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A new metaheuristic football game inspired algorithm

Metaheuristics are high level strategies for exploring the search space by using different methods to solve global optimization problems. In this paper, Football Game Algorithm has been proposed as a new metaheuristic algorithm based on the simulation of football players' behavior during a game for finding best positions to score a goal under supervision of the team coach. Simulation of humans' intelligences who are working together as a team to reach a specific goal instead of simulating the intelligence of various animal swarms in the nature is the most important distinction of the proposed algorithm to other existing algorithms that also introduces a new approach for making balance between diversification and intensification. Football Game Algorithm is a nature inspired, population base algorithm with ability in finding multiple global optimums. We have studied general football game tactics and idealized its characteristics to formulate Football Game Algorithm. We have then compared the proposed algorithm with other metaheuristics, including standard and modified particle swarm optimization and bat algorithm. The result of comparison studies show that the proposed Algorithm outperforms other algorithms and also has more robust performance. Finally, we have discussed and concluded by pointing out special attributes of the Football Game Algorithm.