Learning Adaptive Evolutionary Computation for Solving Multi-Objective Optimization Problems

TL;DR

This paper introduces a novel framework that combines multi-objective evolutionary algorithms with deep reinforcement learning to adaptively control parameters, improving solution quality and reducing computation time in complex optimization problems.

Contribution

It presents a new adaptive parameter control method using DRL within MOEAs, demonstrating transferability and efficiency in real-world and benchmark problems.

Findings

Enhanced solution quality and faster convergence.

Transferability of the learned policy to complex problems.

Effective adaptive parameter tuning using DRL.

Abstract

Multi-objective evolutionary algorithms (MOEAs) are widely used to solve multi-objective optimization problems. The algorithms rely on setting appropriate parameters to find good solutions. However, this parameter tuning could be very computationally expensive in solving non-trial (combinatorial) optimization problems. This paper proposes a framework that integrates MOEAs with adaptive parameter control using Deep Reinforcement Learning (DRL). The DRL policy is trained to adaptively set the values that dictate the intensity and probability of mutation for solutions during optimization. We test the proposed approach with a simple benchmark problem and a real-world, complex warehouse design and control problem. The experimental results demonstrate the advantages of our method in terms of solution quality and computation time to reach good solutions. In addition, we show the learned policy is transferable, i.e., the policy trained on a simple benchmark problem can be directly applied to solve the complex warehouse optimization problem, effectively, without the need for retraining.