Solving Dynamic Multi-objective Optimization Problems Using Incremental Support Vector Machine

TL;DR

This paper introduces an incremental support vector machine approach to enhance dynamic multi-objective optimization by predicting solutions based on past Pareto optimal sets, improving initial populations for various algorithms.

Contribution

It proposes a novel method using ISVM to predict solutions in DMOPs, integrated with multiple evolutionary algorithms, demonstrating improved performance.

Findings

ISVM effectively predicts solutions for DMOPs

Improved initial populations lead to better optimization results

Method enhances multiple population-based algorithms

Abstract

The main feature of the Dynamic Multi-objective Optimization Problems (DMOPs) is that optimization objective functions will change with times or environments. One of the promising approaches for solving the DMOPs is reusing the obtained Pareto optimal set (POS) to train prediction models via machine learning approaches. In this paper, we train an Incremental Support Vector Machine (ISVM) classifier with the past POS, and then the solutions of the DMOP we want to solve at the next moment are filtered through the trained ISVM classifier. A high-quality initial population will be generated by the ISVM classifier, and a variety of different types of population-based dynamic multi-objective optimization algorithms can benefit from the population. To verify this idea, we incorporate the proposed approach into three evolutionary algorithms, the multi-objective particle swarm optimization(MOPSO), Nondominated Sorting Genetic Algorithm II (NSGA-II), and the Regularity Model-based multi-objective estimation of distribution algorithm(RE-MEDA). We employ experiments to test these algorithms, and experimental results show the effectiveness.