Achieving Cost Efficiency and Load Sharing in Power Systems: The Superiority of Adaptive BAT Algorithm

This research paper presents a novel approach to solving optimization problems using the BAT Algorithm with parameter tuning, local mechanism escaping, and adaptive control. The proposed algorithm is evaluated using the IEEE 30-bus system as a test system. The BAT Algorithm is a nature-inspired metaheuristic algorithm that mimics the echolocation behavior of bats. By incorporating parameter tuning, the algorithm's performance is enhanced by fine-tuning the algorithm's key parameters, such as loudness and pulse rate. The local mechanism escaping mechanism allows the algorithm to explore new regions of the search space, promoting diversification and avoiding local optima. The algorithm's performance is evaluated in terms of solution quality, convergence rate, and robustness. Comparative analysis is conducted by comparing the proposed algorithm with other well-known optimization algorithms.The superior performance of the proposed BAT algorithm with parameter tuning, local mechanism escaping, and adaptive control in results. The algorithm exhibits strong convergence and exploration abilities, leading to high-quality solutions for the optimization problem. Furthermore, the algorithm demonstrates its effectiveness in solving the IEEE 30-bus system optimization problem, showcasing its potential for real-world applications in power systems and related domains. The findings demonstrate the algorithm's capability to outperform other state-of-the-art optimization algorithms, including DE, ADE, Cuckoo Search, EADE, TLBO, among others.