Bee-inspired metaheuristics for global optimization: a performance comparison

Metaheuristics are widely applied to solve optimization problems. Numerous metaheuristic algorithms inspired by natural processes have been introduced in the past years. Studying and comparing the convergence of metaheuristics is helpful in future algorithmic development and applications. This study focuses on bee-inspired metaheuristics and identifies seven basic or root algorithms applied to solve continuous optimization problems. They are the bee system, mating bee optimization (MBO), bee colony optimization, bee evolution for genetic algorithms (BEGA), bee algorithm, artificial bee colony (ABC), and bee swarm optimization. The algorithms' performances are evaluated with several benchmark problems. This study's results rank the cited algorithms according to their convergence efficiency. The strengths and shortcomings of each algorithm are discussed. The ABC, BEGA, and MBO are the most efficient algorithms. This study's results show the convergence rate among different algorithms varies, and evaluates the causes of such variation.