Multi-agent Genetic Algorithm Based on Self-Adaptive Operator

Abstract

The crossover and mutation rates are two important parameters of multi-objective evolutionary algorithm. The agent technology is applied to solve multi-objective problem. A new multi-objective genetic algorithm based on self-adaptive agent (SAMOGA) is proposed, in which the evolution parameters is adjusted adaptively in the evolutionary process and a new selection operator is used to select individual. The algorithm is applied to several multi-objective test functions, the simulation results show that the algorithm can converge to the Pareto solutions quickly, and has a well diversity compared with NSGA-II.