Constrained Multi-objective Optimization with Deep Reinforcement Learning Assisted Operator Selection

TL;DR

This paper introduces a deep reinforcement learning framework to adaptively select operators in constrained multi-objective evolutionary algorithms, significantly enhancing their performance and versatility across benchmark problems.

Contribution

It proposes a novel online operator selection method using deep reinforcement learning to improve constrained multi-objective evolutionary algorithms.

Findings

Significant performance improvements on 42 benchmark problems.

Enhanced algorithm versatility compared to existing methods.

Effective adaptive operator selection demonstrated across multiple algorithms.

Abstract

Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention. Various constrained multi-objective optimization evolutionary algorithms (CMOEAs) have been developed with the use of different algorithmic strategies, evolutionary operators, and constraint-handling techniques. The performance of CMOEAs may be heavily dependent on the operators used, however, it is usually difficult to select suitable operators for the problem at hand. Hence, improving operator selection is promising and necessary for CMOEAs. This work proposes an online operator selection framework assisted by Deep Reinforcement Learning. The dynamics of the population, including convergence, diversity, and feasibility, are regarded as the state; the candidate operators are considered as actions; and the improvement of the population state is treated as the reward. By using a Q-Network to learn a policy to estimate the Q-values of all actions, the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance. The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems. The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.