Multi-Objective Optimization by Means of the Thermodynamical Genetic Algorithm
Naoki MORI, Yasuyuki YABUMOTO, Hajime KITA, Yoshikazu NISHIKAWA
Recently, multi-objective optimization by use of the genetic algorithms (GAs) has been getting a growing interest as a novel approach to the problem. Population based search of GA is expected to find Pareto optimal solutions of the multi-objective optimization problem in parallel. In order to achieve this goal, it is an intrinsic requirement that the evolution process of GA maintains well the diversity of the population in the Pareto optimality set. In this paper, the authors propose to utilize the Thermodynamical Genetic Algorithm (TDGA), a genetic algorithm that uses the concepts of the entropy and the temperature in the selection operation, for multi-objective optimization. Being combined with the Pareto-based ranking technique, computer simulation shows that TDGA can find a variety of Pareto optimal solutions efficiently.