Abstract:
In this paper, a memetic co-evolutionary differential evolution algorithm (MCODE) for constrained optimization is proposed. Two cooperative populations are constructed an...Show MoreMetadata
Abstract:
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.
Published in: 2007 IEEE Congress on Evolutionary Computation
Date of Conference: 25-28 September 2007
Date Added to IEEE Xplore: 07 January 2008
ISBN Information: