求解约束优化问题的动态邻域粒子群算法

Dynamic neighborhood particle swarm optimization for constrained optimization problems

粒子群算法(PSO)求解约束优化问题存在较严重的早熟收敛现象,为了有效抑制早熟收敛,提出了基于改进的约束自适应方法的动态邻域粒子群算法(IPSO)。算法采用动态邻域策略提高算法的全局搜索能力,设计了一种改进的自适应约束处理方法,根据迭代代数线性增加搜索偏向系数,在早期偏向于搜索可行解,在后期偏向于搜索最优解,并引入序列二次规划增强算法的局部搜索能力。通过基准测试函数实验对比分析,表明该算法对于约束优化问题具有较好的全局收敛性。

Particle swarm optimization （PSO） for solving constrained optimization problems existed serious premature convergence, in order to inhibit this phenomenon, this paper proposed an improved constraint adaptive and dynamic neighborhood particle swarm optimization （IPSO）. Algorithm used dynamic neighborhood strategy to improve the global search capability, and designed an improved adaptive constraint handling method. According to iteration number linear increase searched biases coefficient, in the early bias tended to search feasible solution, while in the latter tended to search the optimal solution, and adopted sequential quadratic programming to enhance local search capabilities. Through the experimental comparison of bench- mark function shows that the algorithm for constrained optimization problems with better global convergence.