Monarch Butterfly Optimization Algorithm

The text introduces a swarm intelligence optimization algorithm known as the Monarch Butterfly Optimization (MBO) algorithm. MBO is a heuristic optimization algorithm inspired by the collective behavior of monarch butterflies in nature. It simulates the behavioral strategies monarch butterflies employ when searching for food sources and breeding locations, utilizing swarm intelligence to explore optimal solutions. The algorithm's distinctive feature lies in its ability to perform global search within the solution space while demonstrating strong convergence properties and search capabilities. The implementation typically involves initializing a population of monarch butterflies, defining migration operators that balance exploration and exploitation, and incorporating seasonal adjustment mechanisms to avoid local optima. Key algorithmic components include position updates based on butterfly interactions and fitness evaluations guiding the search direction. Due to these characteristics, the Monarch Butterfly Optimization algorithm shows promising application potential for solving complex optimization problems in engineering design, machine learning parameter tuning, and computational intelligence applications.