Abstract:

In the light of the genetic algorithm’s “precocity” problem appearing in the optimization process and multipeak function solution and the slow convergence speed in the immune algorithm, this paper proposes a new algorithm—distributed immune evolutionary algorithm (DIEA) through combining the modified distributed model of an evolutionary algorithm with the advantages of the existing evolutionary algorithm and the immune algorithm. This algorithm includes one master module which is a major global search to find the global optimal solution and several slave modules which are to find all intervals of the local optimal solution. Experimental results show that DIEA is effective in achieving multiple solutions of the multimodal optimization problems.