Breakthrough Platypus Optimization Algorithm

This paper proposes a novel swarm intelligence optimization algorithm, the Breakthrough Platypus Optimization Algorithm (B-POA). This algorithm draws inspiration from the unique biological behaviors of the platypus, including its electrosensory feeding, oviparous nature, and bimodal activity. By incorporating innovative mechanisms such as dynamic adjustment of electric field sensing weights, a randomized oviparous burst mechanism, bimodal search in both water and land, and electric field-sensing guided perturbations, B-POA achieves an adaptive balance between global search and local exploitation. Theoretically, this algorithm can significantly improve search efficiency, convergence speed, and diversity maintenance. This paper constructs a detailed mathematical model of B-POA, presents pseudocode, and analyzes its groundbreaking features, providing a theoretical foundation for subsequent applications.