一种量化正交免疫克隆粒子群数值优化算法  

An Orthogonal Immune Clone Particle Swarm Algorithm with Quantization for Numerical Optimization

作　　者：丛琳[1] 焦李成[1] 沙宇恒[1] 

机构地区：[1]西安电子科技大学智能信息处理研究所,西安710071

出　　处：《模式识别与人工智能》2007年第5期583-592,共10页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60133010.No.60372045);国家863计划项目(No.2002AA135080);国家973计划项目(No.2001CB309403)资助

摘　　要：为了解决粒子群算法收敛速度慢和早熟收敛等问题,根据生物免疫系统理论中的克隆选择学说,提出一种量化正交免疫克隆粒子群算法.给出正交子空间分割算法,并采用正交交叉策略来增强子代个体解分布的均匀性.为避免个体邻域内最优解的丢失,提出一种自学习算子.并证明该算法的全局收敛性.实验中对标准测试函数进行20～1000维的测试,分别与5种算法进行比较,并给出算法参数对计算复杂度的影响.结果表明,本文方法有效克服早熟收敛,并且在保持种群多样性的同时提高收敛速度.In order to overcome prematurity and low searching speed of PSO algorithm, an orthogonal immune clone particle swarm algorithm with quantization （OICPSO/Q） is proposed according to the immune clone selection theory. An orthogonal subspace division method is presented and the orthogonal crossover strategy is used to increase the uniformity of solution. To avoid losing the optimal solution in neighborhood of individuals, a self-learning operator is presented. The global convergence of OICPSO/Q has been proved by theoretical analysis. In experiments, OICPSO/Q is tested on unconstrained benchmark problems with 20-1 000 dimensions, and is compared with five methods. The effects of parameters on computational cost of the algorithm are analyzed. The results indicate that OICPSO/Q is capable of solving complex problems and preserving the diversity of population. To some extent, it avoids prematurity and improves the convergence speed.

关 键 词：粒子群优化 人工免疫系统 克隆选择 正交设计 进化计算 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]