Multi-Objective Optimization by a New Dynamical Evolutionary Algorithm Based on the Information Entropy
Ding Wei, Ting Hu, Zhang Huanguo · 2006
In this paper, a new dynamical multi-objective evolutionary algorithm based on the information entropy is proposed inspired by the principle of minimal free energy from the statistical mechanics. Developed to solving multi-objective optimization problems, the maintenance of the diversity of the population is essentially considered in this new algorithm by using the information entropy. The numerical results show its good performance at two important factors, the number of alternative solution points and their distributions. It also gives us confidence for the further research on dynamical evolutionary algorithm.