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Soft Computing

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25.07.2020 | Foundations

Kinetic-molecular theory optimization algorithm using opposition-based learning and varying accelerated motion

verfasst von: Chaodong Fan, Ningjun Zheng, Jinhua Zheng, Leyi Xiao, Yingnan Liu

Erschienen in: Soft Computing | Ausgabe 17/2020

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Abstract

This paper proposes an improved kinetic-molecular theory optimization algorithm (OKMTOA) by analyzing the characteristics of KMTOA cluster behavior and combining the opposition-based learning strategy with varying accelerated motion in physics. The algorithm first applies different opposition-based learning strategies to the population initialization and iterative process of the algorithm. The two-stage strategy is beneficial to improving the quality of the solution set and accelerating the convergence of the algorithm. Then, based on the concept of varying accelerated motion, the acceleration formula is improved to increase the ability to escape local optimum. The experimental results show that the algorithm has good performance in solution precision, convergence speed and can be well applied to the functions with different shift values.

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Alomoush AA, Alsewari AA, Alamri HS, Zamli KZ, Alomoush W, Younis MI (2020) Modified opposition based learning to improve harmony search variants exploration. In: Advances in intelligent systems and computing, vol 1073. Springer, Cham, pp 279–287

Aslimani N, Ellaia R (2018) A new hybrid algorithm combining a new chaos optimization approach with gradient descent for high dimensional optimization problems. Comput Appl Math 37(3):2460–2488MathSciNetMATHCrossRef

Bairathi D, Gopalani D (2020) Random opposition-based learning for computational intelligence. In: Advances in intelligent systems and computing, vol 933. Springer, Singapore, pp 111–120

Bianchi L, Dorigo M, Gambardella LM, Gutjahr WJ (2009) A survey on metaheuristics for stochastic combinatory al optimization. Nat Comput Int J 8:239–287MATHCrossRef

Blum C, Puchinger J, Raidl GR, Roli A (2011) Hybrid metaheuristics in combinatorial optimization: a survey. Appl Soft Comput 11:4135–4151MATHCrossRef

Boussaïd I, Lepagnot J, Siarry P (2013) A survey on optimization metaheuristics. Inform Sci 237:82–117MathSciNetMATHCrossRef

Ewees AA, Elaziz MA, Houssein EH (2018) Improved grasshopper optimization algorithm using opposition based learning. Expert Syst Appl 112(S0957):417418303701

Fan CD, Ouyang HL, Zhang YJ, Ai ZY (2013) Optimization algorithm based on kinetic-molecular theory. J Central South Univ 20(12):3504–3512CrossRef

Fan CD, Ren K, Zhang YJ et al (2016) Optimal multilevel thresholding based on molecular kinetic theory optimization algorithm and line intercept histogram. J Central South Univ 23(4):880–890CrossRef

Fan C, Li J, Yi L et al (2018) An optimal algorithm based on kinetic-molecular theory with artificial memory to solving economic dispatch problem. Curr Sci 115(3):454–464CrossRef

Fan CD, Liu YN, Zhang J et al (2019) A weak linked multi-subpopulation kinetic-molecular theory optimization algorithm. Control Theory Appl 36(1):108–119MATH

Gogna A, Tayal A (2013) Metaheuristics: review and application. J Exp Theory Artif Intell 25:503–526CrossRef

Grosan C, Abraham A (2011) Intelligent systems: a modern approach. Intelligent systems reference library. Springer, BerlinMATHCrossRef

Gupta S, Deep K (2018a) An opposition-based chaotic grey wolf optimizer for global optimisation tasks. J Exp Theor Artif Intell 31:1–29

Gupta S, Deep K (2018b) A hybrid self-adaptive sine cosine algorithm with opposition based learning. In: Expert systems with applications

Gupta S, Deep K (2019) Improved grey wolf optimizer based on opposition-based learning. In: Soft computing for problem solving, pp 327–338

Gupta S, Deep K, Heidari AA et al (2019) Harmonized salp chain-built optimization. Engineering with Computers 2019:1–31

Hashim FA, Houssein EH, Mabrouk MS, Al-Atabany W, Mirjalili S (2019) Henry gas solubility optimization: a novel physics-based algorithm. Future Generat Comput Syst 101:646–667. https://doi.org/10.1016/j.future.2019.07.015 CrossRef

Iwasa M, Tanaka D (2017) Mechanism underlying the diverse collective behavior in the swarm oscillator model. Phys Lett A 381(36):3054–3061MathSciNetMATHCrossRef

Kang Q, Xiong CF, Zhou MC et al (2018) Opposition based hybrid strategy for particle swarm optimization in noisy environments. IEEE Access 6:21888–21900CrossRef

Li J, Fang G (2019) A novel differential evolution algorithm integrating opposition based learning and adjacent two generations hybrid competition for parameter selection of SVM. Evol Syst. https://doi.org/10.1007/s12530-019-09313-5 CrossRef

Liang J, Ge SL, Qu BY, Yu KJ (2019) Improved particle swarm optimization algorithm for solving power system economic dispatch problem. Control Decis. https://doi.org/10.13195/j.kzyjc.2018.1490 CrossRef

Loshchilov I, Glasmachers T, Beyer HG (2019) Large scale black-box optimization by limited-memory matrix adaptation. IEEE Trans Evol Comput 23(2):353–358CrossRef

Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83(C):80–98CrossRef

Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133CrossRef

Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67CrossRef

Mirjalili S, Mirjalili Seyed M, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69(3):46–61CrossRef

Rahnamayan S, Tizhoosh HR, Salama MM (2008) Opposition based differential evolution. IEEE Trans Evol Comput 12(1):64–79CrossRef

Raj S, Bhattacharyya B (2018) Reactive power planning by opposition based grey wolf optimization method. Int Trans Electr Energy Syst 3:e2551CrossRef

Torreao VDA, Vimieiro R (2018) Effects of population initialization on evolutionary techniques for subgroup discovery in high dimensional datasets. In: 7th Brazilian conference on intelligent systems (BRACIS). São Paulo, SP, Brazil, October 22–25, 25–30

Wang H, Wu Z, Rahnamayan S (2011a) Enhancing particle swarm optimization using generalized opposition-based learning. Inf Sci 181(20):4699–4714MathSciNetCrossRef

Wang H, Wu Z, Rahnamayan S (2011b) Enhanced opposition-based differential evolution for solving high dimensional continuous optimization problems. Soft Comput 15(11):2127–2140CrossRef

Zheng S, Janecek A, Tan Y. (2013) Enhanced Fireworks Algorithm. In: IEEE congress on evolutionary computation (CEC), Cancun, Mexico, June 20–23, pp 2069–2077

Titel: Kinetic-molecular theory optimization algorithm using opposition-based learning and varying accelerated motion
verfasst von: Chaodong Fan
Ningjun Zheng
Jinhua Zheng
Leyi Xiao
Yingnan Liu
Publikationsdatum: 25.07.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 17/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-020-05057-6

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