Monarch Butterfly Optimization Algorithm

This introduces a novel optimization algorithm developed based on Professor Wang Gaige's research topic. The algorithm incorporates multiple complex functions and operations, with the core optimization function employing iterative processes to progressively refine results through successive generations. Key implementation components typically include population initialization, fitness evaluation, migration operators, and adjustment mechanisms that simulate monarch butterfly behavior patterns. This algorithm can achieve superior outcomes across various domains, such as optimizing engineering designs in industrial applications or enhancing algorithm performance in data science projects. The computational framework often involves parameters like population size, migration ratio, and adjustment rates that can be tuned for specific problem domains. Overall, this algorithm represents a promising research direction worthy of in-depth investigation and exploration, particularly for solving complex optimization problems where traditional methods may underperform.