Human-Whale cooperation optimization (HWO) algorithm: A metaheuristic algorithm for solve optimization problems

Metaheuristic algorithms are one of the most effective methods for solving optimization problems and are modeled on the behavior of living things or biological phenomena. The swarm behavior of animals in nature to survive is a good way to create metaheuristic algorithms with a group intelligence approach. The swarm hunting mechanism is one of the most interesting meta-behavioral behaviors observed in a large number of organisms, and the chances of success in prey hunting by swarm behaviors will increase. In this paper, a new metaheuristic algorithm with a swarm intelligence approach is presented by using the human hunting mechanism and whale. In this type of behavior, whales and humans participate in hunting in such a way that whales and humans benefit from each other. Implementation and analysis of the proposed method provided less error than ۸۲.۶۰% of the experiments of other algorithms such as particle swarm optimization(PSO), firefly algorithm(FA), grasshopper optimization algorithm(GOA), and butterfly optimization algorithm(BOA). Experiments show that the proposed method converges in complex functions with a probability of ۴.۳۶% in local optimizations, which is less than the comparable algorithms. Experiments show that the proposed method can be implemented on a wide range of functional optimization problems and reduces the optimization error due to the simultaneous local and global search of the intelligent algorithm.

Keywords:

Optimization problems , Benchmark function , Metaheuristic algorithms , Swarm intelligence algorithms , Human-Whale Cooperation Optimization