Abstract
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.
Highlights
Yang Li Wuhan University of Science and Technology
The multi-objective whale optimization algorithm based on multi-leader guiding (MOWOAMLG) is proposed in this paper, which is the multi-objective version of whale optimization algorithm (WOA)
The differential evolution (DE) is employed to generate the offspring for the leadership solutions, while WOA is employed for the ordinary solutions
Summary
Yang Li Wuhan University of Science and Technology ) Wuhan University of Science and Technology Yun-tao Zhao Wuhan University of Science and Technology
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