论文信息 - A cooperative coevolutionary algorithm for multiobjective optimization

A cooperative coevolutionary algorithm for multiobjective optimization

This paper presents a kind of cooperative co-evolutionary algorithm (CCEA) for multi-objective optimization (MOO). In this algorithm, solutions evolve in the form of cooperative subpopulations. An archive stores non-dominated solutions and helps to evaluate individuals in the subpopulations. The mechanism of niching is applied to maintain the diversity of solutions in the archive. Meanwhile, an extending operator is designed to mine information on solution distribution from the archive and guide the search to regions that are not explored enough. Extensive simulations are performed on different benchmark problems for various multi-objective evolutionary algorithms (MOEAs) and indicate that CCEA is strongly competitive with five recent well-known MOEAs in finding a good non-dominated solution set.

[1] Kenneth A. De Jong,et al. A Cooperative Coevolutionary Approach to Function Optimization , 1994, PPSN.

[2] Martin J. Oates,et al. The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation , 2000, PPSN.

[3] David W. Corne,et al. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[4] Tong Heng Lee,et al. Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization , 2001, IEEE Trans. Evol. Comput..

[5] Tong Heng Lee,et al. Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[6] X. Yao,et al. Scaling up fast evolutionary programming with cooperative coevolution , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[7] Nachol Chaiyaratana,et al. Multi-objective Co-operative Co-evolutionary Genetic Algorithm , 2002, PPSN.