| Article ID: | iaor1999884 |
| Country: | Japan |
| Volume: | 11 |
| Issue: | 3 |
| Start Page Number: | 103 |
| End Page Number: | 111 |
| Publication Date: | Mar 1998 |
| Journal: | Transactions of the Institute of Systems, Control and Information Engineers |
| Authors: | Nishikawa Yoshikazu, Mori Naoki, Kita Hajime, Yabumoto Yasuyuki |
| Keywords: | programming: multiple criteria |
Recently, multi-objective optimization by use of the genetic algorithms (GAs) has been getting a growing interest as a novel approach. 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 TGDA can find a variety of Pareto optimal solutions efficiently.