Domesticated animal-inspired metaheuristic algorithms for static and dynamic optimization problems

Metaheuristic optimization algorithms inspired by animal behaviors have predominantly focused on wild and predatory species, while the structured and cooperative behaviors of domesticated animals remain largely unexplored. Motivated by this gap, this paper proposes a unified family of four domesticated animal–inspired metaheuristic algorithms: Canine–Human Trust Optimization (CHTO), Feline Curiosity Optimization (FCO), Equine–Human Harmony Optimization (EHHO), and Lagomorph Bond Optimization (LBO), where lagomorph refers to rabbit-inspired bonding behavior. Each algorithm models companionship-oriented traits such as trust, curiosity, harmony, and social bonding to achieve an effective balance between exploration and exploitation. The proposed algorithms are evaluated on standard unimodal and multimodal benchmark functions and dynamic optimization problems using the Generalized Moving Peaks Benchmark (GMPB). Experimental results show that EHHO consistently outperforms classical and recent metaheuristics, including Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO), Slime Mould Algorithm (SMA), and Aquila Optimizer (AO). On highly multimodal benchmark functions such as Rastrigin (f₃), Griewank (f₄), and Levy (f₇), EHHO achieves up to 35% improvement in mean fitness values compared to Harris Hawks Optimization (HHO), which is identified as the strongest competing baseline algorithm. In dynamic optimization scenarios using the GMPB, EHHO reduces the average offline error by approximately 28% compared to Harris Hawks Optimization (HHO), while achieving a success rate of 92% across changing environments. Statistical validation using Friedman and Wilcoxon signed-rank tests confirms the robustness and statistical significance of the proposed algorithms. The findings establish domesticated animal companionship behaviors as a competitive and effective paradigm for solving both static and dynamic optimization problems.