A New Metaheuristic Bat-Inspired Algorithm

TL;DR

This paper introduces a novel metaheuristic algorithm inspired by bat echolocation, aiming to improve optimization performance by combining advantages of existing methods and demonstrating superior results in simulations.

Contribution

The paper proposes the Bat Algorithm, a new metaheuristic inspired by bats, integrating strengths of existing algorithms and showing improved performance in optimization tasks.

Findings

Bat Algorithm outperforms genetic algorithms and particle swarm optimization in simulations

The proposed method effectively balances exploration and exploitation

Further studies on the algorithm's capabilities are discussed

Abstract

Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.