Dynamic Multi Objective Particle Swarm Optimization based on a New Environment Change Detection Strategy

TL;DR

This paper presents a novel dynamic multi-objective particle swarm optimization method that detects environment changes to adapt solutions effectively in dynamic optimization problems.

Contribution

It introduces a new environment change detection strategy within a multi-objective PSO framework, enhancing adaptability in dynamic optimization scenarios.

Findings

Effective detection of environment changes in dynamic problems

Balanced exploration and exploitation in dynamic search space

Proven performance on benchmark functions

Abstract

The dynamic of real-world optimization problems raises new challenges to the traditional particle swarm optimization (PSO). Responding to these challenges, the dynamic optimization has received considerable attention over the past decade. This paper introduces a new dynamic multi-objective optimization based particle swarm optimization (Dynamic-MOPSO).The main idea of this paper is to solve such dynamic problem based on a new environment change detection strategy using the advantage of the particle swarm optimization. In this way, our approach has been developed not just to obtain the optimal solution, but also to have a capability to detect the environment changes. Thereby, DynamicMOPSO ensures the balance between the exploration and the exploitation in dynamic research space. Our approach is tested through the most popularized dynamic benchmark's functions to evaluate its performance as a good method.