求解混合变量约束优化问题的改进粒子群算法

针对工程设计中混合变量约束优化问题,提出一种基于模拟退火的粒子群算法。通过引入模拟退火算法,重新生成停止进化粒子的位置,增强了全局搜索能力。鉴于最优解位于可行域边界的特点,结合一种自适应保持群体中不可行解比例的策略,采用个体比较准则处理约束。同时结合混合变量优化问题的特点,通过转换函数,使算法真正在离散空间中进行搜索,保证了解的可行性。仿真结果表明:该算法能够快速准确地找到最优解,具有较好的稳定性。

Improved Particle Swarm Optimization Algorithm Solving Optimization Problems with Mixed Variables and Constraints

Aiming at the optimization problems with mixed variables and constraints in engineering design,a particle swarm optimization based on simulated annealing was proposed.By introducing the simulated annealing algorithm,the locations of the particles,which had stopped the evolution,were regenerated in order to enhance the global search ability.In view of the characteristics of optimal solution in the border of feasible region,combined with a strategy of adaptively maintaining the proportion of unfeasible solutions,the constraints were dealt with by using individual comparative norms.Considering the characteristics of mixed-variable optimization problem,the algorithm could search in the discrete space through the transfer function,to ensure the feasibility of solution.Simulation results show that the algorithm can find the optimal solution quickly and accurately,with good stability.