A Memetic Co-evolutionary Differential Evolution Algorithm for Constrained Optimization

Liu, B. , Ma, H., Zhang, X. and Zhou, Y. (2007) A Memetic Co-evolutionary Differential Evolution Algorithm for Constrained Optimization. In: 2007 IEEE Congress on Evolutionary Computation, Singapore, 25-28 Sep 2007, pp. 2996-3002. ISBN 9781424413393 (doi: 10.1109/CEC.2007.4424853)

Full text not currently available from Enlighten.


In this paper, a memetic co-evolutionary differential evolution algorithm (MCODE) for constrained optimization is proposed. Two cooperative populations are constructed and evolved by independent differential evolution (DE) algorithm. The purpose of the first population is to minimize the objective function regardless of constraints, and that of the second population is to minimize the violation of constraints regardless of the objective function. Interaction and migration happens between the two populations when separate evolutions go on for several iterations, by migrating feasible solutions into the first group, and infeasible ones into the second group. Then, a Gaussian mutation is applied to the individuals when the best solution keep unchanged for several generations. The algorithm is tested by five famous benchmark problems, and is compared with methods based on penalty functions, co-evolutionary genetic algorithm (COGA), and co-evolutionary differential evolution algorithm (CODE). The results proved the proposed cooperative MCODE is very effective and efficient.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Liu, Dr Bo
Authors: Liu, B., Ma, H., Zhang, X., and Zhou, Y.
College/School:College of Science and Engineering > School of Engineering

University Staff: Request a correction | Enlighten Editors: Update this record