基于免疫进化的粒子群混洗蛙跳算法

为了避免混洗蛙跳算法易于出现不成熟收敛，提高求解质量，提出了基于免疫进化的粒子群混洗蛙跳算法。该算法将粒子群算法中粒子追踪全局极值的思想融入混洗蛙跳算法中，对族群内的最差个体同时跟踪族群内和全局两个最优个体的信息，进行深度搜索；并引入免疫进化算法对群体中的最优个体进行免疫进化迭代计算，以达到充分利用最优个体的信息的目的。该算法不仅避免了陷入局部极值的局限，以更高的精度逼近全局最优解，而且能加速收敛。对多个典型测试函数的计算表明：基于免疫进化的粒子群混洗蛙跳算法比传统的混洗蛙跳算法具有更好的寻优能力、稳定效果和更快的收敛速度。

Shuffled frog leaping algorithm based on immune evolutionary particle swarm optimization

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.