An Integrated External Archive Local Disturbance Mechanism for Multi-objective Snake Optimizer

GAO Leifu; LIU Zheng

doi:10.23919/cje.2023.00.023

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Leifu GAO and Zheng LIU, “An Integrated External Archive Local Disturbance Mechanism for Multi-objective Snake Optimizer,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–8, xxxx doi: 10.23919/cje.2023.00.023

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Leifu GAO and Zheng LIU, “An Integrated External Archive Local Disturbance Mechanism for Multi-objective Snake Optimizer,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–8, xxxx doi: 10.23919/cje.2023.00.023

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An Integrated External Archive Local Disturbance Mechanism for Multi-objective Snake Optimizer

doi: 10.23919/cje.2023.00.023

GAO Leifu^,,
LIU Zheng

1.
Institute for Optimization and Decision Analytics, Liaoning Technical University, Fuxin 123000, China

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Author Bio:
Leifu GAO was born in 1963, he received the Ph.D. degree from Liaoning Technical University. He is now a Professor of Liaoning Technical University. He is currently a Doctoral Supervisor at Management Science and Engineering, Liaoning Technical University. His interests include Optimization Theory and Method. (Email: gaoleifu@163.com)

Zheng LIU was born in July 8th, 1989. He was a Ph.D. candidate in Management Science and Engineering from Liaoning Technical University. His interests include optimization theory and methods, stochastic optimization and reliability analysis. (Email: fsliuzheng0708@163.com)
Corresponding author: Email: gaoleifu@163.com
Received Date: 2023-03-15
Accepted Date: 2023-08-07

Available Online: 2022-03-22

Abstract

Abstract

It is an interesting research direction to develop new multi-objective optimization algorithms based on meta-heuristics. The convergence accuracy and population diversity of existing methods are not satisfactory. This paper proposes an integrated external archive local disturbance mechanism for multi-objective snake optimizer (IMOSO) to overcome the above two points. There are two improved strategies. The adaptive mating between subpopulations strategy introduces the special mating behavior of snakes with multiple husbands and wives into the original snake optimizer. Some positions are updated according to the dominated relationships between the newly created individuals and the original individuals. The external archive local disturbance mechanism is used to re-search partial non-inferior solutions with poor diversities. The perturbed solutions are non-dominated sorting with the generated solutions by the next iteration to update the next external archive. The main purpose of this mechanism is to make full use of the non-inferior solution information to better guide the population evolution. The comparison results of the IMOSO and 7 state-of-the-art algorithms on WFG benchmark functions show that IMOSO has better convergence and population diversity.

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References(16)

References

[1]	C. T. Yue, B. Y. Qu, and J. Liang, “A multiobjective particle swarm optimizer using ring topology for solving multimodal multiobjective problems,” IEEE Transactions on Evolutionary Computation, vol. 22, no. 5, pp. 805–817, 2018. doi: 10.1109/TEVC.2017.2754271
[2]	M. Hartikainen, K. Miettinen, and M. M. Wiecek, “PAINT: Pareto front interpolation for nonlinear multiobjective optimization,” Computational Optimization and Applications, vol. 52, no. 3, pp. 845–867, 2012. doi: 10.1007/s10589-011-9441-z
[3]	J. Piri and P. Mohapatra, “An analytical study of modified multi-objective Harris hawk optimizer towards medical data feature selection,” Computers in Biology and Medicine, vol. 135, article no. 104558, 2021. doi: 10.1016/j.compbiomed.2021.104558
[4]	L. F. Yin and Z. X. Sun, “Distributed multi-objective grey wolf optimizer for distributed multi-objective economic dispatch of multi-area interconnected power systems,” Applied Soft Computing, vol. 117, article no. 108345, 2022. doi: 10.1016/j.asoc.2021.108345
[5]	S. J. Zhao, S. L. Ma, L. F. Gao, et al., “A novel quantum entanglement-inspired meta-heuristic framework for solving multimodal optimization problems,” Chinese Journal of Electronics, vol. 30, no. 1, pp. 145–152, 2021. doi: 10.1049/cje.2020.11.012
[6]	S. J. Zhao, L. F. Gao, J. Tu, et al., “A novel modified tree-seed algorithm for high-dimensional optimization problems,” Chinese Journal of Electronics, vol. 29, no. 2, pp. 337–343, 2020. doi: 10.1049/cje.2020.01.012
[7]	C. A. C. Coello and M. S. Lechuga, “MOPSO: A proposal for multiple objective particle swarm optimization,” in Proceedings of the 2002 Congress on Evolutionary Computation, Honolulu, HI, USA, vol. 2, pp. 1051–1056, 2002.
[8]	K. Deb, A. Pratap, S. Agarwal, et al., “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002. doi: 10.1109/4235.996017
[9]	J. P. Luo, Q. Q. Liu, Y. Yang, et al., “An artificial bee colony algorithm for multi-objective optimisation,” Applied Soft Computing, vol. 50, pp. 235–251, 2017. doi: 10.1016/j.asoc.2016.11.014
[10]	X. S. Yang and S. Deb, “Multiobjective cuckoo search for design optimization,” Computers & Operations Research, vol. 40, no. 6, pp. 1616–1624, 2013. doi: 10.1016/j.cor.2011.09.026
[11]	S. Mirjalili, S. Saremi, S. M. Mirjalili, et al., “Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization,” Expert Systems with Applications, vol. 47, pp. 106–119, 2016. doi: 10.1016/j.eswa.2015.10.039
[12]	A. K. Das, A. K. Nikum, S. V. Krishnan, et al., “Multi-objective Bonobo Optimizer (MOBO): An intelligent heuristic for multi-criteria optimization,” Knowledge and Information Systems, vol. 62, no. 11, pp. 4407–4444, 2020. doi: 10.1007/s10115-020-01503-x
[13]	M. Premkumar, P. Jangir, R. Sowmya, et al., “MOSMA: Multi-objective slime mould algorithm based on elitist non-dominated sorting,” IEEE Access, vol. 9, pp. 3229–3248, 2021. doi: 10.1109/ACCESS.2020.3047936
[14]	F. A. Hashim and A. G. Hussien, “Snake optimizer: A novel meta-heuristic optimization algorithm,” Knowledge-Based Systems, vol. 242, article no. 108320, 2022. doi: 10.1016/j.knosys.2022.108320
[15]	S. Mirjalili, S. Saremi, S. M. Mirjalili, et al., “Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization,” Expert Systems with Applications, vol. 47, pp. 106–119, 2016. doi: 10.1016/j.eswa.2015.10.039
[16]	J. J. Durillo and A. J. Nebro, “jMetal: A java framework for multi-objective optimization,” Advances in Engineering Software, vol. 42, no. 10, pp. 760–771, 2011. doi: 10.1016/j.advengsoft.2011.05.014