Memory-Enhanced Dynamic Multi-Objective Evolutionary Algorithm Based on Lp Decomposition

Xinxin Xu,Yanyan Tan,Shengtao Li,Wei Zheng

doi:10.3390/app8091673

Xinxin Xu, Yanyan Tan + Show 2 more

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https://doi.org/10.3390/app8091673

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Abstract

Decomposition-based multi-objective evolutionary algorithms provide a good framework for static multi-objective optimization. Nevertheless, there are few studies on their use in dynamic optimization. To solve dynamic multi-objective optimization problems, this paper integrates the framework into dynamic multi-objective optimization and proposes a memory-enhanced dynamic multi-objective evolutionary algorithm based on L p decomposition (denoted by dMOEA/D- L p ). Specifically, dMOEA/D- L p decomposes a dynamic multi-objective optimization problem into a number of dynamic scalar optimization subproblems and coevolves them simultaneously, where the L p decomposition method is adopted for decomposition. Meanwhile, a subproblem-based bunchy memory scheme that stores good solutions from old environments and reuses them as necessary is designed to respond to environmental change. Experimental results verify the effectiveness of the L p decomposition method in dynamic multi-objective optimization. Moreover, the proposed dMOEA/D- L p achieves better performance than other popular memory-enhanced dynamic multi-objective optimization algorithms.

Highlights

In the real world, many problems can be described as multi-objective optimization problems (MOPs), where multiple objectives often conflict with each other
A memory-enhanced dynamic multi-objective evolutionary algorithm based on L p decomposition is proposed in this paper
To test the effectiveness of the L p decomposition method, dMOEA/D-L p was compared with dMOEA/D-weighted sum approach (WS), dMOEA/D-Tchebycheff approach (TCH), and dMOEA/D-penalty-based boundary intersection approach (PBI) on the FDA series problems

Summary

Introduction

Many problems can be described as multi-objective optimization problems (MOPs), where multiple objectives often conflict with each other. Evolutionary multi-objective optimization, referred to EMO, focuses on using evolutionary algorithms for MOPs [2]. MOPs can be further divided into static and dynamic multi-objective problems (DMOPs). The objective function, constraint function, and the related parameters of DMOPs can change with time [3]. A dynamic evolutionary multi-objective optimization algorithm (DEMOA) must be able to automatically detect and respond to new changes with fast convergence, and track the time-varying POS in a timely fashion. In the past ten years, some researchers have had strong interest in dynamic EMO (DEMO) [5,6]. Artificial immune algorithms [7,8], particle swarm optimization algorithms [9,10], co-evolutionary algorithms [11], membrane computing [12,13], and other natural computing methods, they designed the corresponding

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