Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/83947
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Type: Conference paper
Title: A hybrid particle swarm with velocity mutation for constraint optimization problems
Author: Bonyadi, M.
Li, X.
Michalewicz, Z.
Citation: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, GECCO'13, 2013: pp.1-8
Publisher: ACM
Publisher Place: online
Issue Date: 2013
ISBN: 9781450319638
Conference Name: Genetic and Evolutionary Computation Conference (15th : 2013 : Amsterdam, Netherlands)
Statement of
Responsibility: 
Mohammad Reza Bonyadi, Xiang Li, Zbigniew Michalewicz
Abstract: Two approaches for solving numerical continuous domain constrained optimization problems are proposed and experimented with. The first approach is based on particle swarm optimization algorithm with a new mutation operator in its velocity updating rule. Also, a gradient mutation is proposed and incorporated into the algorithm. This algorithm uses ε-level constraint handling method. The second approach is based on covariance matrix adaptation evolutionary strategy with the same method for handling constraints. It is experimentally shown that the first approach needs less number of function evaluations than the second one to find a feasible solution while the second approach is more effective in optimizing the objective value. Thus, a hybrid approach is proposed (third approach) which uses the first approach for locating potentially different feasible solutions and the second approach for further improving the solutions found so far. Also, a multi-swarm mechanism is used in which several instances of the first approach are run to locate potentially different feasible solutions. The proposed hybrid approach is applied to 18 standard constrained optimization benchmarks with up to 30 dimensions. Comparisons with two other state-of-the-art approaches show that the hybrid approach performs better in terms of finding feasible solutions and minimizing the objective function.
Keywords: Constraint optimization; Particle swarm optimization; Covariance matrix adaptation evolutionary strategy; Constraint handling
Rights: Copyright © 2013 ACM
RMID: 0020137059
DOI: 10.1145/2463372.2463378
Description (link): http://www.sigevo.org/gecco-2013/
Published version: http://dl.acm.org/citation.cfm?id=2463372
Appears in Collections:Computer Science publications

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