Abstract：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 20~1000 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.
[1] Kennedy J, Eberhart R C. Particle Swarm Optimization // Proc of the IEEE International Conference on Neural Networks. Perth, Australia, 1995, Ⅳ: 1942-1948 [2] Shi Yuhui, Eberhart R C. Fuzzy Adaptive Particle Swarm Optimization // Proc of the IEEE Congress on Evolutionary Computation. Seoul, Korea, 2001: 101-106 [3] van den Bergh F, Engelbrecht A P. A Cooperative Approach to Particle Swarm Optimization. IEEE Trans on Evolutionary Computation, 2004, 8(3): 225-239 [4] Kennedy J, Mendes R. Population Structure and Particle Swarm Performance // Proc of the IEEE Congress on Evolutionary Computation. Honolulu, USA, 2002: 1671-1676 [5] Huang Yanxin, Zhou Chunguang, Zou Shuxue, et al. A Hybrid Algorithm on Class Cover Problems. Journal of Software, 2005, 16(4): 513-522 (in Chinese) (黄艳新,周春光,邹淑雪,等.一种求解类覆盖问题的混和算法.软件学报, 2005, 16(4): 513-522) [6] Xie X F, Zhang W J, Yang Z L. A Dissipative Particle Swarm Optimization // Proc of the IEEE Congress on Evolutionary Computation. Honolulu, USA, 2002: 1456-1461 [7] Higashi N, Iba H. Particle Swarm Optimization with Gaussian Mutation // Proc of the IEEE Swarm Intelligence Symposium. Indianapolis, Indiana, 2003: 72-79 [8] Kennedy J. Bare Bones Particle Swarms // Proc of the IEEE Swarm Intelligence Symposium. Indianapolis, Indiana, 2003: 80-87 [9] Ratnaweera A, Halgamuge S K, Waston H C. Self-Organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients. IEEE Trans on Evolutionary Computation, 2004, 8(3): 240-255 [10] Yao Xin, Liu Yong, Lin Guangming. Evolutionary Programming Made Faster. IEEE Trans on Evolutionary Computation, 1999, 3(2): 82-102 [11] Leung Y W, Wang Y. An Orthogonal Genetic Algorithm with Quantization for Global Numerical Optimization. IEEE Trans on Evolutionary Computation, 2001, 5(1): 41-53 [12] Pan Zhengjun, Kang Lishan. An Adaptive Evolutionary Algorithm for Numerical Optimization // Yao X, Kim J H, Furuhashi T, eds. Lecture Notes in Computer Science. Heidelberg, Germany: Springer-Verlag, 1997, 1285: 27-34 [13] Mühlenbein H, Schlierkamp-Voosen D. Predictive Models for the Breeder Genetic Algorithm. Evolutionary Computation, 1993, 1(1): 25-49 [14] Du Haifeng, Gong Maoguo, Jiao Licheng, et al. Immune Memory Clonal Programming Algorithm on High-Dimension Numerical Optimization. Progress in Natural Science, 2004, 14(8): 925-933 (in Chinese) (杜海峰,公茂果,焦李成,等.用于高维函数优化的免疫记忆克隆规划算法.自然科学进展, 2004, 14(8): 925-933)
2007, Vol. 20 
