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Memetic Computing

Erschienen in:

04.07.2017 | Regular Research Paper

Hybrid multi-objective cuckoo search with dynamical local search

verfasst von: Maoqing Zhang, Hui Wang, Zhihua Cui, Jinjun Chen

Erschienen in: Memetic Computing | Ausgabe 2/2018

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Abstract

Cuckoo search (CS) is a recently developed meta-heuristic, which has shown good search abilities on many optimization problems. In this paper, we present a hybrid multi-objective CS (HMOCS) for solving multi-objective optimization problems (MOPs). The HMOCS employs the non-dominated sorting procedure and a dynamical local search. The former is helpful to generate Pareto fronts, and the latter focuses on enhance the local search. In order to verify the performance of our approach HMOCS, six well-known benchmark MOPs were used in the experiments. Simulation results show that HMOCS outperforms three other multi-objective algorithms in terms of convergence, spread and distributions.

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Titel: Hybrid multi-objective cuckoo search with dynamical local search
verfasst von: Maoqing Zhang
Hui Wang
Zhihua Cui
Jinjun Chen
Publikationsdatum: 04.07.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Memetic Computing / Ausgabe 2/2018
Print ISSN: 1865-9284
Elektronische ISSN: 1865-9292
DOI: https://doi.org/10.1007/s12293-017-0237-2

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