Feral Horse Optimization Algorithm: A Novel Metaheuristic Algorithmfor Global Optimization and Constrained Engineering Design Problems

Given the characteristics of individual cooperation and randomness, metaheuristic algorithms show excellent global search ability, which have increasingly become mainstream tools for solving complex engineering design problems. To further enrich the solution approaches, this work reported a novel metaheuristic algorithm called the feral horse optimization algorithm (FHOA) inspired by the social behaviors of the group of feral horses. In a group of feral horses, the stallion and the dominate mare are two most influential members. FHOA is designed based on the simulation for “expulsion behavior” of the stallion and “follow behavior” of the dominate mare. “Expulsion behavior” means the stallion expels the adult male colts; “follow behavior” denotes that the other horses follow the dominate mare to avoid external threats. A notable feature of FHOA is that it only needs the population size and terminal condition for optimization. The performance of FHOA is investigated by 70 challenging numerical functions and three classical constrained engineering design problems. Experimental results demonstrate that FHOA has excellent global search ability and is more suitable for solving complex problems with multimodal properties than the compared algorithms.