An Evolutionary Algorithm Based on a Space-Gridding Scheme for Constrained Multi-objective Optimization Problems

In order to solve the constrained multi-objective optimization problems effectively and find a set of Pareto solutions with uniform distribution as well as wide range, in this paper an evolutionary algorithm is proposed based on a space-gridding search technique. Firstly, the decision space is divided into grids and a feasible ratio is defined for each grid. The mutation operations are executed according to this ratio, which can generate as more feasible individuals as possible. In addition, the objective space is also divided into grids to find non-dominated solutions, which can reduce the computation time evidently. Xperimental results show that these technologies based on space-gridding can improve the efficiency of the algorithm.