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

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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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References

  • 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):2460–2488

    Article  MathSciNet  MATH  Google Scholar 

  • 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 8:239–287

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  • 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):880–890

    Article  Google Scholar 

  • 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):454–464

    Article  Google Scholar 

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

    MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Book  MATH  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Raj S, Bhattacharyya B (2018) Reactive power planning by opposition based grey wolf optimization method. Int Trans Electr Energy Syst 3:e2551

    Article  Google Scholar 

  • 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):4699–4714

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  • 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

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Acknowledgements

This study was funded by the National Natural Science Foundation of China (61573299), Hunan Provincial Natural Science Foundation of China (2020JJ4587), Open Fund Project of Fujian Provincial Key Laboratory of Data Intensive Computing (BD201808), the Scientific Research Project of Xiangtan University (15XZX31, 16XZX30), and the Guangdong Basic and Applied Basic Research Foundation (2019A1515110423).

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Authors and Affiliations

  1. College of Information Technology and Management, Hunan University of Finance and Economics, Changsha, 410205, Hunan, China

    Chaodong Fan, Ningjun Zheng & Yingnan Liu

  2. Foshan Green Intelligent Manufacturing Research Institute of Xiangtan University, Foshan, 528000, Guangdong, China

    Chaodong Fan & Jinhua Zheng

  3. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, Hunan, China

    Leyi Xiao

  4. Fujian Provincial Key Laboratory of Data Intensive Computing, Quanzhou, 362000, Fujian, China

    Chaodong Fan

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  1. Chaodong Fan
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  2. Ningjun Zheng
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  3. Jinhua Zheng
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  4. Leyi Xiao
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  5. Yingnan Liu
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Correspondence to Leyi Xiao.

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Communicated by A. Di Nola.

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Fan, C., Zheng, N., Zheng, J. et al. Kinetic-molecular theory optimization algorithm using opposition-based learning and varying accelerated motion. Soft Comput 24, 12709–12730 (2020). https://doi.org/10.1007/s00500-020-05057-6

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  • DOI: https://doi.org/10.1007/s00500-020-05057-6

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