A New Model Solving Multi-Objective Optimization Problems Based on the Evolutionary Algorithm

A novel evolutionary algorithm based on the new model for multiobjective optimization problems (MOPs) is presented in this paper. Firstly, we defined two measures, one is the rank variance of population and the other is the U - measure variance of population. The rank variance of population is a measure of the quality of solutions and the U -measure variance of population is a measure of the uniformity of the distribution of solutions. Using these two measures as two objective functions, the MOPs is finally converted into a two objective optimization problem. For the transformed problem, a novel multiobjective evolutionary algorithm is proposed. The new approach is tested on two well-known benchmark multiobjective optimization functions taken from the standard literature. Compared with other eight state- of-the-art algorithms, our algorithm remarkably outperforms them in terms of the quality of the solutions and the uniformly distribution of the solutions. So our algorithm can be considered a viable alternative to solve MOPs.