Heap-based optimizer inspired by corporate rank hierarchy for global optimization

•Heap-based optimizer (HBO) inspired by corporate rank hierarchy (CRH) is proposed.•HBO utilizes heap to map the hierarchy and model equations for 3 CRH activities.•A parameter (γ) to escape local optima without lacking exploitation is introduced.•Exploration and exploitation are balanced through se...

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Vydané v:	Expert systems with applications Ročník 161; s. 113702
Hlavní autori:	Askari, Qamar, Saeed, Mehreen, Younas, Irfan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Elsevier Ltd 15.12.2020 Elsevier BV
Predmet:	Algorithms Benchmarks Corporate hierarchy based optimization Corporate structure Data structures Global optimization Global optimization algorithm Mechanical engineering Nature-inspired meta-heuristic Optimization Rank tests Social optimization algorithm Source code Nature-inspired meta-heuristic Social optimization algorithm Corporate hierarchy based optimization Global optimization algorithm
ISSN:	0957-4174, 1873-6793
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Abstract	•Heap-based optimizer (HBO) inspired by corporate rank hierarchy (CRH) is proposed.•HBO utilizes heap to map the hierarchy and model equations for 3 CRH activities.•A parameter (γ) to escape local optima without lacking exploitation is introduced.•Exploration and exploitation are balanced through self-adaptive parameters.•Performance is evaluated on 97 benchmarks and 3 mechanical engineering problems. In an organization, a group of people working for a common goal may not achieve their goal unless they organize themselves in a hierarchy called Corporate Rank Hierarchy (CRH). This principle motivates us to map the concept of CRH to propose a new algorithm for optimization that logically arranges the search agents in a hierarchy based on their fitness. The proposed algorithm is named as heap-based optimizer (HBO) because it utilizes the heap data structure to map the concept of CRH. The mathematical model of HBO is built on three pillars: the interaction between the subordinates and their immediate boss, the interaction between the colleagues, and self-contribution of the employees. The proposed algorithm is benchmarked with 97 diverse test functions including 29 CEC-BC-2017 functions with very challenging landscapes against 7 highly-cited optimization algorithms including the winner of CEC-BC-2017 (EBO-CMAR). In the first two experiments, the exploitative and explorative behavior of HBO is evaluated by using 24 unimodal and 44 multimodal functions, respectively. It is shown through experiments and Friedman mean rank test that HBO outperforms and secures 1st rank. In the third experiment, we use 29 CEC-BC-2017 benchmark functions. According to Friedman mean rank test HBO attains 2nd position after EBO-CMAR; however, the difference in ranks of HBO and EBO-CMAR is shown to be statistically insignificant by using Bonferroni method based multiple comparison test. Moreover, it is shown through the Friedman test that the overall rank of HBO is 1st for all 97 benchmarks. In the fourth and the last experiment, the applicability on real-world problems is demonstrated by solving 3 constrained mechanical engineering optimization problems. The performance is shown to be superior or equivalent to the other algorithms, which have been used in the literature. The source code of HBO is publicly available athttps://github.com/qamar-askari/HBO.
AbstractList	In an organization, a group of people working for a common goal may not achieve their goal unless they organize themselves in a hierarchy called Corporate Rank Hierarchy (CRH). This principle motivates us to map the concept of CRH to propose a new algorithm for optimization that logically arranges the search agents in a hierarchy based on their fitness. The proposed algorithm is named as heap-based optimizer (HBO) because it utilizes the heap data structure to map the concept of CRH. The mathematical model of HBO is built on three pillars: the interaction between the subordinates and their immediate boss, the interaction between the colleagues, and self-contribution of the employees. The proposed algorithm is benchmarked with 97 diverse test functions including 29 CEC-BC-2017 functions with very challenging landscapes against 7 highly-cited optimization algorithms including the winner of CEC-BC-2017 (EBO-CMAR). In the first two experiments, the exploitative and explorative behavior of HBO is evaluated by using 24 unimodal and 44 multimodal functions, respectively. It is shown through experiments and Friedman mean rank test that HBO outperforms and secures 1st rank. In the third experiment, we use 29 CEC-BC-2017 benchmark functions. According to Friedman mean rank test HBO attains 2nd position after EBO-CMAR; however, the difference in ranks of HBO and EBO-CMAR is shown to be statistically insignificant by using Bonferroni method based multiple comparison test. Moreover, it is shown through the Friedman test that the overall rank of HBO is 1st for all 97 benchmarks. In the fourth and the last experiment, the applicability on real-world problems is demonstrated by solving 3 constrained mechanical engineering optimization problems. The performance is shown to be superior or equivalent to the other algorithms, which have been used in the literature. The source code of HBO is publicly available at https://github.com/qamar-askari/HBO. •Heap-based optimizer (HBO) inspired by corporate rank hierarchy (CRH) is proposed.•HBO utilizes heap to map the hierarchy and model equations for 3 CRH activities.•A parameter (γ) to escape local optima without lacking exploitation is introduced.•Exploration and exploitation are balanced through self-adaptive parameters.•Performance is evaluated on 97 benchmarks and 3 mechanical engineering problems. In an organization, a group of people working for a common goal may not achieve their goal unless they organize themselves in a hierarchy called Corporate Rank Hierarchy (CRH). This principle motivates us to map the concept of CRH to propose a new algorithm for optimization that logically arranges the search agents in a hierarchy based on their fitness. The proposed algorithm is named as heap-based optimizer (HBO) because it utilizes the heap data structure to map the concept of CRH. The mathematical model of HBO is built on three pillars: the interaction between the subordinates and their immediate boss, the interaction between the colleagues, and self-contribution of the employees. The proposed algorithm is benchmarked with 97 diverse test functions including 29 CEC-BC-2017 functions with very challenging landscapes against 7 highly-cited optimization algorithms including the winner of CEC-BC-2017 (EBO-CMAR). In the first two experiments, the exploitative and explorative behavior of HBO is evaluated by using 24 unimodal and 44 multimodal functions, respectively. It is shown through experiments and Friedman mean rank test that HBO outperforms and secures 1st rank. In the third experiment, we use 29 CEC-BC-2017 benchmark functions. According to Friedman mean rank test HBO attains 2nd position after EBO-CMAR; however, the difference in ranks of HBO and EBO-CMAR is shown to be statistically insignificant by using Bonferroni method based multiple comparison test. Moreover, it is shown through the Friedman test that the overall rank of HBO is 1st for all 97 benchmarks. In the fourth and the last experiment, the applicability on real-world problems is demonstrated by solving 3 constrained mechanical engineering optimization problems. The performance is shown to be superior or equivalent to the other algorithms, which have been used in the literature. The source code of HBO is publicly available athttps://github.com/qamar-askari/HBO.
ArticleNumber	113702
Author	Younas, Irfan Saeed, Mehreen Askari, Qamar
Author_xml	– sequence: 1 givenname: Qamar orcidid: 0000-0001-7961-3608 surname: Askari fullname: Askari, Qamar email: syedqamar@gift.edu.pk, l165502@lhr.nu.edu.pk organization: Department of Computer Science, GIFT University, Gujranwala, Pakistan – sequence: 2 givenname: Mehreen surname: Saeed fullname: Saeed, Mehreen email: mehreen.saeed@nu.edu.pk organization: Department of Computer Science, National University of Computer and Emerging Sciences, Lahore, Pakistan – sequence: 3 givenname: Irfan surname: Younas fullname: Younas, Irfan email: irfan.younas@nu.edu.pk organization: Department of Computer Science, National University of Computer and Emerging Sciences, Lahore, Pakistan
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Cites_doi	10.1016/j.knosys.2018.11.024 10.1007/s12065-019-00212-x 10.1016/j.ins.2009.03.004 10.1007/s00158-009-0454-5 10.1016/j.mechmachtheory.2006.10.002 10.1016/j.future.2019.02.028 10.1016/j.advengsoft.2013.12.007 10.1016/0022-2569(70)90064-9 10.1109/TEVC.2003.814902 10.1109/NABIC.2009.5393690 10.1016/j.compstruc.2012.07.010 10.1007/978-3-540-72950-1_77 10.1016/j.engappai.2019.08.025 10.1016/j.cnsns.2012.05.010 10.1103/PhysRevLett.89.150201 10.1080/00268976.2011.552444 10.1109/TEVC.2002.804320 10.1016/j.ins.2008.02.014 10.1016/j.future.2019.07.015 10.1007/s40313-016-0242-6 10.1007/978-3-642-12538-6_6 10.1016/j.advengsoft.2005.04.005 10.1016/j.knosys.2019.105190 10.1007/978-3-642-25566-3_17 10.1080/01621459.1937.10503522 10.1016/j.knosys.2015.07.006 10.1109/TEVC.2008.919004 10.14569/IJACSA.2019.0100548 10.1109/4235.771163 10.1016/j.future.2017.10.052 10.1007/s00500-018-3102-4 10.1177/1687814018824930 10.1214/aoms/1177731944 10.1038/scientificamerican0792-66 10.1093/comjnl/bxy133 10.1016/j.asoc.2019.105723 10.1007/s00521-019-04464-7 10.1109/CEC.1999.782657 10.1109/CEC.2017.7969524 10.1016/j.future.2020.03.055 10.1016/j.advengsoft.2015.01.010 10.1109/ACCESS.2019.2918753 10.1016/j.advengsoft.2016.01.008 10.1016/j.swevo.2018.02.013 10.1016/j.eswa.2020.113338 10.1109/TSMCB.2009.2015956 10.1016/j.eswa.2019.05.035 10.1126/science.220.4598.671 10.1007/s00158-008-0238-3 10.1016/j.eswa.2018.08.012 10.1016/j.fcij.2018.03.002 10.1016/j.sbspro.2016.09.057 10.1007/s00521-015-1870-7 10.1016/S0045-7825(01)00323-1 10.1016/j.swevo.2014.02.002 10.1016/j.asoc.2013.12.005 10.1016/j.asoc.2012.11.026 10.1007/s40747-016-0022-8 10.1016/j.knosys.2020.105709 10.1016/j.knosys.2015.12.022 10.1016/j.swevo.2019.03.013 10.1109/ICNN.1995.488968 10.1016/j.asoc.2009.08.031 10.1016/j.eswa.2016.03.047 10.1016/j.cad.2010.12.015 10.1016/j.engappai.2019.01.001 10.1016/j.asoc.2012.05.018
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References	Heidari, Mirjalili, Faris, Aljarah, Mafarja, Chen (b0155) 2019; 97 Gupta, Tiwari, Nair (b0135) 2007; 42 Fadakar, Ebrahimi (b0100) 2016 Friedman (b0115) 1937; 32 Morais, Mourelle, Nedjah (b0300) 2018 Salih, Alsewari (b0360) 2019 Rashedi, Nezamabadi-pour, Saryazdi (b0335) 2009; 179 Lv, W., Xie, Q., Liu, Z., Zhang, X., Luo, S., & Cheng, S. (2010). Election campaign algorithm. In Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts - towards memetic algorithms. Shastri, Jagetia, Sehgal, Patel, Kulkarni (b0375) 2019 Lampinen, Storn (b0215) 2004 Flores, López, Barrera (b0110) 2011 Coello (b0055) 2002; 191 Satapathy, Naik (b0365) 2016; 2 Wang, L., & po Li, L. (2009). An effective differential evolution with level comparison for constrained engineering design. , . Kirkpatrick, Gelatt, Vecchi (b0195) 1983; 220 Ray, Liew (b0340) 2003; 7 Yao, Liu, Lin (b0425) 1999; 3 (pp. 1942–1948). IEEE, Vol. 4. Masadeh, R., A., B., & Sharieh, A. (2019). Sea lion optimization algorithm. Singh, Elaziz, Xiong (b0385) 2019; 84 Deb, Agrawal, Pratap, Meyarivan (b0070) 2000 Gandomi, Alavi (b0125) 2012; 17 Mirjalili (b0265) 2015; 89 Zhan, Zhang, Li, Chung (b0445) 2009; 39 Muneender, Vinodkumar (b0310) 2012 Huning (b0165) 1976; 62 Kumar, Kulkarni, Satapathy (b0210) 2018; 81 Salgotra, Singh (b0355) 2019; 31 Zar (b0430) 1999 Sadollah, Bahreininejad, Eskandar, Hamdi (b0350) 2013; 13 Yang, X.-S., & Deb, S. (2009). Cuckoo search via lévy flights. In Eberhart, R., & Kennedy, J. (1995). Particle swarm optimization. In (pp. 65–74). Springer, Berlin Heidelberg. Borji (b0045) 2007 Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection (Complex adaptive systems). A Bradford Book. Razmjooy, Khalilpour, Ramezani (b0345) 2016; 27 Hashim, Houssein, Mabrouk, Al-Atabany, Mirjalili (b0150) 2019; 101 (pp. 1835–1842). IEEE. Harifi, Khalilian, Mohammadzadeh, Ebrahimnejad (b0145) 2019; 12 Friedman (b0120) 1940; 11 Jain, Singh, Rani (b0170) 2019; 44 Wang, Cai, Zhou, Fan (b0395) 2008; 37 Atashpaz-Gargari, Lucas (b0030) 2007 Balochian, Baloochian (b0040) 2019; 134 Holland (b0160) 1992; 267 Yadav (b0405) 2019; 48 Zhang, Luo, Wang (b0440) 2008; 178 Melvix (b0245) 2014 Karaboga, Basturk (b0180) 2007 Mirjalili, Mirjalili, Hatamlou (b0280) 2015; 27 Rao, Savsani, Vakharia (b0330) 2011; 43 Alsattar, Zaidan, Zaidan (b0015) 2019 Ramezani, Lotfi (b0325) 2013; 13 Dorigo, M., & Di Caro, G. (1999). Ant colony optimization: a new meta-heuristic. In Yang, X.-S. (2010). A new metaheuristic bat-inspired algorithm. In Erol, Eksin (b0090) 2006; 37 Mirjalili (b0260) 2015; 83 Shadravan, Naji, Bardsiri (b0370) 2019; 80 Askari, Younas, Saeed (b0025) 2020 Yang (b0410) 2009 Dhiman, Kumar (b0075) 2019; 165 Ahmadi-Javid (b0005) 2011 (pp. 210–214). IEEE. Mahmoodabadi, Rasekh, Zohari (b0235) 2018; 3 Kumar, A., Misra, R. K., & Singh, D. (2017). Improving the local search capability of effective butterfly optimizer using covariance matrix adapted retreat phase. In Han, Kim (b0140) 2002; 6 Zhao, Qin, Zhang, Ma, Zhang, Song (b0450) 2019; 115 Milton (b0255) 1939; 34 Mirjalili, Mirjalili, Lewis (b0285) 2014; 69 Awad, N.H., P.S.J.L.B.Q., Ali, M. Z. (2017). Problem definitions and evaluation criteria for the cec 2017 special session and competition on single objective real-parameter numerical optimization. In Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Moosavian, Roodsari (b0290) 2014; 17 (6), 947–963. Arora, Singh (b0020) 2018; 23 Daskin, Kais (b0065) 2011; 109 Mirjalili (b0270) 2016; 96 Simon (b0380) 2008; 12 Xu, Cui, Zeng (b0400) 2010 Ahmady, Mehrpour, Nikooravesh (b0010) 2016; 230 Faramarzi, Heidarinejad, Stephens, Mirjalili (b0105) 2020; 191 Kashan (b0185) 2014; 16 Brammya, Praveena, Preetha, Ramya, Rajakumar, Binu (b0050) 2019 IEEE. Mirjalili, Lewis (b0275) 2016; 95 Narayanan, Moore (b0320) 1996 Golinski (b0130) 1970; 5 Nabil (b0315) 2016; 57 (5). Crawford, Soto, Cabrera, Salas-Fernández, Paredes (b0060) 2019 Jones (b0175) 2019 Zaránd, Pázmándi, Pál, Zimányi (b0435) 2002; 89 Khishe, Mosavi (b0190) 2020; 149 Zhao, Wang, Zhang (b0455) 2019; 7 Liu, Cai, Wang (b0225) 2010; 10 (pp. 1470–1477). IEEE, Vol. 2. Mezura-Montes, Velázquez-Reyes, Coello (b0250) 2006 Zhu, Hu, Zhu (b0460) 2019; 11 Moosavi, Bardsiri (b0295) 2019; 86 Eskandar, Sadollah, Bahreininejad, Hamdi (b0095) 2012; 110–111 10.1016/j.eswa.2020.113702_b0305 Zar (10.1016/j.eswa.2020.113702_b0430) 1999 Hashim (10.1016/j.eswa.2020.113702_b0150) 2019; 101 Satapathy (10.1016/j.eswa.2020.113702_b0365) 2016; 2 Arora (10.1016/j.eswa.2020.113702_b0020) 2018; 23 Gandomi (10.1016/j.eswa.2020.113702_b0125) 2012; 17 10.1016/j.eswa.2020.113702_b0220 Salih (10.1016/j.eswa.2020.113702_b0360) 2019 Rao (10.1016/j.eswa.2020.113702_b0330) 2011; 43 10.1016/j.eswa.2020.113702_b0420 Ahmady (10.1016/j.eswa.2020.113702_b0010) 2016; 230 Nabil (10.1016/j.eswa.2020.113702_b0315) 2016; 57 Shastri (10.1016/j.eswa.2020.113702_b0375) 2019 10.1016/j.eswa.2020.113702_b0390 Friedman (10.1016/j.eswa.2020.113702_b0115) 1937; 32 Melvix (10.1016/j.eswa.2020.113702_b0245) 2014 Wang (10.1016/j.eswa.2020.113702_b0395) 2008; 37 Alsattar (10.1016/j.eswa.2020.113702_b0015) 2019 Friedman (10.1016/j.eswa.2020.113702_b0120) 1940; 11 Jones (10.1016/j.eswa.2020.113702_b0175) 2019 Moosavi (10.1016/j.eswa.2020.113702_b0295) 2019; 86 Eskandar (10.1016/j.eswa.2020.113702_b0095) 2012; 110–111 Balochian (10.1016/j.eswa.2020.113702_b0040) 2019; 134 Sadollah (10.1016/j.eswa.2020.113702_b0350) 2013; 13 Shadravan (10.1016/j.eswa.2020.113702_b0370) 2019; 80 Crawford (10.1016/j.eswa.2020.113702_b0060) 2019 Deb (10.1016/j.eswa.2020.113702_b0070) 2000 Kirkpatrick (10.1016/j.eswa.2020.113702_b0195) 1983; 220 Mirjalili (10.1016/j.eswa.2020.113702_b0265) 2015; 89 Yao (10.1016/j.eswa.2020.113702_b0425) 1999; 3 Daskin (10.1016/j.eswa.2020.113702_b0065) 2011; 109 Borji (10.1016/j.eswa.2020.113702_b0045) 2007 Dhiman (10.1016/j.eswa.2020.113702_b0075) 2019; 165 Harifi (10.1016/j.eswa.2020.113702_b0145) 2019; 12 Khishe (10.1016/j.eswa.2020.113702_b0190) 2020; 149 Kumar (10.1016/j.eswa.2020.113702_b0210) 2018; 81 Mezura-Montes (10.1016/j.eswa.2020.113702_b0250) 2006 Golinski (10.1016/j.eswa.2020.113702_b0130) 1970; 5 Liu (10.1016/j.eswa.2020.113702_b0225) 2010; 10 10.1016/j.eswa.2020.113702_b0230 10.1016/j.eswa.2020.113702_b0035 10.1016/j.eswa.2020.113702_b0080 Brammya (10.1016/j.eswa.2020.113702_b0050) 2019 Lampinen (10.1016/j.eswa.2020.113702_b0215) 2004 Mirjalili (10.1016/j.eswa.2020.113702_b0270) 2016; 96 Gupta (10.1016/j.eswa.2020.113702_b0135) 2007; 42 Simon (10.1016/j.eswa.2020.113702_b0380) 2008; 12 Zhan (10.1016/j.eswa.2020.113702_b0445) 2009; 39 Ray (10.1016/j.eswa.2020.113702_b0340) 2003; 7 Yang (10.1016/j.eswa.2020.113702_b0410) 2009 Narayanan (10.1016/j.eswa.2020.113702_b0320) 1996 Coello (10.1016/j.eswa.2020.113702_b0055) 2002; 191 10.1016/j.eswa.2020.113702_b0205 Heidari (10.1016/j.eswa.2020.113702_b0155) 2019; 97 Zhang (10.1016/j.eswa.2020.113702_b0440) 2008; 178 Ramezani (10.1016/j.eswa.2020.113702_b0325) 2013; 13 10.1016/j.eswa.2020.113702_b0200 Mahmoodabadi (10.1016/j.eswa.2020.113702_b0235) 2018; 3 10.1016/j.eswa.2020.113702_b0240 10.1016/j.eswa.2020.113702_b0085 Ahmadi-Javid (10.1016/j.eswa.2020.113702_b0005) 2011 Jain (10.1016/j.eswa.2020.113702_b0170) 2019; 44 Yadav (10.1016/j.eswa.2020.113702_b0405) 2019; 48 Morais (10.1016/j.eswa.2020.113702_b0300) 2018 Huning (10.1016/j.eswa.2020.113702_b0165) 1976; 62 Muneender (10.1016/j.eswa.2020.113702_b0310) 2012 Zaránd (10.1016/j.eswa.2020.113702_b0435) 2002; 89 Holland (10.1016/j.eswa.2020.113702_b0160) 1992; 267 Mirjalili (10.1016/j.eswa.2020.113702_b0285) 2014; 69 Kashan (10.1016/j.eswa.2020.113702_b0185) 2014; 16 Zhao (10.1016/j.eswa.2020.113702_b0455) 2019; 7 Zhu (10.1016/j.eswa.2020.113702_b0460) 2019; 11 Faramarzi (10.1016/j.eswa.2020.113702_b0105) 2020; 191 Han (10.1016/j.eswa.2020.113702_b0140) 2002; 6 Zhao (10.1016/j.eswa.2020.113702_b0450) 2019; 115 Moosavian (10.1016/j.eswa.2020.113702_b0290) 2014; 17 Fadakar (10.1016/j.eswa.2020.113702_b0100) 2016 Salgotra (10.1016/j.eswa.2020.113702_b0355) 2019; 31 10.1016/j.eswa.2020.113702_b0415 Rashedi (10.1016/j.eswa.2020.113702_b0335) 2009; 179 Atashpaz-Gargari (10.1016/j.eswa.2020.113702_b0030) 2007 Xu (10.1016/j.eswa.2020.113702_b0400) 2010 Askari (10.1016/j.eswa.2020.113702_b0025) 2020 Singh (10.1016/j.eswa.2020.113702_b0385) 2019; 84 Mirjalili (10.1016/j.eswa.2020.113702_b0280) 2015; 27 Razmjooy (10.1016/j.eswa.2020.113702_b0345) 2016; 27 Karaboga (10.1016/j.eswa.2020.113702_b0180) 2007 Milton (10.1016/j.eswa.2020.113702_b0255) 1939; 34 Mirjalili (10.1016/j.eswa.2020.113702_b0260) 2015; 83 Mirjalili (10.1016/j.eswa.2020.113702_b0275) 2016; 95 Erol (10.1016/j.eswa.2020.113702_b0090) 2006; 37 Flores (10.1016/j.eswa.2020.113702_b0110) 2011
References_xml	– volume: 191 start-page: 1245 year: 2002 end-page: 1287 ident: b0055 article-title: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: A survey of the state of the art publication-title: Computer Methods in Applied Mechanics and Engineering – volume: 69 start-page: 46 year: 2014 end-page: 61 ident: b0285 article-title: Grey wolf optimizer publication-title: Advances in Engineering Software – volume: 23 start-page: 715 year: 2018 end-page: 734 ident: b0020 article-title: Butterfly optimization algorithm: A novel approach for global optimization publication-title: Soft Computing – start-page: 105709 year: 2020 ident: b0025 article-title: Political optimizer: A novel socio-inspired meta-heuristic for global optimization publication-title: Knowledge-Based Systems – year: 2019 ident: b0015 article-title: Novel meta-heuristic bald eagle search optimisation algorithm publication-title: Artificial Intelligence Review – volume: 11 start-page: 86 year: 1940 end-page: 92 ident: b0120 article-title: A comparison of alternative tests of significance for the problem of m rankings publication-title: The Annals of Mathematical Statistics – start-page: 123 year: 2004 end-page: 166 ident: b0215 article-title: Differential evolution publication-title: New optimization techniques in engineering – volume: 3 start-page: 191 year: 2018 end-page: 199 ident: b0235 article-title: Tga: Team game algorithm publication-title: Future Computing and Informatics Journal – volume: 34 start-page: 109 year: 1939 ident: b0255 article-title: A correction: The use of ranks to avoid the assumption of normality implicit in the analysis of variance publication-title: Journal of the American Statistical Association – start-page: 849 year: 2000 end-page: 858 ident: b0070 article-title: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II publication-title: Parallel problem solving from nature PPSN VI – volume: 97 start-page: 849 year: 2019 end-page: 872 ident: b0155 article-title: Harris hawks optimization: Algorithm and applications publication-title: Future Generation Computer Systems – volume: 10 start-page: 629 year: 2010 end-page: 640 ident: b0225 article-title: Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization publication-title: Applied Soft Computing – volume: 17 start-page: 14 year: 2014 end-page: 24 ident: b0290 article-title: Soccer league competition algorithm: A novel meta-heuristic algorithm for optimal design of water distribution networks publication-title: Swarm and Evolutionary Computation – year: 2011 ident: b0005 article-title: Anarchic society optimization: A human-inspired method publication-title: 2011 IEEE congress of evolutionary computation (CEC) – volume: 7 start-page: 73182 year: 2019 end-page: 73206 ident: b0455 article-title: Supply-demand-based optimization: A novel economics-inspired algorithm for global optimization publication-title: IEEE Access – reference: (pp. 1470–1477). IEEE, Vol. 2. – reference: Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection (Complex adaptive systems). A Bradford Book. – reference: & – reference: (pp. 210–214). IEEE. – reference: (6), 947–963. – reference: Awad, N.H., P.S.J.L.B.Q., Ali, M. Z. (2017). Problem definitions and evaluation criteria for the cec 2017 special session and competition on single objective real-parameter numerical optimization. In – volume: 43 start-page: 303 year: 2011 end-page: 315 ident: b0330 article-title: Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems publication-title: Computer-Aided Design – volume: 44 start-page: 148 year: 2019 end-page: 175 ident: b0170 article-title: A novel nature-inspired algorithm for optimization: Squirrel search algorithm publication-title: Swarm and Evolutionary Computation – reference: (5). – volume: 39 start-page: 1362 year: 2009 end-page: 1381 ident: b0445 article-title: Adaptive particle swarm optimization publication-title: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) – volume: 101 start-page: 646 year: 2019 end-page: 667 ident: b0150 article-title: Henry gas solubility optimization: A novel physics-based algorithm publication-title: Future Generation Computer Systems – volume: 83 start-page: 80 year: 2015 end-page: 98 ident: b0260 article-title: The ant lion optimizer publication-title: Advances in Engineering Software – volume: 165 start-page: 169 year: 2019 end-page: 196 ident: b0075 article-title: Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems publication-title: Knowledge-Based Systems – reference: , – volume: 179 start-page: 2232 year: 2009 end-page: 2248 ident: b0335 article-title: GSA: A gravitational search algorithm publication-title: Information Sciences – volume: 62 start-page: 298 year: 1976 end-page: 300 ident: b0165 publication-title: ARSP: Archiv für Rechts- und Sozialphilosophie/ Archives for Philosophy of Law and Social – volume: 42 start-page: 1418 year: 2007 end-page: 1443 ident: b0135 article-title: Multi-objective design optimisation of rolling bearings using genetic algorithms publication-title: Mechanism and Machine Theory – start-page: 583 year: 2010 end-page: 590 ident: b0400 article-title: Social emotional optimization algorithm for nonlinear constrained optimization problems publication-title: Swarm, Evolutionary, and Memetic Computing – start-page: 226 year: 2011 end-page: 237 ident: b0110 article-title: Gravitational interactions optimization publication-title: Lecture notes in computer science – reference: Lv, W., Xie, Q., Liu, Z., Zhang, X., Luo, S., & Cheng, S. (2010). Election campaign algorithm. In – volume: 134 start-page: 178 year: 2019 end-page: 191 ident: b0040 article-title: Social mimic optimization algorithm and engineering applications publication-title: Expert Systems with Applications – year: 2019 ident: b0175 article-title: Organizational theory, design, and change – volume: 16 start-page: 171 year: 2014 end-page: 200 ident: b0185 article-title: League championship algorithm (LCA): An algorithm for global optimization inspired by sport championships publication-title: Applied Soft Computing – year: 2014 ident: b0245 article-title: Greedy politics optimization: Metaheuristic inspired by political strategies adopted during state assembly elections publication-title: 2014 IEEE international advance computing conference (IACC) – volume: 81 start-page: 252 year: 2018 end-page: 272 ident: b0210 article-title: Socio evolution & learning optimization algorithm: A socio-inspired optimization methodology publication-title: Future Generation Computer Systems – volume: 95 start-page: 51 year: 2016 end-page: 67 ident: b0275 article-title: The whale optimization algorithm publication-title: Advances in Engineering Software – start-page: 43 year: 2019 end-page: 52 ident: b0060 article-title: Using a social media inspired optimization algorithm to solve the set covering problem publication-title: International conference on human-computer interaction – volume: 13 start-page: 2592 year: 2013 end-page: 2612 ident: b0350 article-title: Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems publication-title: Applied Soft Computing – volume: 110–111 start-page: 151 year: 2012 end-page: 166 ident: b0095 article-title: Water cycle algorithm – a novel metaheuristic optimization method for solving constrained engineering optimization problems publication-title: Computers & Structures – volume: 6 start-page: 580 year: 2002 end-page: 593 ident: b0140 article-title: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization publication-title: IEEE Transactions on Evolutionary Computation – volume: 115 start-page: 329 year: 2019 end-page: 345 ident: b0450 article-title: A two-stage differential biogeography-based optimization algorithm and its performance analysis publication-title: Expert Systems with Applications – reference: . IEEE. – volume: 191 year: 2020 ident: b0105 article-title: Equilibrium optimizer: A novel optimization algorithm publication-title: Knowledge-Based Systems – volume: 48 start-page: 93 year: 2019 end-page: 108 ident: b0405 article-title: Aefa: Artificial electric field algorithm for global optimization publication-title: Swarm and Evolutionary Computation – reference: Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts - towards memetic algorithms. – volume: 11 year: 2019 ident: b0460 article-title: A dynamic adaptive particle swarm optimization and genetic algorithm for different constrained engineering design optimization problems publication-title: Advances in Mechanical Engineering – reference: (pp. 1942–1948). IEEE, Vol. 4. – volume: 5 start-page: 287 year: 1970 end-page: 309 ident: b0130 article-title: Optimal synthesis problems solved by means of nonlinear programming and random methods publication-title: Journal of Mechanisms – volume: 89 start-page: 228 year: 2015 end-page: 249 ident: b0265 article-title: Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm publication-title: Knowledge-Based Systems – reference: Wang, L., & po Li, L. (2009). An effective differential evolution with level comparison for constrained engineering design. – volume: 7 start-page: 386 year: 2003 end-page: 396 ident: b0340 article-title: Society and civilization: An optimization algorithm based on the simulation of social behavior publication-title: IEEE Transactions on Evolutionary Computation – volume: 13 start-page: 2837 year: 2013 end-page: 2856 ident: b0325 article-title: Social-based algorithm (SBA) publication-title: Applied Soft Computing – start-page: 789 year: 2007 end-page: 798 ident: b0180 article-title: Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems publication-title: Lecture notes in computer science – volume: 37 start-page: 106 year: 2006 end-page: 111 ident: b0090 article-title: A new optimization method: Big bang–big crunch publication-title: Advances in Engineering Software – volume: 27 start-page: 495 year: 2015 end-page: 513 ident: b0280 article-title: Multi-verse optimizer: A nature-inspired algorithm for global optimization publication-title: Neural Computing and Applications – start-page: 25 year: 2006 end-page: 32 ident: b0250 article-title: Modified differential evolution for constrained optimization publication-title: 2006 IEEE international conference on evolutionary computation – reference: (pp. 1835–1842). IEEE. – volume: 2 start-page: 173 year: 2016 end-page: 203 ident: b0365 article-title: Social group optimization (SGO): A new population evolutionary optimization technique publication-title: Complex & Intelligent Systems – volume: 32 start-page: 675 year: 1937 end-page: 701 ident: b0115 article-title: The use of ranks to avoid the assumption of normality implicit in the analysis of variance publication-title: Journal of the American Statistical Association – reference: Kumar, A., Misra, R. K., & Singh, D. (2017). Improving the local search capability of effective butterfly optimizer using covariance matrix adapted retreat phase. In – volume: 17 start-page: 4831 year: 2012 end-page: 4845 ident: b0125 article-title: Krill herd: A new bio-inspired optimization algorithm publication-title: Communications in Nonlinear Science and Numerical Simulation – reference: Masadeh, R., A., B., & Sharieh, A. (2019). Sea lion optimization algorithm. – volume: 37 start-page: 395 year: 2008 end-page: 413 ident: b0395 article-title: Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique publication-title: Structural and Multidisciplinary Optimization – volume: 230 start-page: 455 year: 2016 end-page: 462 ident: b0010 article-title: Organizational structure publication-title: Procedia - Social and Behavioral Sciences – reference: (pp. 65–74). Springer, Berlin Heidelberg. – year: 1999 ident: b0430 article-title: Biostatistical analysis – volume: 80 start-page: 20 year: 2019 end-page: 34 ident: b0370 article-title: The sailfish optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems publication-title: Engineering Applications of Artificial Intelligence – reference: Dorigo, M., & Di Caro, G. (1999). Ant colony optimization: a new meta-heuristic. In – volume: 267 start-page: 66 year: 1992 end-page: 73 ident: b0160 article-title: Genetic algorithms publication-title: Scientific American – volume: 96 start-page: 120 year: 2016 end-page: 133 ident: b0270 article-title: SCA: A sine cosine algorithm for solving optimization problems publication-title: Knowledge-Based Systems – volume: 220 start-page: 671 year: 1983 end-page: 680 ident: b0195 article-title: Optimization by simulated annealing publication-title: Science – reference: Yang, X.-S. (2010). A new metaheuristic bat-inspired algorithm. In – volume: 12 start-page: 211 year: 2019 end-page: 226 ident: b0145 article-title: Emperor penguins colony: A new metaheuristic algorithm for optimization publication-title: Evolutionary Intelligence – volume: 149 start-page: 113338 year: 2020 ident: b0190 article-title: Chimp optimization algorithm publication-title: Expert Systems with Applications – volume: 86 start-page: 165 year: 2019 end-page: 181 ident: b0295 article-title: Poor and rich optimization algorithm: A new human-based and multi populations algorithm publication-title: Engineering Applications of Artificial Intelligence – year: 2019 ident: b0360 article-title: A new algorithm for normal and large-scale optimization problems: Nomadic people optimizer publication-title: Neural Computing and Applications – reference: Eberhart, R., & Kennedy, J. (1995). Particle swarm optimization. In – volume: 84 year: 2019 ident: b0385 article-title: Ludo game-based metaheuristics for global and engineering optimization publication-title: Applied Soft Computing – year: 2007 ident: b0030 article-title: Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition publication-title: 2007 IEEE congress on evolutionary computation – year: 1996 ident: b0320 article-title: Quantum-inspired genetic algorithms publication-title: Proceedings of IEEE international conference on evolutionary computation – year: 2016 ident: b0100 article-title: A new metaheuristic football game inspired algorithm publication-title: 2016 1st Conference on swarm intelligence and evolutionary computation – volume: 31 start-page: 8837 year: 2019 end-page: 8857 ident: b0355 article-title: The naked mole-rat algorithm publication-title: Neural Computing and Applications – year: 2012 ident: b0310 article-title: Particle swarm optimization with time varying acceleration coefficients for congestion management publication-title: 2012 IEEE conference on sustainable utilization and development in engineering and technology (STUDENT) – volume: 178 start-page: 3043 year: 2008 end-page: 3074 ident: b0440 article-title: Differential evolution with dynamic stochastic selection for constrained optimization publication-title: Information Sciences – volume: 109 start-page: 761 year: 2011 end-page: 772 ident: b0065 article-title: Group leaders optimization algorithm publication-title: Molecular Physics – start-page: 193 year: 2019 end-page: 214 ident: b0375 article-title: Expectation algorithm (exa): A socio-inspired optimization methodology publication-title: Socio-cultural Inspired Metaheuristics – year: 2019 ident: b0050 article-title: Deer hunting optimization algorithm: A new nature-inspired meta-heuristic paradigm publication-title: The Computer Journal – reference: Yang, X.-S., & Deb, S. (2009). Cuckoo search via lévy flights. In – start-page: 169 year: 2018 end-page: 180 ident: b0300 article-title: Hitchcock birds inspired algorithm publication-title: Computational collective intelligence – start-page: 61 year: 2007 end-page: 71 ident: b0045 article-title: A new global optimization algorithm inspired by parliamentary political competitions publication-title: Mexican international conference on artificial intelligence – volume: 27 start-page: 419 year: 2016 end-page: 440 ident: b0345 article-title: A new meta-heuristic optimization algorithm inspired by FIFA world cup competitions: Theory and its application in PID designing for AVR system publication-title: Journal of Control, Automation and Electrical Systems – reference: . – start-page: 169 year: 2009 end-page: 178 ident: b0410 article-title: Firefly algorithms for multimodal optimization publication-title: Stochastic algorithms: Foundations and applications – volume: 57 start-page: 192 year: 2016 end-page: 203 ident: b0315 article-title: A modified flower pollination algorithm for global optimization publication-title: Expert Systems with Applications – volume: 12 start-page: 702 year: 2008 end-page: 713 ident: b0380 article-title: Biogeography-based optimization publication-title: IEEE Transactions on Evolutionary Computation – volume: 89 year: 2002 ident: b0435 article-title: Using hysteresis for optimization publication-title: Physical Review Letters – reference: Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. – volume: 3 start-page: 82 year: 1999 end-page: 102 ident: b0425 article-title: Evolutionary programming made faster publication-title: IEEE Transactions on Evolutionary Computation – start-page: 43 year: 2019 ident: 10.1016/j.eswa.2020.113702_b0060 article-title: Using a social media inspired optimization algorithm to solve the set covering problem – year: 2019 ident: 10.1016/j.eswa.2020.113702_b0175 – volume: 165 start-page: 169 year: 2019 ident: 10.1016/j.eswa.2020.113702_b0075 article-title: Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2018.11.024 – volume: 12 start-page: 211 issue: 2 year: 2019 ident: 10.1016/j.eswa.2020.113702_b0145 article-title: Emperor penguins colony: A new metaheuristic algorithm for optimization publication-title: Evolutionary Intelligence doi: 10.1007/s12065-019-00212-x – volume: 179 start-page: 2232 issue: 13 year: 2009 ident: 10.1016/j.eswa.2020.113702_b0335 article-title: GSA: A gravitational search algorithm publication-title: Information Sciences doi: 10.1016/j.ins.2009.03.004 – volume: 62 start-page: 298 issue: 2 year: 1976 ident: 10.1016/j.eswa.2020.113702_b0165 publication-title: ARSP: Archiv für Rechts- und Sozialphilosophie/ Archives for Philosophy of Law and Social – ident: 10.1016/j.eswa.2020.113702_b0390 doi: 10.1007/s00158-009-0454-5 – ident: 10.1016/j.eswa.2020.113702_b0230 – volume: 42 start-page: 1418 issue: 10 year: 2007 ident: 10.1016/j.eswa.2020.113702_b0135 article-title: Multi-objective design optimisation of rolling bearings using genetic algorithms publication-title: Mechanism and Machine Theory doi: 10.1016/j.mechmachtheory.2006.10.002 – volume: 97 start-page: 849 year: 2019 ident: 10.1016/j.eswa.2020.113702_b0155 article-title: Harris hawks optimization: Algorithm and applications publication-title: Future Generation Computer Systems doi: 10.1016/j.future.2019.02.028 – start-page: 123 year: 2004 ident: 10.1016/j.eswa.2020.113702_b0215 article-title: Differential evolution – volume: 69 start-page: 46 year: 2014 ident: 10.1016/j.eswa.2020.113702_b0285 article-title: Grey wolf optimizer publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2013.12.007 – volume: 5 start-page: 287 issue: 3 year: 1970 ident: 10.1016/j.eswa.2020.113702_b0130 article-title: Optimal synthesis problems solved by means of nonlinear programming and random methods publication-title: Journal of Mechanisms doi: 10.1016/0022-2569(70)90064-9 – ident: 10.1016/j.eswa.2020.113702_b0305 – volume: 7 start-page: 386 issue: 4 year: 2003 ident: 10.1016/j.eswa.2020.113702_b0340 article-title: Society and civilization: An optimization algorithm based on the simulation of social behavior publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2003.814902 – year: 2014 ident: 10.1016/j.eswa.2020.113702_b0245 article-title: Greedy politics optimization: Metaheuristic inspired by political strategies adopted during state assembly elections – ident: 10.1016/j.eswa.2020.113702_b0420 doi: 10.1109/NABIC.2009.5393690 – volume: 110–111 start-page: 151 year: 2012 ident: 10.1016/j.eswa.2020.113702_b0095 article-title: Water cycle algorithm – a novel metaheuristic optimization method for solving constrained engineering optimization problems publication-title: Computers & Structures doi: 10.1016/j.compstruc.2012.07.010 – start-page: 789 year: 2007 ident: 10.1016/j.eswa.2020.113702_b0180 article-title: Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems doi: 10.1007/978-3-540-72950-1_77 – year: 2019 ident: 10.1016/j.eswa.2020.113702_b0360 article-title: A new algorithm for normal and large-scale optimization problems: Nomadic people optimizer publication-title: Neural Computing and Applications – start-page: 849 year: 2000 ident: 10.1016/j.eswa.2020.113702_b0070 article-title: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II – volume: 86 start-page: 165 year: 2019 ident: 10.1016/j.eswa.2020.113702_b0295 article-title: Poor and rich optimization algorithm: A new human-based and multi populations algorithm publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2019.08.025 – volume: 17 start-page: 4831 issue: 12 year: 2012 ident: 10.1016/j.eswa.2020.113702_b0125 article-title: Krill herd: A new bio-inspired optimization algorithm publication-title: Communications in Nonlinear Science and Numerical Simulation doi: 10.1016/j.cnsns.2012.05.010 – volume: 89 issue: 15 year: 2002 ident: 10.1016/j.eswa.2020.113702_b0435 article-title: Using hysteresis for optimization publication-title: Physical Review Letters doi: 10.1103/PhysRevLett.89.150201 – volume: 109 start-page: 761 issue: 5 year: 2011 ident: 10.1016/j.eswa.2020.113702_b0065 article-title: Group leaders optimization algorithm publication-title: Molecular Physics doi: 10.1080/00268976.2011.552444 – volume: 6 start-page: 580 issue: 6 year: 2002 ident: 10.1016/j.eswa.2020.113702_b0140 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 – volume: 178 start-page: 3043 issue: 15 year: 2008 ident: 10.1016/j.eswa.2020.113702_b0440 article-title: Differential evolution with dynamic stochastic selection for constrained optimization publication-title: Information Sciences doi: 10.1016/j.ins.2008.02.014 – volume: 101 start-page: 646 year: 2019 ident: 10.1016/j.eswa.2020.113702_b0150 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 – volume: 27 start-page: 419 issue: 4 year: 2016 ident: 10.1016/j.eswa.2020.113702_b0345 article-title: A new meta-heuristic optimization algorithm inspired by FIFA world cup competitions: Theory and its application in PID designing for AVR system publication-title: Journal of Control, Automation and Electrical Systems doi: 10.1007/s40313-016-0242-6 – ident: 10.1016/j.eswa.2020.113702_b0415 doi: 10.1007/978-3-642-12538-6_6 – volume: 37 start-page: 106 issue: 2 year: 2006 ident: 10.1016/j.eswa.2020.113702_b0090 article-title: A new optimization method: Big bang–big crunch publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2005.04.005 – start-page: 583 year: 2010 ident: 10.1016/j.eswa.2020.113702_b0400 article-title: Social emotional optimization algorithm for nonlinear constrained optimization problems – volume: 191 year: 2020 ident: 10.1016/j.eswa.2020.113702_b0105 article-title: Equilibrium optimizer: A novel optimization algorithm publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2019.105190 – start-page: 226 year: 2011 ident: 10.1016/j.eswa.2020.113702_b0110 article-title: Gravitational interactions optimization doi: 10.1007/978-3-642-25566-3_17 – volume: 32 start-page: 675 issue: 200 year: 1937 ident: 10.1016/j.eswa.2020.113702_b0115 article-title: The use of ranks to avoid the assumption of normality implicit in the analysis of variance publication-title: Journal of the American Statistical Association doi: 10.1080/01621459.1937.10503522 – volume: 89 start-page: 228 year: 2015 ident: 10.1016/j.eswa.2020.113702_b0265 article-title: Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2015.07.006 – volume: 12 start-page: 702 issue: 6 year: 2008 ident: 10.1016/j.eswa.2020.113702_b0380 article-title: Biogeography-based optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2008.919004 – ident: 10.1016/j.eswa.2020.113702_b0240 doi: 10.14569/IJACSA.2019.0100548 – volume: 3 start-page: 82 issue: 2 year: 1999 ident: 10.1016/j.eswa.2020.113702_b0425 article-title: Evolutionary programming made faster publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.771163 – year: 1996 ident: 10.1016/j.eswa.2020.113702_b0320 article-title: Quantum-inspired genetic algorithms – volume: 81 start-page: 252 year: 2018 ident: 10.1016/j.eswa.2020.113702_b0210 article-title: Socio evolution & learning optimization algorithm: A socio-inspired optimization methodology publication-title: Future Generation Computer Systems doi: 10.1016/j.future.2017.10.052 – volume: 23 start-page: 715 issue: 3 year: 2018 ident: 10.1016/j.eswa.2020.113702_b0020 article-title: Butterfly optimization algorithm: A novel approach for global optimization publication-title: Soft Computing doi: 10.1007/s00500-018-3102-4 – volume: 11 issue: 3 year: 2019 ident: 10.1016/j.eswa.2020.113702_b0460 article-title: A dynamic adaptive particle swarm optimization and genetic algorithm for different constrained engineering design optimization problems publication-title: Advances in Mechanical Engineering doi: 10.1177/1687814018824930 – volume: 11 start-page: 86 issue: 1 year: 1940 ident: 10.1016/j.eswa.2020.113702_b0120 article-title: A comparison of alternative tests of significance for the problem of m rankings publication-title: The Annals of Mathematical Statistics doi: 10.1214/aoms/1177731944 – volume: 267 start-page: 66 issue: 1 year: 1992 ident: 10.1016/j.eswa.2020.113702_b0160 article-title: Genetic algorithms publication-title: Scientific American doi: 10.1038/scientificamerican0792-66 – year: 2019 ident: 10.1016/j.eswa.2020.113702_b0050 article-title: Deer hunting optimization algorithm: A new nature-inspired meta-heuristic paradigm publication-title: The Computer Journal doi: 10.1093/comjnl/bxy133 – volume: 84 year: 2019 ident: 10.1016/j.eswa.2020.113702_b0385 article-title: Ludo game-based metaheuristics for global and engineering optimization publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2019.105723 – volume: 31 start-page: 8837 issue: 12 year: 2019 ident: 10.1016/j.eswa.2020.113702_b0355 article-title: The naked mole-rat algorithm publication-title: Neural Computing and Applications doi: 10.1007/s00521-019-04464-7 – ident: 10.1016/j.eswa.2020.113702_b0035 – year: 2016 ident: 10.1016/j.eswa.2020.113702_b0100 article-title: A new metaheuristic football game inspired algorithm – start-page: 193 year: 2019 ident: 10.1016/j.eswa.2020.113702_b0375 article-title: Expectation algorithm (exa): A socio-inspired optimization methodology – ident: 10.1016/j.eswa.2020.113702_b0080 doi: 10.1109/CEC.1999.782657 – ident: 10.1016/j.eswa.2020.113702_b0205 doi: 10.1109/CEC.2017.7969524 – ident: 10.1016/j.eswa.2020.113702_b0220 doi: 10.1016/j.future.2020.03.055 – volume: 83 start-page: 80 year: 2015 ident: 10.1016/j.eswa.2020.113702_b0260 article-title: The ant lion optimizer publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2015.01.010 – volume: 7 start-page: 73182 year: 2019 ident: 10.1016/j.eswa.2020.113702_b0455 article-title: Supply-demand-based optimization: A novel economics-inspired algorithm for global optimization publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2918753 – volume: 95 start-page: 51 year: 2016 ident: 10.1016/j.eswa.2020.113702_b0275 article-title: The whale optimization algorithm publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2016.01.008 – volume: 44 start-page: 148 year: 2019 ident: 10.1016/j.eswa.2020.113702_b0170 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 – year: 2019 ident: 10.1016/j.eswa.2020.113702_b0015 article-title: Novel meta-heuristic bald eagle search optimisation algorithm publication-title: Artificial Intelligence Review – volume: 149 start-page: 113338 year: 2020 ident: 10.1016/j.eswa.2020.113702_b0190 article-title: Chimp optimization algorithm publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2020.113338 – year: 1999 ident: 10.1016/j.eswa.2020.113702_b0430 – volume: 39 start-page: 1362 issue: 6 year: 2009 ident: 10.1016/j.eswa.2020.113702_b0445 article-title: Adaptive particle swarm optimization publication-title: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) doi: 10.1109/TSMCB.2009.2015956 – ident: 10.1016/j.eswa.2020.113702_b0200 – year: 2012 ident: 10.1016/j.eswa.2020.113702_b0310 article-title: Particle swarm optimization with time varying acceleration coefficients for congestion management – volume: 134 start-page: 178 year: 2019 ident: 10.1016/j.eswa.2020.113702_b0040 article-title: Social mimic optimization algorithm and engineering applications publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2019.05.035 – volume: 220 start-page: 671 issue: 4598 year: 1983 ident: 10.1016/j.eswa.2020.113702_b0195 article-title: Optimization by simulated annealing publication-title: Science doi: 10.1126/science.220.4598.671 – volume: 37 start-page: 395 issue: 4 year: 2008 ident: 10.1016/j.eswa.2020.113702_b0395 article-title: Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique publication-title: Structural and Multidisciplinary Optimization doi: 10.1007/s00158-008-0238-3 – volume: 115 start-page: 329 year: 2019 ident: 10.1016/j.eswa.2020.113702_b0450 article-title: A two-stage differential biogeography-based optimization algorithm and its performance analysis publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2018.08.012 – volume: 3 start-page: 191 issue: 2 year: 2018 ident: 10.1016/j.eswa.2020.113702_b0235 article-title: Tga: Team game algorithm publication-title: Future Computing and Informatics Journal doi: 10.1016/j.fcij.2018.03.002 – start-page: 169 year: 2009 ident: 10.1016/j.eswa.2020.113702_b0410 article-title: Firefly algorithms for multimodal optimization – volume: 230 start-page: 455 year: 2016 ident: 10.1016/j.eswa.2020.113702_b0010 article-title: Organizational structure publication-title: Procedia - Social and Behavioral Sciences doi: 10.1016/j.sbspro.2016.09.057 – volume: 27 start-page: 495 issue: 2 year: 2015 ident: 10.1016/j.eswa.2020.113702_b0280 article-title: Multi-verse optimizer: A nature-inspired algorithm for global optimization publication-title: Neural Computing and Applications doi: 10.1007/s00521-015-1870-7 – volume: 191 start-page: 1245 issue: 11–12 year: 2002 ident: 10.1016/j.eswa.2020.113702_b0055 article-title: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: A survey of the state of the art publication-title: Computer Methods in Applied Mechanics and Engineering doi: 10.1016/S0045-7825(01)00323-1 – start-page: 25 year: 2006 ident: 10.1016/j.eswa.2020.113702_b0250 article-title: Modified differential evolution for constrained optimization – volume: 17 start-page: 14 year: 2014 ident: 10.1016/j.eswa.2020.113702_b0290 article-title: Soccer league competition algorithm: A novel meta-heuristic algorithm for optimal design of water distribution networks publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2014.02.002 – volume: 34 start-page: 109 issue: 205 year: 1939 ident: 10.1016/j.eswa.2020.113702_b0255 article-title: A correction: The use of ranks to avoid the assumption of normality implicit in the analysis of variance publication-title: Journal of the American Statistical Association – volume: 16 start-page: 171 year: 2014 ident: 10.1016/j.eswa.2020.113702_b0185 article-title: League championship algorithm (LCA): An algorithm for global optimization inspired by sport championships publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2013.12.005 – volume: 13 start-page: 2592 issue: 5 year: 2013 ident: 10.1016/j.eswa.2020.113702_b0350 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: 2 start-page: 173 issue: 3 year: 2016 ident: 10.1016/j.eswa.2020.113702_b0365 article-title: Social group optimization (SGO): A new population evolutionary optimization technique publication-title: Complex & Intelligent Systems doi: 10.1007/s40747-016-0022-8 – start-page: 169 year: 2018 ident: 10.1016/j.eswa.2020.113702_b0300 article-title: Hitchcock birds inspired algorithm – start-page: 105709 year: 2020 ident: 10.1016/j.eswa.2020.113702_b0025 article-title: Political optimizer: A novel socio-inspired meta-heuristic for global optimization publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2020.105709 – year: 2011 ident: 10.1016/j.eswa.2020.113702_b0005 article-title: Anarchic society optimization: A human-inspired method – volume: 96 start-page: 120 year: 2016 ident: 10.1016/j.eswa.2020.113702_b0270 article-title: SCA: A sine cosine algorithm for solving optimization problems publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2015.12.022 – volume: 48 start-page: 93 year: 2019 ident: 10.1016/j.eswa.2020.113702_b0405 article-title: Aefa: Artificial electric field algorithm for global optimization publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2019.03.013 – ident: 10.1016/j.eswa.2020.113702_b0085 doi: 10.1109/ICNN.1995.488968 – volume: 10 start-page: 629 issue: 2 year: 2010 ident: 10.1016/j.eswa.2020.113702_b0225 article-title: Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2009.08.031 – year: 2007 ident: 10.1016/j.eswa.2020.113702_b0030 article-title: Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition – start-page: 61 year: 2007 ident: 10.1016/j.eswa.2020.113702_b0045 article-title: A new global optimization algorithm inspired by parliamentary political competitions – volume: 57 start-page: 192 year: 2016 ident: 10.1016/j.eswa.2020.113702_b0315 article-title: A modified flower pollination algorithm for global optimization publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2016.03.047 – volume: 43 start-page: 303 issue: 3 year: 2011 ident: 10.1016/j.eswa.2020.113702_b0330 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 – volume: 80 start-page: 20 year: 2019 ident: 10.1016/j.eswa.2020.113702_b0370 article-title: The sailfish optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2019.01.001 – volume: 13 start-page: 2837 issue: 5 year: 2013 ident: 10.1016/j.eswa.2020.113702_b0325 article-title: Social-based algorithm (SBA) publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2012.05.018
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Snippet	•Heap-based optimizer (HBO) inspired by corporate rank hierarchy (CRH) is proposed.•HBO utilizes heap to map the hierarchy and model equations for 3 CRH... In an organization, a group of people working for a common goal may not achieve their goal unless they organize themselves in a hierarchy called Corporate Rank...
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SubjectTerms	Algorithms Benchmarks Corporate hierarchy based optimization Corporate structure Data structures Global optimization Global optimization algorithm Mechanical engineering Nature-inspired meta-heuristic Optimization Rank tests Social optimization algorithm Source code
Title	Heap-based optimizer inspired by corporate rank hierarchy for global optimization
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