Multi-objective Optimisation by Co-operative Co-evolution

This paper presents the integration between a co-operative co-evolutionary genetic algorithm (CCGA) and four evolutionary multiobjective optimisation algorithms (EMOAs): a multi-objective genetic algorithm (MOGA), a niched Pareto genetic algorithm (NPGA), a nondominated sorting genetic algorithm (NSGA) and a controlled elitist nondominated sorting genetic algorithm (CNSGA). The resulting algorithms can be referred to as co-operative co-evolutionary multi-objective optimisation algorithms or CCMOAs. The CCMOAs are benchmarked against the EMOAs in seven test problems. The first six problems cover different characteristics of multi-objective optimisation problems, namely convex Pareto front, non-convex Pareto front, discrete Pareto front, multimodality, deceptive Pareto front and non-uniformity of solution distribution. In contrast, the last problem is a two-objective real-world problem, which is generally referred to as the continuum topology design. The results indicate that the CCMOA...

• Type: Conference
• Year: 2004
• Where: PPSN
• Authors: Kuntinee Maneeratana, Kittipong Boonlong, Nachol Chaiyaratana
• Keywords: Distributed And Parallel Computing, Genetic Algorithm, Pareto Front, Pareto Genetic Algorithm, PPSN 2004