 2013; 13 10.1016/j.cie.2020.106628_b0410 Xue (10.1016/j.cie.2020.106628_b0395) 2012; 43 Emary (10.1016/j.cie.2020.106628_b0105) 2015; 65 Bandaru (10.1016/j.cie.2020.106628_b0035) 2017; 70 10.1016/j.cie.2020.106628_b0090 Wang (10.1016/j.cie.2020.106628_b0365) 2015; 307 Zhang (10.1016/j.cie.2020.106628_b0435) 2017; 7 Han (10.1016/j.cie.2020.106628_b0160) 2002; 6 10.1016/j.cie.2020.106628_b0050 Sayed (10.1016/j.cie.2020.106628_b0315) 2018 Wang (10.1016/j.cie.2020.106628_b0355) 2018; 11 Tan (10.1016/j.cie.2020.106628_b0340) 2014; 125 Yu (10.1016/j.cie.2020.106628_b0425) 2018; 2018 Rizk-Allah (10.1016/j.cie.2020.106628_b0300) 2019 Xue (10.1016/j.cie.2020.106628_b0400) 2013; 43 Chen (10.1016/j.cie.2020.106628_b0060) 2013; 93 Wolpert (10.1016/j.cie.2020.106628_b0380) 1997; 1 Quinlan (10.1016/j.cie.2020.106628_b0290) 1986; 1 Wang (10.1016/j.cie.2020.106628_b0360) 2009; 36 Mirjalili (10.1016/j.cie.2020.106628_b0245) 2015; 83 Guyon (10.1016/j.cie.2020.106628_b0145) 2003; 3 Amoozegar (10.1016/j.cie.2020.106628_b0020) 2018; 113 Zhang (10.1016/j.cie.2020.106628_b0440) 2007; 11 Ma (10.1016/j.cie.2020.106628_b0240) 2018; 56 Xue (10.1016/j.cie.2020.106628_b0385) 2014; 13 10.1016/j.cie.2020.106628_b0420 10.1016/j.cie.2020.106628_b0100 Kiziloz (10.1016/j.cie.2020.106628_b0205) 2018; 306 Paul (10.1016/j.cie.2020.106628_b0285) 2015; 65 |
| References_xml | – volume: 10 start-page: 3155 year: 2019 end-page: 3169 ident: b0185 article-title: Improved salp swarm algorithm based on particle swarm optimization for feature selection publication-title: Journal of Ambient Intelligence and Humanized Computing – volume: 69 start-page: 46 year: 2014 end-page: 61 ident: b0270 article-title: Grey wolf optimizer publication-title: Advances in Engineering Software – reference: Behravan, I., Dehghantanha, O., & Zahiri, S. H. (2016). An optimal svm with feature selection using multi-objective pso. In – reference: (Vol. 1, pp. 309–316). IEEE. – reference: Xue, B., Fu, W., & Zhang, M. (2014). Multi-objective feature selection in classification: a differential evolution approach. In – reference: Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: Nsga-ii. In – reference: Hussien, A. G., Hassanien, A. E., & Houssein, E. H. (2017). Swarming behaviour of salps algorithm for predicting chemical compound activities. In – volume: 116 start-page: 74 year: 2017 end-page: 85 ident: b0450 article-title: An improved nsga-iii algorithm for feature selection used in intrusion detection publication-title: Knowledge-Based Systems – volume: 10 start-page: 478 year: 2018 end-page: 495 ident: bib468 article-title: Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm publication-title: Cognitive Computation – reference: 52, 203–224. – volume: 65 start-page: 51 year: 2015 end-page: 59 ident: b0285 article-title: Simultaneous feature selection and weighting–an evolutionary multi-objective optimization approach publication-title: Pattern Recognition Letters – reference: Auer, P., Chiang, C.-K., Ortner, R., & Drugan, M. (2016). Pareto front identification from stochastic bandit feedback. In – reference: Emmanouilidis, C., Hunter, A., & MacIntyre, J., (2000). A multiobjective evolutionary setting for feature selection and a commonality-based crossover operator. In – volume: 9 start-page: 15 year: 2019 ident: b0015 article-title: Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing publication-title: Human-centric Computing and Information Sciences – volume: 31 start-page: 764 year: 2017 end-page: 791 ident: b0335 article-title: Feature selection via pareto multi-objective genetic algorithms publication-title: Applied Artificial Intelligence – reference: (pp. 2929–2932). IEEE. – reference: Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In – volume: 40 start-page: 16 year: 2014 end-page: 28 ident: b0055 article-title: A survey on feature selection methods publication-title: Computers & Electrical Engineering – volume: 70 start-page: 139 year: 2017 end-page: 159 ident: b0035 article-title: Data mining methods for knowledge discovery in multi-objective optimization: Part a-survey publication-title: Expert Systems with Applications – volume: 108 start-page: 1 year: 2018 end-page: 27 ident: b0175 article-title: Chaotic opposition-based grey-wolf optimization algorithm based on differential evolution and disruption operator for global optimization publication-title: Expert Systems with Applications – start-page: 1 year: 2018 end-page: 17 ident: b0230 article-title: Hybrid binary ant lion optimizer with rough set and approximate entropy reducts for feature selection publication-title: Soft Computing – volume: 43 start-page: 1656 year: 2013 end-page: 1671 ident: b0400 article-title: Particle swarm optimization for feature selection in classification: A multi-objective approach publication-title: IEEE Transactions on Cybernetics – volume: 113 start-page: 499 year: 2018 end-page: 514 ident: b0020 article-title: Optimizing multi-objective pso based feature selection method using a feature elitism mechanism publication-title: Expert Systems with Applications – reference: De Souza, R. C. T., dos Santos Coelho, L., De Macedo, C. A., & Pierezan, J. (2018). A v-shaped binary crow search algorithm for feature selection. In – reference: , 3rd ed. (pp. 3534–3545). IGI Global. – reference: Mohemmed, A. W., & Zhang, M. (2008). Evaluation of particle swarm optimization based centroid classifier with different distance metrics. In – reference: (pp. 367–374). IEEE. – reference: Coello, C. C., & Lechuga, M. S. (2002). Mopso: A proposal for multiple objective particle swarm optimization. In – volume: 20 start-page: 606 year: 2015 end-page: 626 ident: b0405 article-title: A survey on evolutionary computation approaches to feature selection publication-title: IEEE Transactions on Evolutionary Computation – reference: Gehan, E. A. (1965). A generalized wilcoxon test for comparing arbitrarily singly-censored samples. – reference: Pappa, G. L., Freitas, A. A., & Kaestner, C. A. (2002). Attribute selection with a multi-objective genetic algorithm. In: – volume: 161 start-page: 185 year: 2018 end-page: 204 ident: b0220 article-title: Binary dragonfly optimization for feature selection using time-varying transfer functions publication-title: Knowledge-Based Systems – reference: . IEEE (pp. 1–8). – volume: 50 start-page: 142 year: 2017 end-page: 157 ident: b0305 article-title: Multi-level thresholding with a decomposition-based multi-objective evolutionary algorithm for segmenting natural and medical images publication-title: Applied Soft Computing – volume: 13 start-page: 1450009 year: 2014 ident: b0385 article-title: Binary pso and rough set theory for feature selection: A multi-objective filter based approach publication-title: International Journal of Computational Intelligence and Applications – volume: 306 start-page: 94 year: 2018 end-page: 107 ident: b0205 article-title: Novel multiobjective tlbo algorithms for the feature subset selection problem publication-title: Neurocomputing – volume: 7 start-page: 1 year: 2017 end-page: 12 ident: b0435 article-title: A pso-based multi-objective multi-label feature selection method in classification publication-title: Scientific Reports – reference: (pp. 516–528). Springer. – volume: 46 start-page: 79 year: 2017 end-page: 95 ident: b0255 article-title: Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems publication-title: Applied Intelligence – volume: 2013 start-page: 47 year: 2013 ident: b0320 article-title: Data feature selection based on artificial bee colony algorithm publication-title: EURASIP Journal on Image and Video Processing – reference: (pp. 365–369). – volume: 11 start-page: 712 year: 2007 end-page: 731 ident: b0440 article-title: Moea/d: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Transactions on Evolutionary Computation – reference: (pp. 315–320). IEEE. – volume: 4 start-page: 038001 year: 2018 ident: b0170 article-title: Galaxy images classification using hybrid brain storm optimization with moth flame optimization publication-title: Journal of Astronomical Telescopes, Instruments, and Systems – volume: 21 start-page: 4309 year: 2017 end-page: 4322 ident: b0415 article-title: Endocrine-based coevolutionary multi-swarm for multi-objective workflow scheduling in a cloud system publication-title: Soft Computing – reference: (pp. 117–136). Springer. – year: 2017 ident: b0025 article-title: Coevolutionary multi-objective evolutionary algorithms: A survey of the state-of-the-art publication-title: IEEE Transactions on Evolutionary Computation – volume: 13 start-page: 1969 year: 2013 end-page: 1977 ident: b0140 article-title: Self-adaptive differential evolution for feature selection in hyperspectral image data publication-title: Applied Soft Computing – reference: (pp. 856–863). – volume: 31 start-page: 171 year: 2019 end-page: 188 ident: b0310 article-title: Feature selection via a novel chaotic crow search algorithm publication-title: Neural Computing and Applications – volume: 65 start-page: 623 year: 2015 end-page: 632 ident: b0105 article-title: Multi-objective gray-wolf optimization for attribute reduction publication-title: Procedia Computer Science – volume: 307 start-page: 73 year: 2015 end-page: 88 ident: b0365 article-title: A multi-objective evolutionary algorithm for feature selection based on mutual information with a new redundancy measure publication-title: Information Sciences – reference: Lichman, M. (2013). UCI machine learning repository. – volume: 14 start-page: 409 year: 2017 end-page: 413 ident: b0235 article-title: A novel wrapper approach for feature selection in object-based image classification using polygon-based cross-validation publication-title: IEEE Geoscience and Remote Sensing Letters – volume: 11 start-page: 10 year: 2009 end-page: 18 ident: b0150 article-title: The weka data mining software: an update publication-title: ACM SIGKDD Explorations Newsletter – volume: 41 start-page: 228 year: 2011 end-page: 237 ident: b0065 article-title: A hybrid feature selection method for dna microarray data publication-title: Computers in Biology and Medicine – volume: 1 start-page: 67 year: 1997 end-page: 82 ident: b0380 article-title: No free lunch theorems for optimization publication-title: IEEE Transactions on Evolutionary Computation – volume: 214 start-page: 108 year: 2009 end-page: 132 ident: b0190 article-title: A comparative study of artificial bee colony algorithm publication-title: Applied Mathematics and Computation – volume: 97 start-page: 849 year: 2019 end-page: 872 ident: bib464 article-title: Harris hawks optimization: Algorithm and applications publication-title: Future Generation Computer Systems – reference: (pp. 76–81). IEEE. – reference: Reddy, M. J., & Kumar, D. N. (2015). Elitist-mutated multi-objective particle swarm optimization for engineering design. In – volume: 8 start-page: 654 year: 1997 end-page: 662 ident: b0325 article-title: Neural-network feature selector publication-title: IEEE Transactions on Neural Networks – volume: 95 start-page: 51 year: 2016 end-page: 67 ident: b0265 article-title: The whale optimization algorithm publication-title: Advances in Engineering Software – reference: Yu, L. & Liu, H. (2003). Feature selection for high-dimensional data: A fast correlation-based filter solution. In – volume: Vol. 5 year: 2007 ident: b0075 publication-title: Evolutionary algorithms for solving multi-objective problems – volume: 111 start-page: 300 year: 2020 end-page: 323 ident: bib465 article-title: Slime mould algorithm: A new method for stochastic optimization publication-title: Future Generation Computer Systems – volume: 43 start-page: 1656 year: 2012 end-page: 1671 ident: b0395 article-title: Particle swarm optimization for feature selection in classification: A multi-objective approach publication-title: IEEE Transactions on Cybernetics – start-page: 549 year: 2015 end-page: 558 ident: b0095 article-title: A feature selection method based on multi-objective optimisation with gravitational search algorithm publication-title: Geo-Informatics in Resource Management and Sustainable Ecosystem – volume: 125 start-page: 217 year: 2014 end-page: 228 ident: b0340 article-title: A multi-objective evolutionary algorithm-based ensemble optimizer for feature selection and classification with neural network models publication-title: Neurocomputing – reference: (Vol. 2, pp. 1051–1056). IEEE. – year: 2016 ident: b0375 article-title: Data Mining: Practical machine learning tools and techniques – volume: 36 start-page: 5900 year: 2009 end-page: 5908 ident: b0360 article-title: Evolutionary-based feature selection approaches with new criteria for data mining: A case study of credit approval data publication-title: Expert Systems with Applications – reference: (pp. 849–858). Springer. – volume: 422 start-page: 462 year: 2018 end-page: 479 ident: b0155 article-title: Pareto front feature selection based on artificial bee colony optimization publication-title: Information Sciences – volume: 56 start-page: 422 year: 2018 end-page: 435 ident: b0240 article-title: Multiobjective subpixel land-cover mapping publication-title: IEEE Transactions on Geoscience and Remote Sensing – start-page: 1 year: 2018 end-page: 20 ident: b0315 article-title: A novel chaotic salp swarm algorithm for global optimization and feature selection publication-title: Applied Intelligence – reference: (pp. 39–43). IEEE. – volume: 11 start-page: 1561 year: 2018 ident: b0355 article-title: A novel hybrid interval prediction approach based on modified lower upper bound estimation in combination with multi-objective salp swarm algorithm for short-term load forecasting publication-title: Energies – reference: Bermejo, P., Gámez, J. A., & Puerta, J. M. (2009). Incremental wrapper-based subset selection with replacement: An advantageous alternative to sequential forward selection. In – volume: 43 start-page: 5 year: 2010 end-page: 13 ident: b0135 article-title: Feature subset selection in large dimensionality domains publication-title: Pattern Recognition – reference: (pp. 939–947). – volume: 37 start-page: 70 year: 2007 end-page: 76 ident: b0455 article-title: Wrapper–filter feature selection algorithm using a memetic framework publication-title: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) – volume: 6 start-page: 580 year: 2002 end-page: 593 ident: b0160 article-title: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization publication-title: IEEE Transactions on Evolutionary Computation – volume: 415 start-page: 319 year: 2017 end-page: 340 ident: b0125 article-title: A distributed approach to multi-objective evolutionary generation of fuzzy rule-based classifiers from big data publication-title: Information Sciences – volume: 1 start-page: 81 year: 1986 end-page: 106 ident: b0290 article-title: Induction of decision trees publication-title: Machine Learning – volume: 154 start-page: 43 year: 2018 end-page: 67 ident: b0120 article-title: An efficient binary salp swarm algorithm with crossover scheme for feature selection problems publication-title: Knowledge-Based Systems – volume: 14 start-page: 61 year: 2019 end-page: 74 ident: b0350 article-title: Diversity assessment of multi-objective evolutionary algorithms: Performance metric and benchmark problems [research frontier] publication-title: IEEE Computational Intelligence Magazine – volume: 7 start-page: 109 year: 2008 end-page: 124 ident: b0040 article-title: A review of particle swarm optimization. part ii: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications publication-title: Natural Computing – volume: 83 start-page: 80 year: 2015 end-page: 98 ident: b0245 article-title: The ant lion optimizer publication-title: Advances in Engineering Software – reference: Kim, Y, Street, W. N., & Menczer, F. (2000). Feature selection in unsupervised learning via evolutionary search. In – volume: 3 start-page: 1157 year: 2003 end-page: 1182 ident: b0145 article-title: An introduction to variable and feature selection publication-title: Journal of Machine Learning Research – year: 2017 ident: b0250 article-title: Salp swarm algorithm: A bio-inspired optimizer for engineering design problems publication-title: Advances in Engineering Software – reference: Yang, J., & Honavar, V. (1998). Feature subset selection using a genetic algorithm. In – volume: 9 start-page: 1 year: 2013 end-page: 14 ident: b0260 article-title: S-shaped versus v-shaped transfer functions for binary particle swarm optimization publication-title: Swarm and Evolutionary Computation – volume: 145 start-page: 25 year: 2018 end-page: 45 ident: b0225 article-title: Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems publication-title: Knowledge-Based Systems – volume: 71 start-page: 964 year: 2018 end-page: 979 ident: b0010 article-title: Asynchronous accelerating multi-leader salp chains for feature selection publication-title: Applied Soft Computing – volume: 69 start-page: 806 year: 2018 end-page: 815 ident: b0370 article-title: A hybrid multi-objective firefly algorithm for big data optimization publication-title: Applied Soft Computing – volume: 2018 year: 2018 ident: b0425 article-title: Evaluating multiobjective evolutionary algorithms using mcdm methods publication-title: Mathematical Problems in Engineering – volume: 1 start-page: 131 year: 1997 end-page: 156 ident: b0080 article-title: Feature selection for classification publication-title: Intelligent Data Analysis – volume: 45 start-page: 322 year: 2016 end-page: 332 ident: b0115 article-title: Training feedforward neural networks using multi-verse optimizer for binary classification problems publication-title: Applied Intelligence – volume: 13 start-page: 145 year: 2015 end-page: 159 ident: b0195 article-title: Multi-objective feature subset selection using non-dominated sorting genetic algorithm publication-title: Journal of Applied Research and Technology – start-page: 1 year: 2018 end-page: 15 ident: b0180 article-title: Improved salp swarm algorithm based on particle swarm optimization for feature selection publication-title: Journal of Ambient Intelligence and Humanized Computing – reference: (pp. 280–290). Springer. – volume: 14 start-page: 64 year: 2015 end-page: 75 ident: b0430 article-title: Multi-objective particle swarm optimization approach for cost-based feature selection in classification publication-title: IEEE/ACM Transactions on Computational Biology and Bioinformatics – volume: 36 start-page: 8616 year: 2009 end-page: 8630 ident: b0345 article-title: A hybrid evolutionary algorithm for attribute selection in data mining publication-title: Expert Systems with Applications – volume: 93 start-page: 1566 year: 2013 end-page: 1576 ident: b0060 article-title: Efficient ant colony optimization for image feature selection publication-title: Signal Processing – volume: Vol. 184 start-page: 245 year: 1994 end-page: 271 ident: b0210 article-title: Selection of relevant features in machine learning publication-title: Proceedings of the AAAI Fall symposium on relevance – start-page: 1 year: 2019 end-page: 23 ident: b0300 article-title: A new binary salp swarm algorithm: development and application for optimization tasks publication-title: Neural Computing and Applications – volume: 11 start-page: 1 year: 2018 end-page: 13 ident: b0330 article-title: A novel multi-objective moth-flame optimization algorithm for feature selection publication-title: Indian Journal of Science and Technology – volume: 2018 year: 2018 ident: 10.1016/j.cie.2020.106628_b0425 article-title: Evaluating multiobjective evolutionary algorithms using mcdm methods publication-title: Mathematical Problems in Engineering doi: 10.1155/2018/9751783 – start-page: 549 year: 2015 ident: 10.1016/j.cie.2020.106628_b0095 article-title: A feature selection method based on multi-objective optimisation with gravitational search algorithm – start-page: 1 year: 2018 ident: 10.1016/j.cie.2020.106628_b0315 article-title: A novel chaotic salp swarm algorithm for global optimization and feature selection publication-title: Applied Intelligence – volume: 41 start-page: 228 year: 2011 ident: 10.1016/j.cie.2020.106628_b0065 article-title: A hybrid feature selection method for dna microarray data publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2011.02.004 – volume: 93 start-page: 1566 year: 2013 ident: 10.1016/j.cie.2020.106628_b0060 article-title: Efficient ant colony optimization for image feature selection publication-title: Signal Processing doi: 10.1016/j.sigpro.2012.10.022 – ident: 10.1016/j.cie.2020.106628_b0215 – volume: 2013 start-page: 47 year: 2013 ident: 10.1016/j.cie.2020.106628_b0320 article-title: Data feature selection based on artificial bee colony algorithm publication-title: EURASIP Journal on Image and Video Processing doi: 10.1186/1687-5281-2013-47 – volume: 69 start-page: 806 year: 2018 ident: 10.1016/j.cie.2020.106628_b0370 article-title: A hybrid multi-objective firefly algorithm for big data optimization publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2017.06.029 – year: 2017 ident: 10.1016/j.cie.2020.106628_b0250 article-title: Salp swarm algorithm: A bio-inspired optimizer for engineering design problems publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2017.07.002 – year: 2017 ident: 10.1016/j.cie.2020.106628_b0025 article-title: Coevolutionary multi-objective evolutionary algorithms: A survey of the state-of-the-art publication-title: IEEE Transactions on Evolutionary Computation – ident: 10.1016/j.cie.2020.106628_b0420 – volume: 1 start-page: 131 year: 1997 ident: 10.1016/j.cie.2020.106628_b0080 article-title: Feature selection for classification publication-title: Intelligent Data Analysis doi: 10.3233/IDA-1997-1302 – volume: 8 start-page: 654 year: 1997 ident: 10.1016/j.cie.2020.106628_b0325 article-title: Neural-network feature selector publication-title: IEEE Transactions on Neural Networks doi: 10.1109/72.572104 – volume: 40 start-page: 16 year: 2014 ident: 10.1016/j.cie.2020.106628_b0055 article-title: A survey on feature selection methods publication-title: Computers & Electrical Engineering doi: 10.1016/j.compeleceng.2013.11.024 – ident: 10.1016/j.cie.2020.106628_b0045 doi: 10.1109/CSIEC.2016.7482135 – ident: 10.1016/j.cie.2020.106628_b0130 doi: 10.1093/biomet/52.1-2.203 – volume: 69 start-page: 46 year: 2014 ident: 10.1016/j.cie.2020.106628_b0270 article-title: Grey wolf optimizer publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2013.12.007 – volume: 125 start-page: 217 year: 2014 ident: 10.1016/j.cie.2020.106628_b0340 article-title: A multi-objective evolutionary algorithm-based ensemble optimizer for feature selection and classification with neural network models publication-title: Neurocomputing doi: 10.1016/j.neucom.2012.12.057 – volume: 111 start-page: 300 year: 2020 ident: 10.1016/j.cie.2020.106628_bib465 article-title: Slime mould algorithm: A new method for stochastic optimization publication-title: Future Generation Computer Systems doi: 10.1016/j.future.2020.03.055 – volume: 1 start-page: 81 year: 1986 ident: 10.1016/j.cie.2020.106628_b0290 article-title: Induction of decision trees publication-title: Machine Learning doi: 10.1023/A:1022643204877 – volume: 3 start-page: 1157 year: 2003 ident: 10.1016/j.cie.2020.106628_b0145 article-title: An introduction to variable and feature selection publication-title: Journal of Machine Learning Research – volume: 71 start-page: 964 year: 2018 ident: 10.1016/j.cie.2020.106628_b0010 article-title: Asynchronous accelerating multi-leader salp chains for feature selection publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2018.07.040 – volume: 10 start-page: 478 issue: 3 year: 2018 ident: 10.1016/j.cie.2020.106628_bib468 article-title: Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm publication-title: Cognitive Computation doi: 10.1007/s12559-017-9542-9 – volume: 422 start-page: 462 year: 2018 ident: 10.1016/j.cie.2020.106628_b0155 article-title: Pareto front feature selection based on artificial bee colony optimization publication-title: Information Sciences doi: 10.1016/j.ins.2017.09.028 – volume: 14 start-page: 409 year: 2017 ident: 10.1016/j.cie.2020.106628_b0235 article-title: A novel wrapper approach for feature selection in object-based image classification using polygon-based cross-validation publication-title: IEEE Geoscience and Remote Sensing Letters doi: 10.1109/LGRS.2016.2645710 – volume: 50 start-page: 142 year: 2017 ident: 10.1016/j.cie.2020.106628_b0305 article-title: Multi-level thresholding with a decomposition-based multi-objective evolutionary algorithm for segmenting natural and medical images publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2016.10.032 – volume: 11 start-page: 10 year: 2009 ident: 10.1016/j.cie.2020.106628_b0150 article-title: The weka data mining software: an update publication-title: ACM SIGKDD Explorations Newsletter doi: 10.1145/1656274.1656278 – volume: 45 start-page: 322 year: 2016 ident: 10.1016/j.cie.2020.106628_b0115 article-title: Training feedforward neural networks using multi-verse optimizer for binary classification problems publication-title: Applied Intelligence doi: 10.1007/s10489-016-0767-1 – volume: 11 start-page: 1 year: 2018 ident: 10.1016/j.cie.2020.106628_b0330 article-title: A novel multi-objective moth-flame optimization algorithm for feature selection publication-title: Indian Journal of Science and Technology – volume: 307 start-page: 73 year: 2015 ident: 10.1016/j.cie.2020.106628_b0365 article-title: A multi-objective evolutionary algorithm for feature selection based on mutual information with a new redundancy measure publication-title: Information Sciences doi: 10.1016/j.ins.2015.02.031 – ident: 10.1016/j.cie.2020.106628_b0100 doi: 10.1109/MHS.1995.494215 – volume: 113 start-page: 499 year: 2018 ident: 10.1016/j.cie.2020.106628_b0020 article-title: Optimizing multi-objective pso based feature selection method using a feature elitism mechanism publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2018.07.013 – volume: 65 start-page: 623 year: 2015 ident: 10.1016/j.cie.2020.106628_b0105 article-title: Multi-objective gray-wolf optimization for attribute reduction publication-title: Procedia Computer Science doi: 10.1016/j.procs.2015.09.006 – volume: 108 start-page: 1 year: 2018 ident: 10.1016/j.cie.2020.106628_b0175 article-title: Chaotic opposition-based grey-wolf optimization algorithm based on differential evolution and disruption operator for global optimization publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2018.04.028 – volume: 10 start-page: 3155 year: 2019 ident: 10.1016/j.cie.2020.106628_b0185 article-title: Improved salp swarm algorithm based on particle swarm optimization for feature selection publication-title: Journal of Ambient Intelligence and Humanized Computing doi: 10.1007/s12652-018-1031-9 – volume: 97 start-page: 849 year: 2019 ident: 10.1016/j.cie.2020.106628_bib464 article-title: Harris hawks optimization: Algorithm and applications publication-title: Future Generation Computer Systems doi: 10.1016/j.future.2019.02.028 – volume: 306 start-page: 94 year: 2018 ident: 10.1016/j.cie.2020.106628_b0205 article-title: Novel multiobjective tlbo algorithms for the feature subset selection problem publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.04.020 – ident: 10.1016/j.cie.2020.106628_b0165 doi: 10.1109/INTELCIS.2017.8260072 – volume: 13 start-page: 1450009 year: 2014 ident: 10.1016/j.cie.2020.106628_b0385 article-title: Binary pso and rough set theory for feature selection: A multi-objective filter based approach publication-title: International Journal of Computational Intelligence and Applications doi: 10.1142/S1469026814500096 – volume: 14 start-page: 64 year: 2015 ident: 10.1016/j.cie.2020.106628_b0430 article-title: Multi-objective particle swarm optimization approach for cost-based feature selection in classification publication-title: IEEE/ACM Transactions on Computational Biology and Bioinformatics doi: 10.1109/TCBB.2015.2476796 – volume: 31 start-page: 764 year: 2017 ident: 10.1016/j.cie.2020.106628_b0335 article-title: Feature selection via pareto multi-objective genetic algorithms publication-title: Applied Artificial Intelligence doi: 10.1080/08839514.2018.1444334 – ident: 10.1016/j.cie.2020.106628_b0110 doi: 10.1109/CEC.2000.870311 – volume: 145 start-page: 25 year: 2018 ident: 10.1016/j.cie.2020.106628_b0225 article-title: Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2017.12.037 – start-page: 1 year: 2018 ident: 10.1016/j.cie.2020.106628_b0230 article-title: Hybrid binary ant lion optimizer with rough set and approximate entropy reducts for feature selection publication-title: Soft Computing – volume: 9 start-page: 1 year: 2013 ident: 10.1016/j.cie.2020.106628_b0260 article-title: S-shaped versus v-shaped transfer functions for binary particle swarm optimization publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2012.09.002 – year: 2016 ident: 10.1016/j.cie.2020.106628_b0375 – volume: 46 start-page: 79 year: 2017 ident: 10.1016/j.cie.2020.106628_b0255 article-title: Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems publication-title: Applied Intelligence doi: 10.1007/s10489-016-0825-8 – volume: 65 start-page: 51 year: 2015 ident: 10.1016/j.cie.2020.106628_b0285 article-title: Simultaneous feature selection and weighting–an evolutionary multi-objective optimization approach publication-title: Pattern Recognition Letters doi: 10.1016/j.patrec.2015.07.007 – volume: 7 start-page: 109 year: 2008 ident: 10.1016/j.cie.2020.106628_b0040 article-title: A review of particle swarm optimization. part ii: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications publication-title: Natural Computing doi: 10.1007/s11047-007-9050-z – volume: 154 start-page: 43 year: 2018 ident: 10.1016/j.cie.2020.106628_b0120 article-title: An efficient binary salp swarm algorithm with crossover scheme for feature selection problems publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2018.05.009 – volume: Vol. 5 year: 2007 ident: 10.1016/j.cie.2020.106628_b0075 – ident: 10.1016/j.cie.2020.106628_b0090 doi: 10.1109/CEC.2018.8477975 – volume: 1 start-page: 67 year: 1997 ident: 10.1016/j.cie.2020.106628_b0380 article-title: No free lunch theorems for optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.585893 – volume: 4 start-page: 038001 year: 2018 ident: 10.1016/j.cie.2020.106628_b0170 article-title: Galaxy images classification using hybrid brain storm optimization with moth flame optimization publication-title: Journal of Astronomical Telescopes, Instruments, and Systems doi: 10.1117/1.JATIS.4.3.038001 – volume: 70 start-page: 139 year: 2017 ident: 10.1016/j.cie.2020.106628_b0035 article-title: Data mining methods for knowledge discovery in multi-objective optimization: Part a-survey publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2016.10.015 – ident: 10.1016/j.cie.2020.106628_b0070 doi: 10.1109/CEC.2002.1004388 – ident: 10.1016/j.cie.2020.106628_b0030 – ident: 10.1016/j.cie.2020.106628_b0295 doi: 10.4018/978-1-4666-5888-2.ch346 – volume: 161 start-page: 185 year: 2018 ident: 10.1016/j.cie.2020.106628_b0220 article-title: Binary dragonfly optimization for feature selection using time-varying transfer functions publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2018.08.003 – volume: 6 start-page: 580 year: 2002 ident: 10.1016/j.cie.2020.106628_b0160 article-title: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2002.804320 – ident: 10.1016/j.cie.2020.106628_b0200 doi: 10.1145/347090.347169 – ident: 10.1016/j.cie.2020.106628_b0275 doi: 10.1109/CEC.2008.4631192 – volume: 7 start-page: 1 year: 2017 ident: 10.1016/j.cie.2020.106628_b0435 article-title: A pso-based multi-objective multi-label feature selection method in classification publication-title: Scientific Reports – volume: 116 start-page: 74 year: 2017 ident: 10.1016/j.cie.2020.106628_b0450 article-title: An improved nsga-iii algorithm for feature selection used in intrusion detection publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2016.10.030 – ident: 10.1016/j.cie.2020.106628_b0280 doi: 10.1007/3-540-36127-8_27 – ident: 10.1016/j.cie.2020.106628_b0410 doi: 10.1007/978-1-4615-5725-8_8 – volume: 43 start-page: 5 year: 2010 ident: 10.1016/j.cie.2020.106628_b0135 article-title: Feature subset selection in large dimensionality domains publication-title: Pattern Recognition doi: 10.1016/j.patcog.2009.06.009 – volume: 21 start-page: 4309 year: 2017 ident: 10.1016/j.cie.2020.106628_b0415 article-title: Endocrine-based coevolutionary multi-swarm for multi-objective workflow scheduling in a cloud system publication-title: Soft Computing doi: 10.1007/s00500-016-2063-8 – volume: 13 start-page: 1969 year: 2013 ident: 10.1016/j.cie.2020.106628_b0140 article-title: Self-adaptive differential evolution for feature selection in hyperspectral image data publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2012.11.042 – volume: 56 start-page: 422 year: 2018 ident: 10.1016/j.cie.2020.106628_b0240 article-title: Multiobjective subpixel land-cover mapping publication-title: IEEE Transactions on Geoscience and Remote Sensing doi: 10.1109/TGRS.2017.2748701 – volume: 9 start-page: 15 year: 2019 ident: 10.1016/j.cie.2020.106628_b0015 article-title: Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing publication-title: Human-centric Computing and Information Sciences doi: 10.1186/s13673-019-0174-9 – volume: 36 start-page: 5900 year: 2009 ident: 10.1016/j.cie.2020.106628_b0360 article-title: Evolutionary-based feature selection approaches with new criteria for data mining: A case study of credit approval data publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2008.07.026 – volume: 11 start-page: 712 year: 2007 ident: 10.1016/j.cie.2020.106628_b0440 article-title: Moea/d: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2007.892759 – volume: 37 start-page: 70 year: 2007 ident: 10.1016/j.cie.2020.106628_b0455 article-title: Wrapper–filter feature selection algorithm using a memetic framework publication-title: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) doi: 10.1109/TSMCB.2006.883267 – volume: 31 start-page: 171 year: 2019 ident: 10.1016/j.cie.2020.106628_b0310 article-title: Feature selection via a novel chaotic crow search algorithm publication-title: Neural Computing and Applications doi: 10.1007/s00521-017-2988-6 – volume: 36 start-page: 8616 year: 2009 ident: 10.1016/j.cie.2020.106628_b0345 article-title: A hybrid evolutionary algorithm for attribute selection in data mining publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2008.10.013 – volume: Vol. 184 start-page: 245 year: 1994 ident: 10.1016/j.cie.2020.106628_b0210 article-title: Selection of relevant features in machine learning – volume: 43 start-page: 1656 year: 2013 ident: 10.1016/j.cie.2020.106628_b0400 article-title: Particle swarm optimization for feature selection in classification: A multi-objective approach publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TSMCB.2012.2227469 – volume: 20 start-page: 606 year: 2015 ident: 10.1016/j.cie.2020.106628_b0405 article-title: A survey on evolutionary computation approaches to feature selection publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2015.2504420 – ident: 10.1016/j.cie.2020.106628_b0050 doi: 10.1109/CIDM.2009.4938673 – ident: 10.1016/j.cie.2020.106628_b0390 doi: 10.1007/978-3-319-13563-2_44 – volume: 214 start-page: 108 year: 2009 ident: 10.1016/j.cie.2020.106628_b0190 article-title: A comparative study of artificial bee colony algorithm publication-title: Applied Mathematics and Computation doi: 10.1016/j.amc.2009.03.090 – volume: 83 start-page: 80 year: 2015 ident: 10.1016/j.cie.2020.106628_b0245 article-title: The ant lion optimizer publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2015.01.010 – volume: 13 start-page: 145 year: 2015 ident: 10.1016/j.cie.2020.106628_b0195 article-title: Multi-objective feature subset selection using non-dominated sorting genetic algorithm publication-title: Journal of Applied Research and Technology doi: 10.1016/S1665-6423(15)30013-4 – start-page: 1 year: 2019 ident: 10.1016/j.cie.2020.106628_b0300 article-title: A new binary salp swarm algorithm: development and application for optimization tasks publication-title: Neural Computing and Applications – ident: 10.1016/j.cie.2020.106628_b0085 doi: 10.1007/3-540-45356-3_83 – volume: 11 start-page: 1561 year: 2018 ident: 10.1016/j.cie.2020.106628_b0355 article-title: A novel hybrid interval prediction approach based on modified lower upper bound estimation in combination with multi-objective salp swarm algorithm for short-term load forecasting publication-title: Energies doi: 10.3390/en11061561 – volume: 43 start-page: 1656 year: 2012 ident: 10.1016/j.cie.2020.106628_b0395 article-title: Particle swarm optimization for feature selection in classification: A multi-objective approach publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TSMCB.2012.2227469 – volume: 95 start-page: 51 year: 2016 ident: 10.1016/j.cie.2020.106628_b0265 article-title: The whale optimization algorithm publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2016.01.008 – start-page: 1 year: 2018 ident: 10.1016/j.cie.2020.106628_b0180 article-title: Improved salp swarm algorithm based on particle swarm optimization for feature selection publication-title: Journal of Ambient Intelligence and Humanized Computing – volume: 415 start-page: 319 year: 2017 ident: 10.1016/j.cie.2020.106628_b0125 article-title: A distributed approach to multi-objective evolutionary generation of fuzzy rule-based classifiers from big data publication-title: Information Sciences doi: 10.1016/j.ins.2017.06.039 – volume: 14 start-page: 61 year: 2019 ident: 10.1016/j.cie.2020.106628_b0350 article-title: Diversity assessment of multi-objective evolutionary algorithms: Performance metric and benchmark problems [research frontier] publication-title: IEEE Computational Intelligence Magazine doi: 10.1109/MCI.2019.2919398 |
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