基于免疫粒子群混合优化算法的混合电梯群控系统新型派梯策略

粒子群算法（Particle Swarm Optimization, PSO）具有模型简单，收敛的快速性和在连续系统中应用的优势，但存在着进化的后期收敛速度变慢，易陷入局部值的缺点。人工免疫 (Artificial Immune, AI) 优化算法利用人工免疫系统抗体多样性的机理和克隆选择算子搜索抗体群，具有很强的全局寻优能力，可以弥补粒子群算法的缺点。结合这两种算法的优缺点，提出了免疫粒子群 (Immune PSO, IPSO) 混合优化算法，并应用于混合电梯群控系统中进行派梯优化，取得了良好的效果。与人工免疫优化算法、粒子群算法分别进行比较，显示出免疫粒子群混合优化算法在优化派梯方案的优越性。文章的结尾展望了今后工作的研究重点和发展趋势。

A New Hybrid Elevator Group Control System Scheduling Strategy Based On Immune Particle Swarm Hybrid Optimization Algorithm

Particle swarm optimization has the advantages of simple model, rapid convergence and the application in continuous system. But it still has some disadvantages of slowing convergence in the late evolution and trapping into local values easily. Artificial immune(AI) optimization algorithm has a strong capacity optimization to make up the disadvantages of particle swarm optimization by using the diversity of the antibodies in artificial immune system and clonal selection operator to search for the antibody group. For combining the advantages of the two algorithms, propose immune particle swarm hybrid optimization algorithm. Apply the hybrid optimization algorithm on hybrid elevator group control system for optimizing scheduling, and has made good effect. Comparing with artificial immune optimization algorithm and particle swarm optimization algorithm under the same condition, demonstrates the superiority in optimizing scheduling the elevators. This paper prospects for the emphasis of future research and the trend in the end.