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Multi-Objective Cuckoo Search Under Multiple Archiving Strategies

Kamel Zeltni

Departement IFA, Faculty NTIC, University of Constantine 2 - Abdelhamid Mehri, Constantine, Algeria

E-mail Address: kamel.zeltni@univ-constantine2.dz

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Souham Meshoul

Departement IFA, Faculty NTIC, University of Constantine 2 - Abdelhamid Mehri, Constantine, Algeria

E-mail Address: souahm.meshoul@univ-constantine2.dz

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https://doi.org/10.1142/S1469026816500206Cited by:3 (Source: Crossref)

Abstract

Cuckoo Search (CS) is a recent addition to the field of swarm-based metaheuristics. It has been shown to be an efficient approach for global optimization. Moreover, its application for solving Multi-objective Optimization (MOO) shows very promising results as well. In multi-objective context, a bounded archive is required to store the set of nondominated solutions. But, what is the best archiving strategy to use in order to maintain a bounded set with good characteristics is a critical issue that may lead to a questionable choice. In this work, the behavior of the developed multi-objective CS is studied under several archiving strategies. An extensive experimental study has been conducted using several test problems and two performance metrics related to convergence and diversity. A nonparametric test for statistical analysis is performed. In addition, we used a Multi-Objective Particle Swarm Optimization (MOPSO) for further analysis and comparison. The results revealed that archiving strategies play an important role as they can impact differently on the quality of obtained fronts depending on the problem’s characteristics. Also, this study confirms that the proposed MOCS algorithm is a very promising approach for MOPs compared to the widely used MOPSO.

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