Hybrid slime mould algorithm with adaptive guided differential evolution algorithm for combinatorial and global optimization problems

•SMA-AGDE method is proposed for solving various optimization problems.•The algorithm performance is verified on CEC’17 benchmark.•The method performance is verified on 3 engineering and 2 combinatorial problems.•Efficiency of the proposed method is compared with many metaheuristics. The Slime Mould...

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Vydané v:	Expert systems with applications Ročník 174; s. 114689
Hlavní autori:	Houssein, Essam H., Mahdy, Mohamed A., Blondin, Maude J., Shebl, Doaa, Mohamed, Waleed M.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Elsevier Ltd 15.07.2021 Elsevier BV
Predmet:	Adaptive algorithms Adaptive guided differential evolution algorithm (AGDE) Algorithms Combinatorial analysis Combinatorial optimization problems Compression springs Compression tests Covariance matrix Design engineering Design optimization Engineering design problems Evolutionary algorithms Evolutionary computation Exploration and exploitation Global optimization Global optimization problems Heuristic methods Machine learning Metaheuristics Mutation Optimization Performance evaluation Pressure vessels Roller bearings Search algorithms Slime Slime mould algorithm (SMA) Exploration and exploitation Combinatorial optimization problems Global optimization problems Engineering design problems Metaheuristics Slime mould algorithm (SMA) Adaptive guided differential evolution algorithm (AGDE)
ISSN:	0957-4174, 1873-6793
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Abstract	•SMA-AGDE method is proposed for solving various optimization problems.•The algorithm performance is verified on CEC’17 benchmark.•The method performance is verified on 3 engineering and 2 combinatorial problems.•Efficiency of the proposed method is compared with many metaheuristics. The Slime Mould Algorithm (SMA) is a recent metaheuristic inspired by the oscillation of slime mould. Similar to other original metaheuristic algorithms (MAs), SMA may suffer from drawbacks, such as being trapped in minimum local regions and improper balance between exploitation and exploration phases. To overcome these weaknesses, this paper proposes a hybrid algorithm: SMA combined to Adaptive Guided Differential Evolution Algorithm (AGDE) (SMA-AGDE). The AGDE mutation method is employed to enhance the swarm agents’ local search, increase the population’s diversity, and help avoid premature convergence. The SMA-AGDE’s performance is evaluated on the CEC’17 test suite, three engineering design problems – tension/compression spring, pressure vessel, and rolling element bearing – and two combinatorial optimization problems – bin packing and quadratic assignment. The SMA-AGDE is compared with three categories of optimization methods: (1) The well-studied MAs, i.e., Biogeography-Based Optimizer (BBO), Gravitational Search Algorithm (GSA), and Teaching Learning-Based Optimization (TLBO), (2) Recently developed MAs, i.e., Harris Hawks Optimization (HHO), Manta Ray Foraging optimization (MRFO), and the original SMA, and (3) High-performance MAs, i.e., Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), and AGDE. The overall simulation results reveal that the SMA-AGDE ranked first among the compared algorithms, and so, over different function landscapes. Thus, the proposed SMA-AGDE is a promising optimization tool for global and combinatorial optimization problems and engineering design problems.
AbstractList	The Slime Mould Algorithm (SMA) is a recent metaheuristic inspired by the oscillation of slime mould. Similar to other original metaheuristic algorithms (MAs), SMA may suffer from drawbacks, such as being trapped in minimum local regions and improper balance between exploitation and exploration phases. To overcome these weaknesses, this paper proposes a hybrid algorithm: SMA combined to Adaptive Guided Differential Evolution Algorithm (AGDE) (SMA-AGDE). The AGDE mutation method is employed to enhance the swarm agents' local search, increase the population's diversity, and help avoid premature convergence. The SMA-AGDE's performance is evaluated on the CEC'17 test suite, three engineering design problems – tension/compression spring, pressure vessel, and rolling element bearing – and two combinatorial optimization problems – bin packing and quadratic assignment. The SMA-AGDE is compared with three categories of optimization methods: (1) The well-studied MAs, i.e., Biogeography-Based Optimizer (BBO), Gravitational Search Algorithm (GSA), and Teaching Learning-Based Optimization (TLBO), (2) Recently developed MAs, i.e., Harris Hawks Optimization (HHO), Manta Ray Foraging optimization (MRFO), and the original SMA, and (3) High-performance MAs, i.e., Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), and AGDE. The overall simulation results reveal that the SMA-AGDE ranked first among the compared algorithms, and so, over different function landscapes. Thus, the proposed SMA-AGDE is a promising optimization tool for global and combinatorial optimization problems and engineering design problems. •SMA-AGDE method is proposed for solving various optimization problems.•The algorithm performance is verified on CEC’17 benchmark.•The method performance is verified on 3 engineering and 2 combinatorial problems.•Efficiency of the proposed method is compared with many metaheuristics. The Slime Mould Algorithm (SMA) is a recent metaheuristic inspired by the oscillation of slime mould. Similar to other original metaheuristic algorithms (MAs), SMA may suffer from drawbacks, such as being trapped in minimum local regions and improper balance between exploitation and exploration phases. To overcome these weaknesses, this paper proposes a hybrid algorithm: SMA combined to Adaptive Guided Differential Evolution Algorithm (AGDE) (SMA-AGDE). The AGDE mutation method is employed to enhance the swarm agents’ local search, increase the population’s diversity, and help avoid premature convergence. The SMA-AGDE’s performance is evaluated on the CEC’17 test suite, three engineering design problems – tension/compression spring, pressure vessel, and rolling element bearing – and two combinatorial optimization problems – bin packing and quadratic assignment. The SMA-AGDE is compared with three categories of optimization methods: (1) The well-studied MAs, i.e., Biogeography-Based Optimizer (BBO), Gravitational Search Algorithm (GSA), and Teaching Learning-Based Optimization (TLBO), (2) Recently developed MAs, i.e., Harris Hawks Optimization (HHO), Manta Ray Foraging optimization (MRFO), and the original SMA, and (3) High-performance MAs, i.e., Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), and AGDE. The overall simulation results reveal that the SMA-AGDE ranked first among the compared algorithms, and so, over different function landscapes. Thus, the proposed SMA-AGDE is a promising optimization tool for global and combinatorial optimization problems and engineering design problems.
ArticleNumber	114689
Author	Mohamed, Waleed M. Shebl, Doaa Blondin, Maude J. Houssein, Essam H. Mahdy, Mohamed A.
Author_xml	– sequence: 1 givenname: Essam H. surname: Houssein fullname: Houssein, Essam H. email: essam.halim@mu.edu.eg organization: Faculty of Computers and Information, Minia University, Minia, Egypt – sequence: 2 givenname: Mohamed A. surname: Mahdy fullname: Mahdy, Mohamed A. email: marafa.mahdy@fcis.bsu.edu.eg organization: Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt – sequence: 3 givenname: Maude J. surname: Blondin fullname: Blondin, Maude J. email: maude.josee.blondin@usherbrooke.ca organization: Department of Electrical Engineering, Université de Sherbrooke, Sherbrooke, Canada – sequence: 4 givenname: Doaa surname: Shebl fullname: Shebl, Doaa email: dshebl@fcis.bsu.edu.eg organization: Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt – sequence: 5 givenname: Waleed M. surname: Mohamed fullname: Mohamed, Waleed M. email: waleedmakram@minia.edu.eg organization: Faculty of Computers and Information, Minia University, Minia, Egypt
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Cites_doi	10.1093/biomet/75.4.800 10.1093/biomet/75.2.383 10.1016/j.asoc.2016.02.018 10.1080/03052150310001624403 10.1007/s13042-017-0711-7 10.1016/j.engappai.2021.104155 10.1007/s00500-016-2471-9 10.1088/1742-6596/1617/1/012034 10.1109/TFUZZ.2020.3003506 10.1287/mnsc.9.4.586 10.1016/j.ins.2014.02.123 10.1007/978-3-030-64541-0_1 10.1080/08839514.2018.1508807 10.1016/0377-2217(91)90197-4 10.1109/MCI.2019.2954644 10.1016/j.asoc.2011.02.032 10.1016/j.ins.2018.10.005 10.5772/intechopen.72103 10.1109/MC.2016.292 10.1016/j.future.2019.07.015 10.1016/j.future.2020.03.055 10.1016/j.future.2018.06.008 10.1007/s10664-013-9249-9 10.1007/s00521-019-04611-0 10.1016/j.swevo.2016.12.005 10.1016/j.swevo.2019.04.008 10.1016/j.advengsoft.2017.05.014 10.1016/S0167-8191(05)80147-4 10.1016/j.asoc.2020.106642 10.3390/a10040128 10.1016/j.asoc.2019.01.001 10.1007/s10462-017-9605-z 10.1038/s41598-020-71502-z 10.1016/j.knosys.2020.105675 10.1109/TCYB.2017.2780274 10.3390/w12102692 10.1016/j.cor.2012.04.012 10.1016/j.future.2019.02.028 10.1007/s12293-016-0212-3 10.1016/j.ijleo.2020.165277 10.1016/j.eswa.2018.06.023 10.1016/j.swevo.2018.02.013 10.1109/CEC48606.2020.9185582 10.1016/j.asoc.2018.08.047 10.1162/106365603321828970 10.1016/j.asoc.2017.06.044 10.1016/j.jocs.2017.03.004 10.1016/j.cad.2010.12.015 10.1007/s00521-015-1923-y 10.1016/j.engappai.2020.103731 10.1016/j.asoc.2012.11.026 10.1016/j.ins.2009.03.004 10.1080/01621459.1993.10476358 10.1080/03610928008827904 10.2307/1907742 10.1016/j.eswa.2020.113364 10.5815/ijieeb.2012.02.02 10.1080/18756891.2015.1046324
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Keywords	Exploration and exploitation Combinatorial optimization problems Global optimization problems Engineering design problems Metaheuristics Slime mould algorithm (SMA) Adaptive guided differential evolution algorithm (AGDE)
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References	Boussaïd, Chatterjee, Siarry, Ahmed-Nacer (b0030) 2012; 39 Hashim, Hussain, Houssein, Mabrouk, Al-Atabany (b0120) 2020 Houssein, Mahdy, Eldin, Shebl, Mohamed, Abdel-Aty (b0160) 2020 Zubaidi, Abdulkareem, Hashim, Al-Bugharbee, Ridha, Gharghan, Al-Qaim, Muradov, Kot, Al-Khaddar (b0350) 2020; 12 Izci, Serdar Ekinci, Kayri (b0185) 2020 Neggaz, N., Houssein, E. H. & Hussain, K. (2020). An efficient henry gas solubility optimization for feature selection. Expert Systems with Applications, (p. 113364). Hashim, Houssein, Mabrouk, Al-Atabany, Mirjalili (b0115) 2019; 101 Li, W., Wang, G. -G. & Alavi, A. H. (2020b). Learning-based elephant herding optimization algorithm for solving numerical optimization problems. Knowledge-Based Systems, (p. 105675). Wilcoxon (b0310) 1992 Heidari, Pahlavani (b0135) 2017; 60 Rashedi, Nezamabadi-Pour, Saryazdi (b0260) 2009; 179 Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian journal of statistics (pp. 65–70). Wang (b0290) 2018; 10 Koopmans, T. C. & Beckmann, M. (1957). Assignment problems and the location of economic activities. Econometrica: Journal of the Econometric Society, (pp. 53–76). Blondin (bib351) 2021 Tang, R., Fong, S. & Dey, N. (2018). Metaheuristics and chaos theory. Chaos Theory, (pp. 182–196). Burkard, Karisch, Rendl (b0035) 1991; 55 Abdel-Basset, Manogaran, El-Shahat, Mirjalili (b0010) 2018; 73 Layeb, Chenche (b0215) 2012; 4 Yi, Xing, Wang, Dong, Vasilakos, Alavi, Wang (b0330) 2020; 509 Yang, Deb, Fong, He, Zhao (b0320) 2016; 49 Hashim, Houssein, Hussain, Mabrouk, Al-Atabany (b0110) 2020; 32 Abdel-Basset, M., Chang, V. & Mohamed, R. (2020). Hsma_woa: A hybrid novel slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images. Applied Soft Computing, (p. 106642). Hochberg (b0140) 1988; 75 Hassan, Houssein, Mahdy, Kamel (b0125) 2021; 100 Arcuri, Fraser (b0015) 2013; 18 Scholl, A. & Klein, R. (2007). Bin packing. On line document at http://www. wiwi. uni-jena. de/Entscheidung/binpp/, last visited on October. Wang, Deb, Cui (b0295) 2019; 31 Chakraborty*, I., Kumar, V., Nair, S. B. & Tiwari, R. (2003). Rolling element bearing design through genetic algorithms. Engineering Optimization, 35, 649–659. Hommel (b0150) 1988; 75 Jensi, Jiji (b0195) 2016; 43 Blum, Puchinger, Raidl, Roli (b0025) 2011; 11 Houssein, Saad, Hashim, Shaban, Hassaballah (b0165) 2020; 94 Sun, Jia, Li, Jiang (b0275) 2020; 40 Yi, Deb, Dong, Alavi, Wang (b0325) 2018; 88 Hussain, Salleh, Cheng, Shi (b0175) 2019; 52 Wang, Guo, Gandomi, Hao, Wang (b0300) 2014; 274 Rao, Savsani, Vakharia (b0255) 2011; 43 Mohamed, Mohamed (b0245) 2019; 10 Gao, Wang, Pedrycz (b0085) 2020; 28 Li, S., Chen, H., Wang, M., Heidari, A. A. & Mirjalili, S. (2020a). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems. Finner (b0080) 1993; 88 Hansen, Müller, Koumoutsakos (b0105) 2003; 11 Gao, Z. -M., Zhao, J., Yang, Y. & Tian, X .-J. (2020b). The hybrid grey wolf optimization-slime mould algorithm. In Journal of Physics: Conference Series (p. 012034). IOP Publishing volume 1617. Jain, Singh, Rani (b0190) 2019; 44 Taillard (b0280) 1991; 17 Chandra, Singh (b0045) 2014; 5 Kumar, Raj, Premkumar, Raj (b0205) 2020; 223 Mavrovouniotis, Li, Yang (b0235) 2017; 33 Awad, N., Ali, M., Liang, J., Qu, B. & Suganthan, P. (2016). Problem definitions and evaluation criteria for the cec 2017 special session and competition on single objective real-parameter numerical optimization. Tech. Rep. Sadollah, Bahreininejad, Eskandar, Hamdi (b0265) 2013; 13 Wang, Tan (b0305) 2017; 49 Dokeroglu, Sevinc, Cosar (b0065) 2019; 76 Gary, M. R. & Johnson, D. S. (1979). Computers and intractability: A guide to the theory of np-completeness. Mavrovouniotis, Yang, Van, Li, Polycarpou (b0240) 2020; 15 Ewees, Elaziz, Houssein (b0075) 2018; 112 Heidari, Mirjalili, Faris, Aljarah, Mafarja, Chen (b0130) 2019; 97 Du, Dong, Li (b0070) 2017; 10 Del Ser, Osaba, Molina, Yang, Salcedo-Sanz, Camacho, Das, Suganthan, Coello, Herrera (b0055) 2019; 48 Zendaoui, Layeb (b0335) 2016 Lawler (b0210) 1963; 9 Zendaoui, Layeb (b0340) 2016 Ligeiro (b0230) 2017; 20 Hussain, Salleh, Cheng, Shi (b0170) 2019; 52 Houssein, Hosney, Elhoseny, Oliva, Mohamed, Hassaballah (b0155) 2020; 10 Dhiman, Kumar (b0060) 2017; 114 Chawla, Duhan (b0050) 2018; 32 Iman, Davenport (b0180) 1980; 9 Xiong, Molina, Ortiz, Herrera (b0315) 2015; 8 García-Martínez, Gutiérrez, Molina, Lozano, Herrera (b0095) 2017; 21 Zhao, H., Zhan, Z. -H. & Zhang, J. (2020). Adaptive guidance-based differential evolution with iterative feedback archive strategy for multimodal optimization problems. In 2020 IEEE congress on evolutionary computation (CEC) (pp. 1–8). IEEE. Rashedi (10.1016/j.eswa.2021.114689_b0260) 2009; 179 10.1016/j.eswa.2021.114689_b0005 10.1016/j.eswa.2021.114689_b0285 10.1016/j.eswa.2021.114689_b0200 Wilcoxon (10.1016/j.eswa.2021.114689_b0310) 1992 Hashim (10.1016/j.eswa.2021.114689_b0115) 2019; 101 Boussaïd (10.1016/j.eswa.2021.114689_b0030) 2012; 39 Kumar (10.1016/j.eswa.2021.114689_b0205) 2020; 223 Rao (10.1016/j.eswa.2021.114689_b0255) 2011; 43 Sadollah (10.1016/j.eswa.2021.114689_b0265) 2013; 13 Hansen (10.1016/j.eswa.2021.114689_b0105) 2003; 11 Houssein (10.1016/j.eswa.2021.114689_b0160) 2020 Taillard (10.1016/j.eswa.2021.114689_b0280) 1991; 17 Heidari (10.1016/j.eswa.2021.114689_b0130) 2019; 97 10.1016/j.eswa.2021.114689_b0250 10.1016/j.eswa.2021.114689_b0090 Izci (10.1016/j.eswa.2021.114689_b0185) 2020 Heidari (10.1016/j.eswa.2021.114689_b0135) 2017; 60 Del Ser (10.1016/j.eswa.2021.114689_b0055) 2019; 48 Yi (10.1016/j.eswa.2021.114689_b0325) 2018; 88 Hussain (10.1016/j.eswa.2021.114689_b0175) 2019; 52 Yi (10.1016/j.eswa.2021.114689_b0330) 2020; 509 Xiong (10.1016/j.eswa.2021.114689_b0315) 2015; 8 Hashim (10.1016/j.eswa.2021.114689_b0120) 2020 Hommel (10.1016/j.eswa.2021.114689_b0150) 1988; 75 10.1016/j.eswa.2021.114689_b0020 Yang (10.1016/j.eswa.2021.114689_b0320) 2016; 49 García-Martínez (10.1016/j.eswa.2021.114689_b0095) 2017; 21 Ewees (10.1016/j.eswa.2021.114689_b0075) 2018; 112 10.1016/j.eswa.2021.114689_b0345 Sun (10.1016/j.eswa.2021.114689_b0275) 2020; 40 Hashim (10.1016/j.eswa.2021.114689_b0110) 2020; 32 Houssein (10.1016/j.eswa.2021.114689_b0155) 2020; 10 10.1016/j.eswa.2021.114689_b0225 Mohamed (10.1016/j.eswa.2021.114689_b0245) 2019; 10 10.1016/j.eswa.2021.114689_b0220 10.1016/j.eswa.2021.114689_b0145 10.1016/j.eswa.2021.114689_b0100 Lawler (10.1016/j.eswa.2021.114689_b0210) 1963; 9 Wang (10.1016/j.eswa.2021.114689_b0295) 2019; 31 Zubaidi (10.1016/j.eswa.2021.114689_b0350) 2020; 12 Mavrovouniotis (10.1016/j.eswa.2021.114689_b0235) 2017; 33 Wang (10.1016/j.eswa.2021.114689_b0290) 2018; 10 Hussain (10.1016/j.eswa.2021.114689_b0170) 2019; 52 Chandra (10.1016/j.eswa.2021.114689_b0045) 2014; 5 Mavrovouniotis (10.1016/j.eswa.2021.114689_b0240) 2020; 15 10.1016/j.eswa.2021.114689_b0270 Jain (10.1016/j.eswa.2021.114689_b0190) 2019; 44 Wang (10.1016/j.eswa.2021.114689_b0305) 2017; 49 Arcuri (10.1016/j.eswa.2021.114689_b0015) 2013; 18 Chawla (10.1016/j.eswa.2021.114689_b0050) 2018; 32 Jensi (10.1016/j.eswa.2021.114689_b0195) 2016; 43 Dhiman (10.1016/j.eswa.2021.114689_b0060) 2017; 114 Dokeroglu (10.1016/j.eswa.2021.114689_b0065) 2019; 76 Zendaoui (10.1016/j.eswa.2021.114689_b0340) 2016 Houssein (10.1016/j.eswa.2021.114689_b0165) 2020; 94 Wang (10.1016/j.eswa.2021.114689_b0300) 2014; 274 Finner (10.1016/j.eswa.2021.114689_b0080) 1993; 88 Zendaoui (10.1016/j.eswa.2021.114689_b0335) 2016 Abdel-Basset (10.1016/j.eswa.2021.114689_b0010) 2018; 73 Hochberg (10.1016/j.eswa.2021.114689_b0140) 1988; 75 Blondin (10.1016/j.eswa.2021.114689_bib351) 2021 Ligeiro (10.1016/j.eswa.2021.114689_b0230) 2017; 20 Du (10.1016/j.eswa.2021.114689_b0070) 2017; 10 Layeb (10.1016/j.eswa.2021.114689_b0215) 2012; 4 10.1016/j.eswa.2021.114689_b0040 Iman (10.1016/j.eswa.2021.114689_b0180) 1980; 9 Blum (10.1016/j.eswa.2021.114689_b0025) 2011; 11 Burkard (10.1016/j.eswa.2021.114689_b0035) 1991; 55 Hassan (10.1016/j.eswa.2021.114689_b0125) 2021; 100 Gao (10.1016/j.eswa.2021.114689_b0085) 2020; 28
References_xml	– volume: 28 start-page: 3265 year: 2020 end-page: 3275 ident: b0085 article-title: Solving fuzzy job-shop scheduling problem using de algorithm improved by a selection mechanism publication-title: IEEE Transactions on Fuzzy Systems – year: 2020 ident: b0160 article-title: Optimizing quantum cloning circuit parameters based on adaptive guided differential evolution algorithm publication-title: Journal of Advanced Research – volume: 179 start-page: 2232 year: 2009 end-page: 2248 ident: b0260 article-title: Gsa: A gravitational search algorithm publication-title: Information Sciences – volume: 75 start-page: 383 year: 1988 end-page: 386 ident: b0150 article-title: A stagewise rejective multiple test procedure based on a modified bonferroni test publication-title: Biometrika – volume: 94 year: 2020 ident: b0165 article-title: Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems publication-title: Engineering Applications of Artificial Intelligence – volume: 31 start-page: 1995 year: 2019 end-page: 2014 ident: b0295 article-title: Monarch butterfly optimization publication-title: Neural Computing and Applications – start-page: 107 year: 2016 end-page: 120 ident: b0335 article-title: Adaptive cuckoo search algorithm for the bin packing problem publication-title: Modelling and implementation of complex systems – volume: 12 start-page: 2692 year: 2020 ident: b0350 article-title: Hybridised artificial neural network model with slime mould algorithm: A novel methodology for prediction of urban stochastic water demand publication-title: Water – volume: 100 year: 2021 ident: b0125 article-title: An improved manta ray foraging optimizer for cost-effective emission dispatch problems publication-title: Engineering Applications of Artificial Intelligence – reference: Zhao, H., Zhan, Z. -H. & Zhang, J. (2020). Adaptive guidance-based differential evolution with iterative feedback archive strategy for multimodal optimization problems. In 2020 IEEE congress on evolutionary computation (CEC) (pp. 1–8). IEEE. – volume: 49 start-page: 542 year: 2017 end-page: 555 ident: b0305 article-title: Improving metaheuristic algorithms with information feedback models publication-title: IEEE Transactions on Cybernetics – reference: Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian journal of statistics (pp. 65–70). – volume: 18 start-page: 594 year: 2013 end-page: 623 ident: b0015 article-title: Parameter tuning or default values? An empirical investigation in search-based software engineering publication-title: Empirical Software Engineering – volume: 76 start-page: 595 year: 2019 end-page: 606 ident: b0065 article-title: Artificial bee colony optimization for the quadratic assignment problem publication-title: Applied Soft Computing – reference: Abdel-Basset, M., Chang, V. & Mohamed, R. (2020). Hsma_woa: A hybrid novel slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images. Applied Soft Computing, (p. 106642). – volume: 39 start-page: 3293 year: 2012 end-page: 3304 ident: b0030 article-title: Biogeography-based optimization for constrained optimization problems publication-title: Computers & Operations Research – volume: 101 start-page: 646 year: 2019 end-page: 667 ident: b0115 article-title: Henry gas solubility optimization: A novel physics-based algorithm publication-title: Future Generation Computer Systems – volume: 60 start-page: 115 year: 2017 end-page: 134 ident: b0135 article-title: An efficient modified grey wolf optimizer with lévy flight for optimization tasks publication-title: Applied Soft Computing – volume: 10 start-page: 253 year: 2019 end-page: 277 ident: b0245 article-title: Adaptive guided differential evolution algorithm with novel mutation for numerical optimization publication-title: International Journal of Machine Learning and Cybernetics – start-page: 107 year: 2016 end-page: 120 ident: b0340 article-title: Adaptive cuckoo search algorithm for the bin packing problem publication-title: Modelling and implementation of complex systems – volume: 88 start-page: 571 year: 2018 end-page: 585 ident: b0325 article-title: An improved nsga-iii algorithm with adaptive mutation operator for big data optimization problems publication-title: Future Generation Computer Systems – volume: 48 start-page: 220 year: 2019 end-page: 250 ident: b0055 article-title: Bio-inspired computation: Where we stand and what’s next publication-title: Swarm and Evolutionary Computation – volume: 43 start-page: 303 year: 2011 end-page: 315 ident: b0255 article-title: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems publication-title: Computer-Aided Design – reference: Li, S., Chen, H., Wang, M., Heidari, A. A. & Mirjalili, S. (2020a). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems. – reference: Li, W., Wang, G. -G. & Alavi, A. H. (2020b). Learning-based elephant herding optimization algorithm for solving numerical optimization problems. Knowledge-Based Systems, (p. 105675). – reference: Scholl, A. & Klein, R. (2007). Bin packing. On line document at http://www. wiwi. uni-jena. de/Entscheidung/binpp/, last visited on October. – volume: 43 start-page: 248 year: 2016 end-page: 261 ident: b0195 article-title: An enhanced particle swarm optimization with levy flight for global optimization publication-title: Applied Soft Computing – volume: 15 start-page: 52 year: 2020 end-page: 63 ident: b0240 article-title: Ant colony optimization algorithms for dynamic optimization: A case study of the dynamic travelling salesperson problem [research frontier] publication-title: IEEE Computational Intelligence Magazine – volume: – start-page: 1 year: 2020 end-page: 21 ident: b0120 article-title: Archimedes optimization algorithm: A new metaheuristic algorithm for solving optimization problems publication-title: Applied Intelligence – start-page: 196 year: 1992 end-page: 202 ident: b0310 article-title: Individual comparisons by ranking methods publication-title: Breakthroughs in statistics – volume: 88 start-page: 920 year: 1993 end-page: 923 ident: b0080 article-title: On a monotonicity problem in step-down multiple test procedures publication-title: Journal of the American Statistical Association – reference: Neggaz, N., Houssein, E. H. & Hussain, K. (2020). An efficient henry gas solubility optimization for feature selection. Expert Systems with Applications, (p. 113364). – volume: 55 start-page: 115 year: 1991 end-page: 119 ident: b0035 article-title: Qaplib-a quadratic assignment problem library publication-title: European Journal of Operational Research – reference: Gao, Z. -M., Zhao, J., Yang, Y. & Tian, X .-J. (2020b). The hybrid grey wolf optimization-slime mould algorithm. In Journal of Physics: Conference Series (p. 012034). IOP Publishing volume 1617. – volume: 44 start-page: 148 year: 2019 end-page: 175 ident: b0190 article-title: A novel nature-inspired algorithm for optimization: Squirrel search algorithm publication-title: Swarm and Evolutionary Computation – volume: 8 start-page: 606 year: 2015 end-page: 636 ident: b0315 article-title: A walk into metaheuristics for engineering optimization: Principles, methods and recent trends publication-title: International Journal of Computational Intelligence Systems – volume: 73 start-page: 530 year: 2018 end-page: 546 ident: b0010 article-title: Integrating the whale algorithm with tabu search for quadratic assignment problem: A new approach for locating hospital departments publication-title: Applied Soft Computing – volume: 114 start-page: 48 year: 2017 end-page: 70 ident: b0060 article-title: Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications publication-title: Advances in Engineering Software – volume: 21 start-page: 5573 year: 2017 end-page: 5583 ident: b0095 article-title: Since cec 2005 competition on real-parameter optimisation: A decade of research, progress and comparative analysis’s weakness publication-title: Soft Computing – reference: Chakraborty*, I., Kumar, V., Nair, S. B. & Tiwari, R. (2003). Rolling element bearing design through genetic algorithms. Engineering Optimization, 35, 649–659. – volume: 75 start-page: 800 year: 1988 end-page: 802 ident: b0140 article-title: A sharper bonferroni procedure for multiple tests of significance publication-title: Biometrika – reference: Awad, N., Ali, M., Liang, J., Qu, B. & Suganthan, P. (2016). Problem definitions and evaluation criteria for the cec 2017 special session and competition on single objective real-parameter numerical optimization. Tech. Rep. – volume: 40 start-page: 1 year: 2020 end-page: 13 ident: b0275 article-title: Hybrid improved slime mould algorithm with adaptive publication-title: Journal of Intelligent & Fuzzy Systems – volume: 17 start-page: 443 year: 1991 end-page: 455 ident: b0280 article-title: Robust taboo search for the quadratic assignment problem publication-title: Parallel Computing – start-page: 1 year: 2020 end-page: 6 ident: b0185 article-title: Improved manta ray foraging optimization using opposition-based learning for optimization problems publication-title: International congress on human-computer interaction, optimization and robotic applications (HORA) – volume: 52 start-page: 2191 year: 2019 end-page: 2233 ident: b0175 article-title: Metaheuristic research: A comprehensive survey publication-title: Artificial Intelligence Review – year: 2021 ident: bib351 article-title: Optimization Algorithms in Control Systems. publication-title: Controller Tuning Optimization Methods for Multi-Constraints and Nonlinear Systems – volume: 13 start-page: 2592 year: 2013 end-page: 2612 ident: b0265 article-title: Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems publication-title: Applied Soft Computing – reference: Gary, M. R. & Johnson, D. S. (1979). Computers and intractability: A guide to the theory of np-completeness. – volume: 9 start-page: 586 year: 1963 end-page: 599 ident: b0210 article-title: The quadratic assignment problem publication-title: Management Science – volume: 52 start-page: 2191 year: 2019 end-page: 2233 ident: b0170 article-title: Metaheuristic research: A comprehensive survey publication-title: Artificial Intelligence Review – reference: Koopmans, T. C. & Beckmann, M. (1957). Assignment problems and the location of economic activities. Econometrica: Journal of the Econometric Society, (pp. 53–76). – volume: 32 start-page: 10759 year: 2020 end-page: 10771 ident: b0110 article-title: A modified henry gas solubility optimization for solving motif discovery problem publication-title: Neural Computing and Applications – volume: 20 start-page: 1 year: 2017 end-page: 7 ident: b0230 article-title: Linked markovian quantum tunnels: An approximation technique for solving the bin packing problem publication-title: Journal of Computational Science – volume: 97 start-page: 849 year: 2019 end-page: 872 ident: b0130 article-title: Harris hawks optimization: Algorithm and applications publication-title: Future Generation Computer Systems – volume: 10 start-page: 151 year: 2018 end-page: 164 ident: b0290 article-title: Moth search algorithm: A bio-inspired metaheuristic algorithm for global optimization problems publication-title: Memetic Computing – volume: 223 year: 2020 ident: b0205 article-title: A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters publication-title: Optik – volume: 4 start-page: 8 year: 2012 ident: b0215 article-title: A novel grasp algorithm for solving the bin packing problem publication-title: International Journal of Information Engineering and Electronic Business – volume: 509 start-page: 470 year: 2020 end-page: 487 ident: b0330 article-title: Behavior of crossover operators in nsga-iii for large-scale optimization problems publication-title: Information Sciences – volume: 49 start-page: 52 year: 2016 end-page: 59 ident: b0320 article-title: From swarm intelligence to metaheuristics: Nature-inspired optimization algorithms publication-title: Computer – volume: 11 start-page: 1 year: 2003 end-page: 18 ident: b0105 article-title: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es) publication-title: Evolutionary Computation – volume: 32 start-page: 802 year: 2018 end-page: 821 ident: b0050 article-title: Levy flights in metaheuristics optimization algorithms – a review publication-title: Applied Artificial Intelligence – volume: 10 start-page: 128 year: 2017 ident: b0070 article-title: Truss structure optimization with subset simulation and augmented lagrangian multiplier method publication-title: Algorithms – volume: 10 start-page: 1 year: 2020 end-page: 22 ident: b0155 article-title: Hybrid harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics publication-title: Scientific RepoRtS – volume: 11 start-page: 4135 year: 2011 end-page: 4151 ident: b0025 article-title: Hybrid metaheuristics in combinatorial optimization: A survey publication-title: Applied Soft Computing – volume: 274 start-page: 17 year: 2014 end-page: 34 ident: b0300 article-title: Chaotic krill herd algorithm publication-title: Information Sciences – volume: 5 start-page: 5368 year: 2014 end-page: 5373 ident: b0045 article-title: Firefly algorithm to solve two dimensional bin packing problem publication-title: International Journal of Computer Science and Information Technologies – reference: Tang, R., Fong, S. & Dey, N. (2018). Metaheuristics and chaos theory. Chaos Theory, (pp. 182–196). – volume: 112 start-page: 156 year: 2018 end-page: 172 ident: b0075 article-title: Improved grasshopper optimization algorithm using opposition-based learning publication-title: Expert Systems with Applications – volume: 9 start-page: 571 year: 1980 end-page: 595 ident: b0180 article-title: Approximations of the critical region of the fbietkan statistic publication-title: Communications in Statistics-Theory and Methods – volume: 33 start-page: 1 year: 2017 end-page: 17 ident: b0235 article-title: A survey of swarm intelligence for dynamic optimization: Algorithms and applications publication-title: Swarm and Evolutionary Computation – volume: 75 start-page: 800 year: 1988 ident: 10.1016/j.eswa.2021.114689_b0140 article-title: A sharper bonferroni procedure for multiple tests of significance publication-title: Biometrika doi: 10.1093/biomet/75.4.800 – volume: 75 start-page: 383 year: 1988 ident: 10.1016/j.eswa.2021.114689_b0150 article-title: A stagewise rejective multiple test procedure based on a modified bonferroni test publication-title: Biometrika doi: 10.1093/biomet/75.2.383 – volume: 43 start-page: 248 year: 2016 ident: 10.1016/j.eswa.2021.114689_b0195 article-title: An enhanced particle swarm optimization with levy flight for global optimization publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2016.02.018 – ident: 10.1016/j.eswa.2021.114689_b0040 doi: 10.1080/03052150310001624403 – volume: 10 start-page: 253 year: 2019 ident: 10.1016/j.eswa.2021.114689_b0245 article-title: Adaptive guided differential evolution algorithm with novel mutation for numerical optimization publication-title: International Journal of Machine Learning and Cybernetics doi: 10.1007/s13042-017-0711-7 – volume: 100 year: 2021 ident: 10.1016/j.eswa.2021.114689_b0125 article-title: An improved manta ray foraging optimizer for cost-effective emission dispatch problems publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2021.104155 – volume: 21 start-page: 5573 year: 2017 ident: 10.1016/j.eswa.2021.114689_b0095 article-title: Since cec 2005 competition on real-parameter optimisation: A decade of research, progress and comparative analysis’s weakness publication-title: Soft Computing doi: 10.1007/s00500-016-2471-9 – ident: 10.1016/j.eswa.2021.114689_b0090 doi: 10.1088/1742-6596/1617/1/012034 – volume: 28 start-page: 3265 year: 2020 ident: 10.1016/j.eswa.2021.114689_b0085 article-title: Solving fuzzy job-shop scheduling problem using de algorithm improved by a selection mechanism publication-title: IEEE Transactions on Fuzzy Systems doi: 10.1109/TFUZZ.2020.3003506 – volume: 9 start-page: 586 year: 1963 ident: 10.1016/j.eswa.2021.114689_b0210 article-title: The quadratic assignment problem publication-title: Management Science doi: 10.1287/mnsc.9.4.586 – volume: 274 start-page: 17 year: 2014 ident: 10.1016/j.eswa.2021.114689_b0300 article-title: Chaotic krill herd algorithm publication-title: Information Sciences doi: 10.1016/j.ins.2014.02.123 – year: 2021 ident: 10.1016/j.eswa.2021.114689_bib351 article-title: Optimization Algorithms in Control Systems. publication-title: Controller Tuning Optimization Methods for Multi-Constraints and Nonlinear Systems doi: 10.1007/978-3-030-64541-0_1 – volume: 32 start-page: 802 year: 2018 ident: 10.1016/j.eswa.2021.114689_b0050 article-title: Levy flights in metaheuristics optimization algorithms – a review publication-title: Applied Artificial Intelligence doi: 10.1080/08839514.2018.1508807 – volume: 55 start-page: 115 year: 1991 ident: 10.1016/j.eswa.2021.114689_b0035 article-title: Qaplib-a quadratic assignment problem library publication-title: European Journal of Operational Research doi: 10.1016/0377-2217(91)90197-4 – volume: 15 start-page: 52 year: 2020 ident: 10.1016/j.eswa.2021.114689_b0240 article-title: Ant colony optimization algorithms for dynamic optimization: A case study of the dynamic travelling salesperson problem [research frontier] publication-title: IEEE Computational Intelligence Magazine doi: 10.1109/MCI.2019.2954644 – volume: 11 start-page: 4135 year: 2011 ident: 10.1016/j.eswa.2021.114689_b0025 article-title: Hybrid metaheuristics in combinatorial optimization: A survey publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2011.02.032 – volume: 509 start-page: 470 year: 2020 ident: 10.1016/j.eswa.2021.114689_b0330 article-title: Behavior of crossover operators in nsga-iii for large-scale optimization problems publication-title: Information Sciences doi: 10.1016/j.ins.2018.10.005 – volume: 40 start-page: 1 year: 2020 ident: 10.1016/j.eswa.2021.114689_b0275 article-title: Hybrid improved slime mould algorithm with adaptive β hill climbing for numerical optimization publication-title: Journal of Intelligent & Fuzzy Systems – ident: 10.1016/j.eswa.2021.114689_b0285 doi: 10.5772/intechopen.72103 – volume: 49 start-page: 52 year: 2016 ident: 10.1016/j.eswa.2021.114689_b0320 article-title: From swarm intelligence to metaheuristics: Nature-inspired optimization algorithms publication-title: Computer doi: 10.1109/MC.2016.292 – volume: 101 start-page: 646 year: 2019 ident: 10.1016/j.eswa.2021.114689_b0115 article-title: Henry gas solubility optimization: A novel physics-based algorithm publication-title: Future Generation Computer Systems doi: 10.1016/j.future.2019.07.015 – ident: 10.1016/j.eswa.2021.114689_b0220 doi: 10.1016/j.future.2020.03.055 – volume: 88 start-page: 571 year: 2018 ident: 10.1016/j.eswa.2021.114689_b0325 article-title: An improved nsga-iii algorithm with adaptive mutation operator for big data optimization problems publication-title: Future Generation Computer Systems doi: 10.1016/j.future.2018.06.008 – volume: 18 start-page: 594 year: 2013 ident: 10.1016/j.eswa.2021.114689_b0015 article-title: Parameter tuning or default values? An empirical investigation in search-based software engineering publication-title: Empirical Software Engineering doi: 10.1007/s10664-013-9249-9 – volume: 32 start-page: 10759 year: 2020 ident: 10.1016/j.eswa.2021.114689_b0110 article-title: A modified henry gas solubility optimization for solving motif discovery problem publication-title: Neural Computing and Applications doi: 10.1007/s00521-019-04611-0 – volume: 33 start-page: 1 year: 2017 ident: 10.1016/j.eswa.2021.114689_b0235 article-title: A survey of swarm intelligence for dynamic optimization: Algorithms and applications publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2016.12.005 – volume: 48 start-page: 220 year: 2019 ident: 10.1016/j.eswa.2021.114689_b0055 article-title: Bio-inspired computation: Where we stand and what’s next publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2019.04.008 – volume: 114 start-page: 48 year: 2017 ident: 10.1016/j.eswa.2021.114689_b0060 article-title: Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2017.05.014 – volume: 17 start-page: 443 year: 1991 ident: 10.1016/j.eswa.2021.114689_b0280 article-title: Robust taboo search for the quadratic assignment problem publication-title: Parallel Computing doi: 10.1016/S0167-8191(05)80147-4 – ident: 10.1016/j.eswa.2021.114689_b0005 doi: 10.1016/j.asoc.2020.106642 – start-page: 107 year: 2016 ident: 10.1016/j.eswa.2021.114689_b0335 article-title: Adaptive cuckoo search algorithm for the bin packing problem – start-page: 107 year: 2016 ident: 10.1016/j.eswa.2021.114689_b0340 article-title: Adaptive cuckoo search algorithm for the bin packing problem – volume: 10 start-page: 128 year: 2017 ident: 10.1016/j.eswa.2021.114689_b0070 article-title: Truss structure optimization with subset simulation and augmented lagrangian multiplier method publication-title: Algorithms doi: 10.3390/a10040128 – ident: 10.1016/j.eswa.2021.114689_b0100 – ident: 10.1016/j.eswa.2021.114689_b0145 – volume: 76 start-page: 595 year: 2019 ident: 10.1016/j.eswa.2021.114689_b0065 article-title: Artificial bee colony optimization for the quadratic assignment problem publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2019.01.001 – volume: 52 start-page: 2191 year: 2019 ident: 10.1016/j.eswa.2021.114689_b0170 article-title: Metaheuristic research: A comprehensive survey publication-title: Artificial Intelligence Review doi: 10.1007/s10462-017-9605-z – volume: 52 start-page: 2191 year: 2019 ident: 10.1016/j.eswa.2021.114689_b0175 article-title: Metaheuristic research: A comprehensive survey publication-title: Artificial Intelligence Review doi: 10.1007/s10462-017-9605-z – volume: 10 start-page: 1 year: 2020 ident: 10.1016/j.eswa.2021.114689_b0155 article-title: Hybrid harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics publication-title: Scientific RepoRtS doi: 10.1038/s41598-020-71502-z – year: 2020 ident: 10.1016/j.eswa.2021.114689_b0160 article-title: Optimizing quantum cloning circuit parameters based on adaptive guided differential evolution algorithm publication-title: Journal of Advanced Research – volume: 5 start-page: 5368 year: 2014 ident: 10.1016/j.eswa.2021.114689_b0045 article-title: Firefly algorithm to solve two dimensional bin packing problem publication-title: International Journal of Computer Science and Information Technologies – ident: 10.1016/j.eswa.2021.114689_b0270 – ident: 10.1016/j.eswa.2021.114689_b0225 doi: 10.1016/j.knosys.2020.105675 – volume: 49 start-page: 542 year: 2017 ident: 10.1016/j.eswa.2021.114689_b0305 article-title: Improving metaheuristic algorithms with information feedback models publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2017.2780274 – volume: 12 start-page: 2692 year: 2020 ident: 10.1016/j.eswa.2021.114689_b0350 article-title: Hybridised artificial neural network model with slime mould algorithm: A novel methodology for prediction of urban stochastic water demand publication-title: Water doi: 10.3390/w12102692 – volume: 39 start-page: 3293 year: 2012 ident: 10.1016/j.eswa.2021.114689_b0030 article-title: Biogeography-based optimization for constrained optimization problems publication-title: Computers & Operations Research doi: 10.1016/j.cor.2012.04.012 – volume: 97 start-page: 849 year: 2019 ident: 10.1016/j.eswa.2021.114689_b0130 article-title: Harris hawks optimization: Algorithm and applications publication-title: Future Generation Computer Systems doi: 10.1016/j.future.2019.02.028 – volume: 10 start-page: 151 year: 2018 ident: 10.1016/j.eswa.2021.114689_b0290 article-title: Moth search algorithm: A bio-inspired metaheuristic algorithm for global optimization problems publication-title: Memetic Computing doi: 10.1007/s12293-016-0212-3 – start-page: 1 year: 2020 ident: 10.1016/j.eswa.2021.114689_b0185 article-title: Improved manta ray foraging optimization using opposition-based learning for optimization problems – volume: 223 year: 2020 ident: 10.1016/j.eswa.2021.114689_b0205 article-title: A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters publication-title: Optik doi: 10.1016/j.ijleo.2020.165277 – volume: 112 start-page: 156 year: 2018 ident: 10.1016/j.eswa.2021.114689_b0075 article-title: Improved grasshopper optimization algorithm using opposition-based learning publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2018.06.023 – volume: 44 start-page: 148 year: 2019 ident: 10.1016/j.eswa.2021.114689_b0190 article-title: A novel nature-inspired algorithm for optimization: Squirrel search algorithm publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2018.02.013 – ident: 10.1016/j.eswa.2021.114689_b0345 doi: 10.1109/CEC48606.2020.9185582 – volume: 73 start-page: 530 year: 2018 ident: 10.1016/j.eswa.2021.114689_b0010 article-title: Integrating the whale algorithm with tabu search for quadratic assignment problem: A new approach for locating hospital departments publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2018.08.047 – volume: 11 start-page: 1 year: 2003 ident: 10.1016/j.eswa.2021.114689_b0105 article-title: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es) publication-title: Evolutionary Computation doi: 10.1162/106365603321828970 – volume: 60 start-page: 115 year: 2017 ident: 10.1016/j.eswa.2021.114689_b0135 article-title: An efficient modified grey wolf optimizer with lévy flight for optimization tasks publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2017.06.044 – volume: 20 start-page: 1 year: 2017 ident: 10.1016/j.eswa.2021.114689_b0230 article-title: Linked markovian quantum tunnels: An approximation technique for solving the bin packing problem publication-title: Journal of Computational Science doi: 10.1016/j.jocs.2017.03.004 – volume: 43 start-page: 303 year: 2011 ident: 10.1016/j.eswa.2021.114689_b0255 article-title: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems publication-title: Computer-Aided Design doi: 10.1016/j.cad.2010.12.015 – ident: 10.1016/j.eswa.2021.114689_b0020 – volume: 31 start-page: 1995 year: 2019 ident: 10.1016/j.eswa.2021.114689_b0295 article-title: Monarch butterfly optimization publication-title: Neural Computing and Applications doi: 10.1007/s00521-015-1923-y – volume: – start-page: 1 year: 2020 ident: 10.1016/j.eswa.2021.114689_b0120 article-title: Archimedes optimization algorithm: A new metaheuristic algorithm for solving optimization problems publication-title: Applied Intelligence – volume: 94 year: 2020 ident: 10.1016/j.eswa.2021.114689_b0165 article-title: Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2020.103731 – volume: 13 start-page: 2592 year: 2013 ident: 10.1016/j.eswa.2021.114689_b0265 article-title: Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2012.11.026 – volume: 179 start-page: 2232 year: 2009 ident: 10.1016/j.eswa.2021.114689_b0260 article-title: Gsa: A gravitational search algorithm publication-title: Information Sciences doi: 10.1016/j.ins.2009.03.004 – start-page: 196 year: 1992 ident: 10.1016/j.eswa.2021.114689_b0310 article-title: Individual comparisons by ranking methods – volume: 88 start-page: 920 year: 1993 ident: 10.1016/j.eswa.2021.114689_b0080 article-title: On a monotonicity problem in step-down multiple test procedures publication-title: Journal of the American Statistical Association doi: 10.1080/01621459.1993.10476358 – volume: 9 start-page: 571 year: 1980 ident: 10.1016/j.eswa.2021.114689_b0180 article-title: Approximations of the critical region of the fbietkan statistic publication-title: Communications in Statistics-Theory and Methods doi: 10.1080/03610928008827904 – ident: 10.1016/j.eswa.2021.114689_b0200 doi: 10.2307/1907742 – ident: 10.1016/j.eswa.2021.114689_b0250 doi: 10.1016/j.eswa.2020.113364 – volume: 4 start-page: 8 year: 2012 ident: 10.1016/j.eswa.2021.114689_b0215 article-title: A novel grasp algorithm for solving the bin packing problem publication-title: International Journal of Information Engineering and Electronic Business doi: 10.5815/ijieeb.2012.02.02 – volume: 8 start-page: 606 year: 2015 ident: 10.1016/j.eswa.2021.114689_b0315 article-title: A walk into metaheuristics for engineering optimization: Principles, methods and recent trends publication-title: International Journal of Computational Intelligence Systems doi: 10.1080/18756891.2015.1046324
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Snippet	•SMA-AGDE method is proposed for solving various optimization problems.•The algorithm performance is verified on CEC’17 benchmark.•The method performance is... The Slime Mould Algorithm (SMA) is a recent metaheuristic inspired by the oscillation of slime mould. Similar to other original metaheuristic algorithms (MAs),...
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SubjectTerms	Adaptive algorithms Adaptive guided differential evolution algorithm (AGDE) Algorithms Combinatorial analysis Combinatorial optimization problems Compression springs Compression tests Covariance matrix Design engineering Design optimization Engineering design problems Evolutionary algorithms Evolutionary computation Exploration and exploitation Global optimization Global optimization problems Heuristic methods Machine learning Metaheuristics Mutation Optimization Performance evaluation Pressure vessels Roller bearings Search algorithms Slime Slime mould algorithm (SMA)
Title	Hybrid slime mould algorithm with adaptive guided differential evolution algorithm for combinatorial and global optimization problems
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