Enhanced Multi-Objective Grey Wolf Optimization using Adaptive Diversity Tuning and Levy Flights

This study proposes an enhanced Multi-Objective Grey Wolf Optimizer (MOGWO) using adaptive population diversity tuning and levy flight theories (EMOGWO-ADTLF). It addresses the issues of parameter tunning by balancing exploration and exploitation. Using MATLAB and Python library Pymoo, the study implemented and evaluated the performance of EMOGWO-ADTLF using multi-objective test problems. The results were compared to high-performing algorithms like MOGWO, Non-Dominated Sorting Grey Wolf Optimizer (NSGWO), Dynamic Chaos MOGWO (DCMOGWO), Multi-Objective Mayfly Algorithm (MMA), Multi-Objective Antlion Algorithm (MOALO) and Multi-Objective Dragonfly Algorithm (MODA). In this work, inverted generational distance (IGD) and hypervolume (HV) were the metrics used to measure the performance of algorithms. The metrics measure the diversity, coverage, and spread of solutions. The results obtained showed the potency of EMOGWO-ADTLF in approximating the Pareto fronts. It ranks first in overall average scores in IGD and HV, with total rank scores of 17 and 18, respectively.