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PDF 176 KB
DOI: http://dx.doi.org/10.7551/978-0-262-32621-6-ch102
Pages 631-638
First published 30 July 2014

Constrained Group Counseling Optimization

Mohammad A. Eita, Amin B. Shoukry and Hitoshi C. Iba

Abstract (Excerpt)

Group Counseling Optimization (GCO) has recently been proposed in an attempt to emulate the human social behavior in solving life problems through counseling within a group. After its promising results in solving unconstrained single-objective and multi-objective optimization problems, in this paper, GCO is extended to solve the constrained optimization problems for the first time. Also, a hybrid parameter-less constraint handling technique is proposed, which uses two well-known constraint handling techniques: feasible rules and penalty function. The Constrained Group Counseling Optimization (CGCO) uses gradient-based mutation only in the case of all-equality-constraints COPs to reach the extremely small feasible region easily. Moreover, CGCO performance is tested by solving the constrained benchmarks function of the CEC 2010 competition. The results demonstrate that CGCO is competitive to other state-of-the-art algorithms and consistently reaches feasible solutions.