Solving dynamic multi-objective optimization problems via support vector machine

TL;DR

This paper introduces SVM-DMOEA, a novel approach that uses support vector machines to predict and quickly find the Pareto optimal set in dynamic multi-objective optimization problems by leveraging past solutions.

Contribution

The paper presents a new SVM-based method for dynamic multi-objective optimization that improves the speed and accuracy of finding Pareto optimal sets over time.

Findings

SVM-DMOEA effectively predicts the Pareto optimal set in dynamic environments.

The approach accelerates convergence compared to traditional methods.

Experimental results validate the method's effectiveness.

Abstract

Dynamic Multi-objective Optimization Problems (DMOPs) refer to optimization problems that objective functions will change with time. Solving DMOPs implies that the Pareto Optimal Set (POS) at different moments can be accurately found, and this is a very difficult job due to the dynamics of the optimization problems. The POS that have been obtained in the past can help us to find the POS of the next time more quickly and accurately. Therefore, in this paper we present a Support Vector Machine (SVM) based Dynamic Multi-Objective Evolutionary optimization Algorithm, called SVM-DMOEA. The algorithm uses the POS that has been obtained to train a SVM and then take the trained SVM to classify the solutions of the dynamic optimization problem at the next moment, and thus it is able to generate an initial population which consists of different individuals recognized by the trained SVM. The initial populuation can be fed into any population based optimization algorithm, e.g., the Nondominated Sorting Genetic Algorithm II (NSGA-II), to get the POS at that moment. The experimental results show the validity of our proposed approach.