Constrained evolutionary optimization based on reinforcement learning using the objective function and constraints

Abstract

Solving constrained optimization problems (COPs) with evolutionary algorithms is highly active in the evolutionary computation community. Combining evolutionary algorithms with the learning techniques is an efficient way to obtain promising performance for the COPs. Based on this consideration, we propose a differential evolution assisted by reinforcement learning (RL), namely RL-CORCO, to effectively solve the COPs. The proposed method can be featured as (i) the Q-learning in RL is used for adaptive operator selection; (ii) the hierarchical population is set as a state to find the feasible optimal solution; and (iii) the correlation between constraints and the objective function is utilized. The RL-CORCO is tested on 18 benchmark problems in the CEC 2010 competition and 28 benchmark problems in the CEC 2017 competition. Experimental results show that in CEC2017, RL-CORCO performed better than others on 12 problems in 50 dimensions and 14 problems in 100 dimensions. The results of the Friedman’s test demonstrate the efficacy of the algorithm, which is able to obtain highly competitive results compared with other related methods.