"Megalodon-Inspired Metaheuristic Algorithm (MIMA): A Novel Bio-Inspired Optimization Framework for Superior Speed, Accuracy, and Computational Efficiency"

This paper presents the Megalodon-Inspired Metaheuristic Algorithm (MIMA), a pioneering optimization technique inspired by the predatory behavior of the extinct Megalodon shark. MIMA integrates a "Predatory Pursuit" mechanism for rapid global exploration with an "Adaptive Prey Detection" strategy for precise local exploitation, achieving exceptional convergence speed, solution accuracy, and computational efficiency. Implemented in Python ۳.۹, MIMA was evaluated on CEC ۲۰۱۷ benchmark functions and a practical pressure vessel design problem. Simulations were executed on an Intel Core i۷-۱۲۷۰۰H processor with ۳۲ GB RAM, leveraging NumPy ۱.۲۱ and Matplotlib ۳.۵ for computations and visualizations. Comparative analyses against Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA) reveal MIMA’s superiority: ۲۵% faster convergence, ۳۰% lower computational cost, and statistically significant improvements (Wilcoxon p < ۰.۰۵) over ۳۰ runs. Detailed results, supported by convergence curves, boxplots, and comparison tables, demonstrate MIMA’s robustness and scalability. Its energy-efficient design minimizes redundant evaluations, making it suitable for resource-constrained applications. This study offers a reproducible framework with open-source code, positioning MIMA as a transformative tool for optimization in engineering, machine learning, and operational research.This paper presents the Megalodon-Inspired Metaheuristic Algorithm (MIMA), a pioneering optimization technique inspired by the predatory behavior of the extinct Megalodon shark. MIMA integrates a "Predatory Pursuit" mechanism for rapid global exploration with an "Adaptive Prey Detection" strategy for precise local exploitation, achieving exceptional convergence speed, solution accuracy, and computational efficiency. Implemented in Python ۳.۹, MIMA was evaluated on CEC ۲۰۱۷ benchmark functions and a practical pressure vessel design problem. Simulations were executed on an Intel Core i۷-۱۲۷۰۰H processor with ۳۲ GB RAM, leveraging NumPy ۱.۲۱ and Matplotlib ۳.۵ for computations and visualizations. Comparative analyses against Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA) reveal MIMA’s superiority: ۲۵% faster convergence, ۳۰% lower computational cost, and statistically significant improvements (Wilcoxon p < ۰.۰۵) over ۳۰ runs. Detailed results, supported by convergence curves, boxplots, and comparison tables, demonstrate MIMA’s robustness and scalability. Its energy-efficient design minimizes redundant evaluations, making it suitable for resource-constrained applications. This study offers a reproducible framework with open-source code, positioning MIMA as a transformative tool for optimization in engineering, machine learning, and operational research.

کلیدواژه ها:

Bio-Inspired Optimization ، Megalodon Shark ، Metaheuristic Algorithm ، Computational Efficiency ، Pressure Vessel Design ، Python Simulation