Heterogeneous-species Win-win Cooperation Optimizer

Heterogeneous-species Win-win Cooperation Optimizer (HWCO) for engineering optimization problems and path planning problems

A novel metaheuristic called Heterogeneous-species Win-win Cooperation Optimizer (HWCO) is proposed. Different from previous swarm intelligence optimizers inspired by cooperative predation within a single population, the proposed optimizer simulates four behaviors of cooperative predation between octopus-fish hunting clusters: (ⅰ) goatfish searching for prey; (ⅱ) Different outcomes caused by different decisions made by the octopus; (ⅲ) An octopus drives away an inactive fish; (ⅳ) The prey hides in a safer place. The performance of HWCO was comprehensively benchmarked on multiple dimensions using the CEC2017 benchmark test functions and compared with ten algorithms. The results show that HWCO can always provide competitive solutions. Meanwhile, HWCO was applied to six engineering optimization problems and path planning problems. The optimization results indicate that HWCO has superiority in solving a series of challenging real-world problems.