求解约束优化的模拟退火PSO算法

针对有约束最优化问题，提出了基于模拟退火的粒子群优化(particle swarm optimization simulated annealing, PSO SA)算法。该算法利用模拟退火算法以一定概率接受较差点的概率突跳特性，克服粒子群优化算法易陷入局部最优的缺陷。采用可行性原则进行约束处理，并在模拟退火算法产生新粒子的过程中保留最优不可行解的信息，弥补了可行性原则处理最优点位于约束边界附近时存在的不足。4个典型工程优化设计的实验结果表明，该算法能够寻得更优的约束最优化解.

Particle swarm optimization based on simulated annealing for solving constrained optimization problems

Considering to solve constrained optimization problems, a hybrid method combining particle swarm optimization (PSO) and simulated annealing (SA) is proposed. The probability jump property of SA is adopted to avoid PSO trapping into local optimum. A feasibility based rule is used to solve constrained problems. This rule maybe invalid when the optimum is close to the boundary of constraint conditions, so the new particle containing the information of good infeasible solution is produced in the process of SA. The algorithm is validated using four standard engineering design problems, and the results indicate that PSO SA can find out better optimum.