Hybrid Grey Wolf Algorithm Development and Analysis for Effective Multi Objective Optimizat

Abstract

This thesis presents an innovative approach to multi-objective optimization in industrial processes by hybridizing the Grey Wolf Optimizer (GWO) algorithm with Grey Relational Analysis (GRA). The study aims to enhance GWO's capability in handling complex, multi faceted optimization problems. Using MATLAB software, the hybrid algorithm's effectiveness is evaluated against the standard GWO. The findings demonstrate improved efficiency and accuracy in optimization tasks, highlighting the hybrid algorithm's potential in reducing error margins and increasing convergence rates. This work's novelty lies in the unique integration of GWO with GRA, contributing significantly to optimization algorithms' theoretical understanding and practical applications. While promising, the study recognizes limitations, including its focus on specific scenarios, suggesting further scalability research and empirical validation. The enhanced GWO algorithm offers substantial practical implications for industries reliant on optimization, promising improved decision-making and process efficiency, thereby fostering continuous improvement and quality enhancement in various industrial applications.