A new metaheuristic algorithm called the Bat Algorithm (BA) is introduced, inspired by the echolocation behavior of bats. This algorithm combines the advantages of existing metaheuristics such as particle swarm optimization and genetic algorithms. The BA is designed to solve complex optimization problems by simulating the echolocation behavior of bats, which involves emitting sound pulses and detecting echoes to locate prey and navigate in the dark. The algorithm uses parameters such as frequency, loudness, and pulse emission rate to adjust the search process. The BA is compared with other algorithms, including genetic algorithms and particle swarm optimization, on various benchmark functions. Simulation results show that the BA performs significantly better in terms of accuracy and efficiency. The algorithm is implemented using a pseudo-code framework, and its performance is validated through numerical experiments on standard test functions. The BA is found to be a promising approach for continuous constrained optimization problems, combining the strengths of existing algorithms with the unique features of bat echolocation. Further research is suggested to explore the algorithm's potential in various engineering and industrial optimization applications.A new metaheuristic algorithm called the Bat Algorithm (BA) is introduced, inspired by the echolocation behavior of bats. This algorithm combines the advantages of existing metaheuristics such as particle swarm optimization and genetic algorithms. The BA is designed to solve complex optimization problems by simulating the echolocation behavior of bats, which involves emitting sound pulses and detecting echoes to locate prey and navigate in the dark. The algorithm uses parameters such as frequency, loudness, and pulse emission rate to adjust the search process. The BA is compared with other algorithms, including genetic algorithms and particle swarm optimization, on various benchmark functions. Simulation results show that the BA performs significantly better in terms of accuracy and efficiency. The algorithm is implemented using a pseudo-code framework, and its performance is validated through numerical experiments on standard test functions. The BA is found to be a promising approach for continuous constrained optimization problems, combining the strengths of existing algorithms with the unique features of bat echolocation. Further research is suggested to explore the algorithm's potential in various engineering and industrial optimization applications.