House Mouse Optimization Algorithm

This paper proposes a novel swarm intelligence optimization algorithm—the House Mouse Optimization Algorithm (HMOA)—inspired by the foraging, predator avoidance, and social behaviors of house mice in their natural environment. Traditional optimization algorithms are prone to getting trapped in local optima or exhibiting slow convergence when dealing with high-dimensional complex functions. HMOA, by introducing an adaptive energy dynamic model, path memory mechanism, hierarchical search strategy, and emergency escape mechanism, effectively balances exploration and exploitation capabilities, improving global search performance. This paper provides a detailed explanation of the algorithm's mathematical model and presents the complete update formulas and process. Theoretical analysis demonstrates that HMOA exhibits good convergence and robustness in multi-objective optimization and complex constraint problems.