Hunter–prey optimization: algorithm and applications

This paper proposes a new population-based optimization algorithm called hunter–prey optimizer (HPO). This algorithm is inspired by the behavior of predator animals such as lions, leopards and wolves, and preys such as stag and gazelle. There are many scenarios of animal hunting behavior, and some of them have transformed into optimization algorithms. The scenario used in this paper is different from the scenario of the previous algorithms. In the proposed approach, a prey and predator population, and a predator attacks a prey that moves away from the prey population. The hunter adjusts his position toward this far prey, and the prey adjusts his position toward a safe place. The search agent’s position that was the best value of the fitness function considered a safe place. The HPO algorithm implemented on several test functions to evaluate its performance. Also, to performance verification, the proposed algorithm is applied to several engineering problems. The results showed that the proposed algorithm performed effective in solving test functions and engineering problems.