Hybrid mating optimization algorithm based on natural mating behaviors for complex optimization problems

Abstract Swarm Intelligence (SI) has become a strong paradigm for numerical optimization, which has inspired a wide range of metaheuristic algorithms. This paper presents the Hybrid Mating Optimization (HMO), a novel bio-inspired algorithm that synergically merges four mating and communication behaviors of nature: butterfly pheromone navigation (global exploration), honeybee foraging (local exploitation), red deer dominance selection (adaptive hierarchy), and woodpecker rhythmic perturbation (diversity preservation). This hybrid mechanism is able to find a dynamic balance between exploration and exploitation, without causing premature convergence. Extensive experiments on the CEC-2017 benchmark suite show that HMO can converge faster and obtain higher accuracy than state-of-the-art algorithms such as PSO, DE, EHO, and CMA-ES. The statistical significance is further verified using Wilcoxon signed-rank tests and t-tests. HMO also has scalability both in unimodal and multimodal environments. Furthermore, a real-world case study of an engineering problem on pressure vessel design validates the effectiveness of HMO in constrained optimization problems.