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Cognitive Computation

Erschienen in:

01.06.2013

Mussels Wandering Optimization: An Ecologically Inspired Algorithm for Global Optimization

verfasst von: Jing An, Qi Kang, Lei Wang, Qidi Wu

Erschienen in: Cognitive Computation | Ausgabe 2/2013

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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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Titel: Mussels Wandering Optimization: An Ecologically Inspired Algorithm for Global Optimization
verfasst von: Jing An
Qi Kang
Lei Wang
Qidi Wu
Publikationsdatum: 01.06.2013
Verlag: Springer-Verlag
Erschienen in: Cognitive Computation / Ausgabe 2/2013
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI: https://doi.org/10.1007/s12559-012-9189-5

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