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Mussels Wandering Optimization: An Ecologically Inspired Algorithm for Global Optimization

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Abstract

Over the last decade, we have encountered various complex optimization problems in the engineering and research domains. Some of them are so hard that we had to turn to heuristic algorithms to obtain approximate optimal solutions. In this paper, we present a novel metaheuristic algorithm called mussels wandering optimization (MWO). MWO is inspired by mussels’ leisurely locomotion behavior when they form bed patterns in their habitat. It is an ecologically inspired optimization algorithm that mathematically formulates a landscape-level evolutionary mechanism of the distribution pattern of mussels through a stochastic decision and Lévy walk. We obtain the optimal shape parameter μ of the movement strategy and demonstrate its convergence performance via eight benchmark functions. The MWO algorithm has competitive performance compared with four existing metaheuristics, providing a new approach for solving complex optimization problems.

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Acknowledgments

The authors thank Prof. M. Zhou and Prof. R. Kozma for helpful discussions and constructive comments. The authors also thank the reviewers for their instrumental comments in improving this paper from its original version. This work was supported in part by the National Science Foundation of China (grants no. 61005090, 61034004, 61272271, and 91024023), the Natural Science Foundation Program of Shanghai (grant no. 12ZR1434000), the Program for New Century Excellent Talents in University of MOE of China (grant no. NECT-10-0633), and the Ph.D. Programs Foundation of MOE of China (grant no. 20100072110038).

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Authors and Affiliations

  1. Department of Control Science and Engineering, Tongji University, Shanghai, 201804, China

    Jing An, Qi Kang, Lei Wang & Qidi Wu

  2. School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai, 201418, China

    Jing An

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  1. Jing An
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  2. Qi Kang
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Correspondence to Qi Kang.

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An, J., Kang, Q., Wang, L. et al. Mussels Wandering Optimization: An Ecologically Inspired Algorithm for Global Optimization. Cogn Comput 5, 188–199 (2013). https://doi.org/10.1007/s12559-012-9189-5

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