Mouse Optimization Algorithm

Swarm intelligence optimization algorithms, as a global optimization method based on the simulation of biological swarm behavior, have wide applications in complex function optimization, path planning, combinatorial optimization, and engineering optimization. This paper proposes a novel swarm intelligence optimization algorithm—the Mouse Optimization Algorithm (MOA), inspired by the behavioral characteristics of mice searching for food in their natural environment, including olfactory search, exploration-utilization balance, swarm information sharing, and dynamic behavioral strategies. MOA abstracts the search behavior of a mouse swarm into a solution method for a continuous optimization problem through mathematical modeling and plain text formulas. The algorithm design incorporates multiple modules such as local search, global exploration, multi-neighbor information sharing, random jumping, and fitness pressure mechanisms, achieving an organic combination of intelligence, global search capability, and adaptive search strategies. This paper provides a detailed analysis of the core mechanisms of the MOA algorithm and describes its update rules and dynamic strategies using mathematical formulas, providing a basic framework for further theoretical research and engineering applications.