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Opposition-based multi-objective whale optimization algorithm with multi-leader guiding


During recent decades, evolutionary algorithms have been widely studied in optimization problems. The multi-objective whale optimization algorithm based on multi-leader guiding is proposed in this paper, which attempts to offer a proper framework to apply whale optimization algorithm and other swarm intelligence algorithms to solving multi-objective optimization problems. The proposed algorithm adopts several improvements to enhance optimization performance. First, search agents are classified into leadership set and ordinary set by grid mechanism, and multiple leadership solutions guide the population to search the sparse spaces to achieve more homogeneous exploration in per iteration. Second, the differential evolution and whale optimization algorithm are employed to generate the offspring for the leadership and ordinary solutions, respectively. In addition, a novel opposition-based learning strategy is developed to improve the distribution of the initial population. The performance of the proposed algorithm is verified in contrast to 10 classic or state-of-the-arts algorithms on 20 bi-objective and tri-objective unconstrained problems, and experimental results demonstrate the competitive advantages in optimization quality and convergence speed. Moreover, it is tested on load distribution of hot rolling, and the result proves its good performance in real-world applications. Thus, all of the aforementioned experiments have indicated that the proposed algorithm is comparatively effective and efficient.

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The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve this paper. This work is supported by the National Natural Science Foundation of China [grant number 51774219, 71701156], the Philosophy and Social Science Key Foundation of Department of Education of Hubei Province [grant number 20D020].

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YL and WL contributed the central idea for the study, developed software and wrote the original draft; YZ and AL contributed to refining the ideas, collating and analyzing results; all authors revised the manuscript.

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Correspondence to Wei-gang Li.

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Li, Y., Li, Wg., Zhao, Yt. et al. Opposition-based multi-objective whale optimization algorithm with multi-leader guiding. Soft Comput 25, 15131–15161 (2021).

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  • Multi-objective optimization problems
  • Whale optimization algorithm
  • Multi-leader guiding
  • Opposition-based learning strategy