摘要
针对复杂约束优化问题,提出一种基于模拟退火(SA)的粒子群(PSO)算法(SAPSO)。该算法使粒子的飞行无记忆性,结合模拟退火算法重新生成停止进化粒子的位置,增强了全局搜索能力。同时采用双群体搜索机制,一个群体保存具有可行解的粒子,用SAPSO算法使粒子逐步搜索到最优可行解;另一个群体保存具有不可行解的粒子,并且可行解群体以一定的概率接受具有不可行解的粒子,有效地维持了群体的多样性。仿真结果表明:该算法能够快速准确地找到位于约束边界上(或附近)的最优解,具有较好的稳定性。
On the basis of simulated annealing, a Particle Swarm Algorithm （PSA） was put forward for solving complicated constrained optimizations. In this algorithm the inertia weight was set to zero, and by simulated annealing algorithm it reproduces the positions of those particles whose .evolution has been ceased. This algorithm does not require the penalty function. Instead, it use the double population searching mechanism, one population storing the particles having feasible solution, the other storing the particles having no feasible solution. In a given probability, the feasible population accepts particles having no feasible solution to keep the diversity of the population. Simulation results show that, hy this proposed algorithm, the particles reach the global optimum solutions located on or near the boundary of the feasible region quickly and precisely with good stability.
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2007年第1期136-140,共5页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金资助项目(60574075)
陕西省自然科学基金资助项目(2000SL03)
关键词
人工智能
粒子群算法
模拟退火
约束优化问题
双群体
多样性
artificial intelligence
particle swarm optimization
simulated annealing
constrained optimization problems
double population
diversity
