An angle based constrained many-objective evolutionary algorithm

Abstract

Having successfully handled many-objective optimization problems with box constraints only by using VaEA, a vector angle based many-objective evolutionary algorithm in our precursor study, this paper extended VaEA to solve generic constrained many-objective optimization problems. The proposed algorithm (denoted by CVaEA) differs from the original one mainly in the mating selection and the environmental selection, which are made suitable in the presence of infeasible solutions. Furthermore, we suggest a set of new constrained many-objective test problems which have different ranges of function values for all the objectives. Compared with normalized problems, this set of scaled ones is more applicable to test an algorithm’s performance. This is due to the nature property of practical problems being usually far from normalization. The proposed CVaEA was compared with two latest constrained many-objective optimization methods on the proposed test problems with up to 15 objectives, and on a constrained engineering problem from practice. It was shown by the simulation results that CVaEA could find a set of well converged and properly distributed solutions, and, compared with its competitors, obtained a better balance between convergence and diversity. This, and the original VaEA paper, together demonstrate the usefulness and efficiency of vector angle based algorithms for handling both constrained and unconstrained many-objective optimization problems.