关键词: 模拟退火, 全局优化, 约束优化, 可行规则

Abstract: In this paper, an Adaptive Direct search Simulated Annealing （ADSA） algorithm is developed to overcome poor stability, low convergence rate of Direct search Simulated Annealing （DSA） algorithm for high dimensional global optimization problems. ADSA improves capability of escaping from local extreme and rapidly exploring in adjacent space, by formulating adaptive optimization module and new point generation mechanism with dynamic adjustment search based on iterative temperature. Cauchy distribution state generator enhances the computational efficiency and avoids trapping into local optimum for complex multimodal functions, which is good at searching in wide space. Feasibility-based rule is introduced into ADSA to handle constraints. The algorithm is validated using five complex functions and four standard engineering design problems commonly in the literatures. The results indicate that ADSA is more prominent than DSA, and can effectively solve the high dimensional optimization problems.

Key words: simulated annealing, global optimization, constrained optimization, feasibility-based rule