A Universal Framework for Large-Scale Multi-Objective Optimization Based on Particle Drift and Diffusion

TL;DR

This paper introduces a physics-inspired universal framework for large-scale multi-objective optimization that improves convergence, diversity, and efficiency by simulating particle movement through drift and diffusion across different optimization stages.

Contribution

The paper presents a novel three-stage particle drift-diffusion framework that enhances existing evolutionary algorithms for large-scale multi-objective problems.

Findings

Significantly improves convergence and diversity in large-scale MOEAs.

Enhances computational efficiency in solving high-dimensional problems.

Effective in practical neural network training scenarios.

Abstract

Large-scale multi-objective optimization poses challenges to existing evolutionary algorithms in maintaining the performances of convergence and diversity because of high dimensional decision variables. Inspired by the motion of particles in physics, we propose a universal framework for large-scale multi-objective optimization based on particle drift and diffusion to solve these challenges in this paper. This framework innovatively divides the optimization process into three sub-stages: two coarse-tuning sub-stages and one fine-tuning sub-stage. Different strategies of drift-diffusion operations are performed on the guiding solutions according to the current sub-stage, ingeniously simulating the movement of particles under diverse environmental conditions. Finally, representative evolutionary algorithms are embedded into the proposed framework, and their effectiveness are evaluated through comparative experiments on various large-scale multi-objective problems with 1000 to 5000 decision variables. Moreover, comparative algorithms are conducted on neural network training problems to validate the effectiveness of the proposed framework in the practical problems. The experimental results demonstrate that the framework proposed in this paper significantly enhances the performance of convergence and diversity of MOEAs, and improves the computational efficiency of algorithms in solving large-scale multi-objective optimization problems.