Improved PESA algorithm based on comentropy - Kun WANG; Lin-lin WANG; Yan LIU; Yu-hua ZHANG; Meng WU (Tongxin xuebao)
Aiming at the issue that the computational effort the complexity and the running time of PESA algorithm are increasing rapidly with the growth of the solutions set number, a comentropy-based PESA algorithm (C-PESA) by merg-ing the entropy value metric into PESA algorithm was proposed. According to the distributed characteristic of the entropy value metric over the Pareto solution set, the proposed algorithm could determine whether the population has developed to the mature stage, which is reached when the number iterations is 1 300 in C-PESA. Thereby, the optimization process can be finished as soon as possible, and in a certain extent, the time complexity of PESA was simplified. Simula-tion results show that the computational effort of C-PESA increases linearly with the rising number of solutions. Mean-while, the computation time is improved almost four times, and the evolutionary computation efficiency is also enhanced.