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.
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2007, Vol. 20 
