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适应性粒子群寻优算法I(APSO-Ⅰ)是在有序的决策中始终引入随机的、不可预测的决定.为解决APSO-I算法收敛深度不够的问题,提出适应性粒子群寻优第II代算法(APSO-Ⅱ).APSO-Ⅱ算法是将有序(标准PSO粒子群寻优)和无序(自适应寻优)进行适当的分离,以发挥各自的优势.在自适应寻优阶段,通过在最优粒子邻域空间探寻更优化的解.一但新的优化解被发掘,便利用标准PSO快速寻优.典型复杂函数优化的仿真结果表明,APSO-Ⅱ在收敛速度和收敛深度上均优于DPSO(耗散型PSO),HPSO(自适应层次PSO),AEPSO(自适应逃逸PSO)和APSO-Ⅰ.
The adaptive particle swarm optimization algorithm I (APSO-I) always introduces stochastic and unpredictable decisions in the orderly decision-making.In order to solve the problem of insufficient convergence depth of the APSO-I algorithm, an adaptive Particle Swarm Optimization II algorithm (APSO-Ⅱ) .APSO-Ⅱ algorithm is the order (standard PSO particle swarm optimization) and disorder (adaptive optimization) for the appropriate separation, in order to play their own advantages in the adaptive optimization Stage, we search for a more optimal solution by searching for the optimal solution in the neighborhood of the optimal particle. Once a new optimization solution is discovered, the standard PSO is used to search quickly. Simulation results of typical complex functions show that APSO- Depth is better than DPSO (Dissipative PSO), HPSO (Adaptive Level PSO), AEPSO (Adaptive Escape PSO) and APSO-Ⅰ.