Abstract
Cuckoo Search has been recently added to the pool of nature inspired metaheuristics. Its promising results in solving single objective optimization motivate its use in multiobjetive context. In this paper we describe a Pareto based multiobjective Cuckoo search algorithm. Like swarm based metaheuristics, the basic algorithm needs to specify the best solutions in order to update the population. As the best solution is not unique in multiobjective optimization, this requires the use of a selection strategy. For this purpose, we propose in this paper investigation of five leader selection strategies namely random selection, sigma method, crowding distance method, hybrid selection method and MaxiMin method. Performance of the proposed algorithm has been assessed using benchmark problems from the field of numerical optimization. Impact of selection strategies on both convergence and diversity of obtained fronts has been studied empirically. Experimental results show in one hand the great ability of the proposed algorithm to deal with multiobjective optimization and in other hand no strategy has been shown to be the best in all test problems from both convergence and diversity points of view. However they may impact significantly the performance of the algorithm in some cases.
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Zeltni, K., Meshoul, S. (2014). Multi-Objective Cuckoo Search with Leader Selection Strategies. In: Fouilhoux, P., Gouveia, L., Mahjoub, A., Paschos, V. (eds) Combinatorial Optimization. ISCO 2014. Lecture Notes in Computer Science(), vol 8596. Springer, Cham. https://doi.org/10.1007/978-3-319-09174-7_36
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DOI: https://doi.org/10.1007/978-3-319-09174-7_36
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