An Improved Multi-Objective Particle Swarm Optimization Algorithm Based on Adaptive Local Search

Introduction

Optimization is a methodology of making something perfect or more effective. Multi-objective optimization generally deals with the conflicting nature of the objectives to be achieved. Thus, researchers are finding new ways to tackle these conflicts and to obtain improved trade-off solutions in fewer iterations. The main aim of multi-objective optimization algorithms is to improve the Pareto- optimal solution convergence to the true Pareto front and simultaneously maintain good diversity (Sierra & Coello, 2006). Convergence is the closeness of the obtained Pareto-optimal front to the True Pareto Front and diversity signifies the uniform distribution of solutions over the True Pareto Front (Baviskar & Krishnapillai, 2016).