A Time-Varying Grey Wolf Optimizer

Metaheuristic algorithms are crucial for addressing real-world multiobjective optimization challenges.Among these, the Grey Wolf Optimization (GWO) algorithm has appeared as one of the most prominent in recent years, demonstrating strong performance in tackling multi-objective optimization (MOOP) tasks, particularly with respect to convergence speed and the ability to evade local minima.Despite its successes, GWO still has limitations in avoiding local minima in the case of MOOP.A primary concern that has been identified is the algorithm's use of a linear convergence factor.This has prompted extensive research efforts aimed at improving this aspect, inspiring our investigation into the problem.In this paper, we introduce a variant of GWO that incorporates a non-linear, time-varying convergence factor and a time-varying position update strategy.Our results show that this new approach outperforms existing well-known GWO variants on well-known benchmark optimization functions.