关键词: 混洗蛙跳算法, 免疫进化算法, 粒子群算法, 函数测试

Abstract: A new shuffled frog leaping algorithm based on immune evolutionary particle swarm optimization was proposed in order to avoid premature convergence and to improve the precision of solution by using basic Shuffled Frog Leaping Algorithm (SFLA). The proposed algorithm integrated the global searching idea in the Particle Swarm Optimization (PSO) into SFLA, to pursue the information of two optimal solutions in the sub-swarm and the whole-swarm simultaneously, so as to search thoroughly near by the space gap of the worst solution, and also integrated the immune evolutionary algorithm into SFLA making immune evolutionary iterative computation to the optimal solution in the whole-swarm, so as to use the information of optimal solution fully. This algorithm can not only get free from trapping into local optimum and be close to the global optimal solution with higher precision, but also speeds up the convergence. Calculation results show that the Immune Evolutionary Particle Swarm Optimization-Shuffled Frog Leaping Algorithm (IEPSO-SFLA) has better optimal searching ability and stability as well as faster convergence than those of basic SFLA.

Key words: Shuffled Frog Leaping Algorithm(SFLA), immune evolutionary algorithm, Particle Swarm Optimization （PSO）, function test