大域的最適化法としてのDifferential Evolutionと数値計算

金沢大学理工研究域機械工学系In this paper, Differential Evolution (DE) is examined through two aspects. Thus, one is the meta-heuristics, and the other is the global optimization technique. It is said that DE is the global optimization technique, and also belongs to the meta-huristics. Indeed, DE can find the global minimum through numerical experiments. However, there are no proofs and useful examinations with respect to such comments. In this paper, DE is compared with the Generalized Random Tunneling Algorithm (GRTA) and the Particle Swarm Optimization (PSO), that are the global optimization techniques. Through the examination, some common characteristics as the global optimization technique are clarified in this paper. In addition, the difference of the neighborhood between DE and PSO is clarified. As the result, DE is possible to belong to the global optimization techniques. Additionally, DE is also examined as the meta-heuristics. Through benchmark test problems, the search ability of DE as the global optimization technique is examined