Multi‐swarm and chaotic whale‐particle swarm optimization algorithm with a selection method based on roulette wheel

Expert Systems - Tập 38 Số 8 - 2021
Kayvan Asghari1, Mohammad Masdari1, Farhad Soleimanian Gharehchopogh1, Rahim Saneifard2
1Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
2Department of Applied Mathematics, Urmia Branch Islamic Azad University Urmia Iran

Tóm tắt

AbstractThe particle swarm optimization (PSO) and the whale optimization algorithm (WOA) are two admired optimization methods that have drawn various researchers' attention. The PSO implements some particles' intelligent movements in a search space, and the WOA is originated based on the hunting mechanism of humpback whales. The PSO and WOA have different strategies for moving towards the optimum solution. Nevertheless, both algorithms' performances encounter several problems, such as premature convergence and falling in local optimums. Several approaches have been proposed to enhance meta‐heuristic algorithms' performance, such as applying the chaotic maps, adding mathematical or stochastic operators or local searches, and hybridizing the algorithms. In this article, a new hybrid algorithm denoted as chaotic‐based hybrid whale and PSO has been presented by improving the WOA, combining it with PSO, and using the chaotic maps. The hybrid algorithm has significantly more diverse movements than both of the mentioned algorithms. Therefore, it explores different regions of a problem's search space more precisely and avoids local optima. The roulette wheel selection operator has also been applied based on their fitness value to select the proposed algorithm's search agents and exploit promising regions of the search space. In the hybrid algorithm, the chaotic maps have been applied to initialize the whales' population, particles of the particle swarm, and adjust motion parameters to increase population diversity. The multi‐swarm version of the proposed algorithm with higher performance than the single‐swarm version and other methods has been introduced in this article too. The proposed algorithms have been evaluated using 23 mathematical benchmark functions, including unimodal, multimodal, and composite functions and four engineering optimization problems. The obtained results and statistical tests prove that the proposed algorithms provide competitive solutions for most of the experiments, compared to the state‐of‐the‐art and well‐known optimization meta‐heuristic methods in terms of convergence towards the global optimum, local optima avoidance, exploration, and exploitation.

Từ khóa


Tài liệu tham khảo

10.1016/j.enconman.2018.05.062

10.1109/ACCESS.2020.3031472

10.1016/j.eswa.2010.02.042

10.1016/j.chaos.2007.09.063

10.1016/j.swevo.2019.03.013

10.1016/B978-012064155-0/50012-4

10.3233/JIFS-16798

10.1007/s00500-018-3102-4

10.1007/s13748-021-00244-4

10.1016/j.energy.2017.03.054

10.1002/nme.1620210904

10.1007/978-3-540-24653-4_50

10.1016/j.compstruc.2014.03.007

10.1002/(SICI)1097-0207(19960315)39:5<829::AID-NME884>3.0.CO;2-U

10.1016/S0166-3615(99)00046-9

10.1080/02630250008970288

10.1016/S0045-7825(01)00323-1

daSilveira L. A. Soncco‐Álvarez J. L. deLima T. A. &Ayala‐Rincón M.(2016).Memetic and opposition‐based learning genetic algorithms for sorting unsigned genomes by translocations. Paper presented at the Advances in Nature and Biologically Inspired Computing Cham.

10.1016/j.swevo.2016.01.004

10.2514/3.10834

10.1016/S0045-7825(99)00389-8

10.1016/j.swevo.2011.02.002

10.1080/00207160108805080

10.1016/j.jocs.2018.10.002

Eberhart R. C. &Kennedy J.(1995).Particle swarm optimization. Paper presented at the Proceeding of IEEE International Conference on Neural Network Perth Australia (pp. 1942–1948).https://doi.org/10.1109/ICNN.1995.488968

Eberhart R. C. &Shi Y.(2000 July).Comparing inertia weights and constriction factors in particle swarm optimization. Paper presented at the Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

10.1093/acprof:oso/9780199566433.001.0001

10.1007/s10586-021-03264-w

10.1080/01621459.1937.10503522

10.1214/aoms/1177731944

10.1016/j.cnsns.2012.05.010

10.1016/j.jocs.2013.10.002

10.1007/s00366-011-0241-y

10.1007/s00521-012-1028-9

10.1016/j.cnsns.2012.06.009

10.1016/j.swevo.2019.03.004

10.1007/s10462-019-09733-4

10.1007/978-1-4613-0303-9_33

10.1109/81.933333

10.1016/j.engappai.2006.03.003

10.1016/j.future.2019.02.028

10.1093/acprof:oso/9780198507239.001.0001

10.1038/scientificamerican0792-66

Holm S., 1979, A simple sequentially rejective multiple test procedure, Scandinavian Journal of Statistics, 6, 65

10.1007/s10723-021-09556-0

10.1504/IJMMNO.2013.055204

10.1007/s10898-007-9149-x

10.1007/s10462-012-9328-0

10.1016/j.jcde.2017.12.006

10.1016/j.cma.2004.09.007

10.1007/s00521-010-0432-2

10.1016/j.compstruc.2006.11.020

10.1016/j.eswa.2015.07.043

Liang J. Suganthan P. &Deb K.(2005).Novel composition test functions for numerical global optimization. Paper presented at the Proceedings 2005 IEEE Swarm Intelligence Symposium 2005. SIS 2005 (pp. 68–75).https://doi.org/10.1109/SIS.2005.1501604

10.1016/j.asoc.2009.08.031

10.1007/s11227-019-02933-3

10.1007/s12652-020-01902-6

10.1007/s10922-016-9385-9

10.1007/978-0-387-21830-4_7

10.1080/03081070701303470

10.1016/j.advengsoft.2015.01.010

10.1016/j.knosys.2015.07.006

10.1016/j.advengsoft.2017.07.002

10.1016/j.advengsoft.2016.01.008

10.1007/s00521-015-1870-7

10.1016/j.advengsoft.2013.12.007

10.1007/s12065-020-00479-5

10.1016/j.asoc.2017.09.039

10.1016/j.amc.2006.07.026

10.1017/CBO9780511803260

10.1007/b97624

Price K., 2005, Differential evolution – A practical approach to global optimization

10.1115/1.3438995

Rahnamayan S. Tizhoosh H. R. &Salama M. M. A.(2007 September).Quasi‐oppositional differential evolution. Paper presented at the 2007 IEEE Congress on Evolutionary Computation.

10.1016/j.asoc.2011.05.008

10.1016/j.cad.2010.12.015

10.1002/9781119454816

10.1016/j.ins.2009.03.004

10.1080/03052150108940941

10.1016/j.engappai.2017.01.006

10.1016/j.engappai.2019.08.025

10.1016/j.advengsoft.2017.01.004

10.1007/s10723-020-09530-2

10.1016/j.asoc.2018.07.033

10.1016/j.ipl.2004.11.003

10.1007/BFb0040810

Shi Y. &Eberhart R. C.(1999 July).Empirical study of particle swarm optimization. Paper presented at the Proceedings of the 1999 Congress on Evolutionary Computation‐CEC99.

Suganthan P., 2005, Problem definitions and evaluation criteria for the CEC 2005 special session on real‐parameter optimization, Technical Report, Nanyang Technological University, Singapore, 341

10.1016/j.engappai.2019.103330

10.1007/BF00047506

10.1016/j.cnsns.2011.08.021

Tizhoosh H. R.(2005 November).Opposition‐based learning: A new scheme for machine intelligence. Paper presented at the International Conference on Computational Intelligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and Internet Commerce (CIMCA‐IAWTIC'06).

10.1016/j.amc.2006.09.087

10.1007/s12293-016-0212-3

10.1016/j.apm.2013.10.052

10.1016/j.ins.2014.02.123

10.1515/9781400858156.273

10.1109/4235.585893

10.1016/j.asoc.2018.02.042

10.1016/j.chaos.2006.04.057

10.1007/978-3-642-12538-6_6

10.1109/4235.771163

10.1016/j.asoc.2012.12.020

10.1016/j.ins.2008.02.014

10.1016/j.eswa.2010.09.032

10.1016/j.asoc.2016.11.047

Thông tin
Thông tin xuất bản

Expert Systems

Tập 38 Số 8

Thông tin tác giả