Dynamic Multi Objective Particle Swarm Optimization based on a New Environment Change Detection Strategy
This work addresses dynamic optimization challenges for real-world applications, but appears incremental as it builds on existing PSO methods with a new detection strategy.
The paper tackles dynamic multi-objective optimization problems by proposing a new particle swarm optimization method with an environment change detection strategy, achieving a balance between exploration and exploitation in dynamic spaces.
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.