Novel Heuristic Algorithm - Bat Algorithm Implementation

This project presents a MATLAB implementation of the novel heuristic Bat Algorithm for production optimization problems. The Bat Algorithm is a nature-inspired optimization technique based on the echolocation behavior of bats, which simulates bat flight patterns and listening mechanisms to solve complex optimization challenges. The algorithm can be effectively applied to various production optimization scenarios including resource allocation, path planning, scheduling systems, and logistics optimization. Key implementation features include frequency tuning that mimics bat sonar systems, loudness adjustment for exploration-exploitation balance, and pulse emission rate control for convergence optimization. The MATLAB code incorporates vectorized operations for efficient swarm intelligence computations, adaptive parameter updating mechanisms, and convergence monitoring functions. By implementing this innovative heuristic approach in MATLAB, we provide a powerful new tool for production optimization that demonstrates excellent robustness, fast convergence properties, and effective global search capabilities. The algorithm's bio-inspired methodology offers distinct advantages in handling multi-modal optimization landscapes commonly encountered in industrial production systems.