This study introduces a multi-objective version of the recently proposed colonial competitive algorithm (CCA) called multi- objective colonial competitive algorithm. In contrast to original CCA, which used the combination of the objective functions to solve multi-objective problems, the proposed algorithm incorporates the Pareto concept to store simultaneously optimal solutions of multiple conﬂicting functions. Another novelty of this paper is the integration of the variable neighborhood search as an assimilation strategy, in order to improve the performance of the obtained solutions. To prove the effectiveness of the proposed algorithm, a set of standard test functions with high dimensions and some multi-objective engineering design problems are investigated. The results obtained amply demonstrate that the proposed approach is efﬁcient and is able to yield a wide spread of solutions with good convergence to true Pareto fronts, compared with other proposed methods in the literature. Keywords MOCCA · Pareto optimal solutions · Variable neighborhood search · Engineering optimization · High dimension · constraint-handling method Nomenclature θ Deviation angle of colony X Direction parameter of colonies motion N Number of imperialists imp d Distance between a colony and an imperialist Cost Cost of an imperialist imp VNS Variable neighborhood search Cost Cost of a
Soft Computing – Springer Journals
Published: Jun 5, 2018
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