Competitive Coevolutionary Genetic Algorithms for Multiobjective Optimization Problems

Enlightened by the knowledge of ecological environment and population competition, we proposed a Competitive Coevolutionary Genetic Algorithm (CCGA) based on ecological population competition mode for multiobjective optimization problems. In the algorithms, each objective corresponds to a population. At each generation, these populations compete among themselves. An ecological population density competition equation is used for reference to describe the relation between multiple objectives and to direct the adjustment over the relation at individual and population levels. The proposed approach store the Pareto optimal point obtained along the evolutionary process into external set, enforcing a more uniform distribution of such vectors along the Pareto front. The proposed approach was validated using typical test function taken from the specialized literature. Our comparative study showed that the proposed approach is competitive with respect three other algorithms that are representative of the state-of-the-art in evolutionary multiobjective optimization.