An efficient evolutionary algorithm for engineering design problems

An efficient evolutionary algorithm for engineering design problems 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Soft Computing Springer Journals

An efficient evolutionary algorithm for engineering design problems

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Mathematical Logic and Foundations; Control, Robotics, Mechatronics
ISSN
1432-7643
eISSN
1433-7479
D.O.I.
10.1007/s00500-018-3273-z
Publisher site
See Article on Publisher Site

Abstract

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

Journal

Soft ComputingSpringer Journals

Published: Jun 5, 2018

References

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