Comparing the Performance of the Wolf Algorithm with Three other Meta-Heuristic Algorithms (Bees, Biogeography-Based, Chicken Swarm)
Keywords:
Grey wolf optimization algorithm, Bees algorithm, Biogeography-based optimization algorithm, Chicken swarm optimization algorithm, Criteria functionsAbstract
Nowadays, meta-heuristic algorithms have made significant contributions to achieving approximate solutions to optimization problems. It is important to choose a suitable algorithm for each problem, as an algorithm can be appropriate for one type of problem and, at the same time, inappropriate for another one. In this paper, an attempt has been made to compare the Grey Wolf Optimization (GWO) algorithm with 3 modern optimization algorithms (bees algorithm, Biogeography-Based Optimization (BBO) algorithm and Chicken Swarm Optimization (CSO) algorithm). By utilizing 9 criteria functions, the performances of these algorithms in terms of reaching the global optimal point and also the time of reaching have been investigated. In order to make the correct comparison, the selected algorithms are all among the ones which are derived from the foraging behaviors of living organisms.
References
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