Bat Algorithm: A Novel Approach for Global Engineering Optimization

TL;DR

The paper introduces the bat algorithm, a new nature-inspired metaheuristic based on echolocation, demonstrating its effectiveness in solving complex engineering optimization problems better than existing methods.

Contribution

It presents a novel optimization algorithm inspired by bats' echolocation, with detailed formulation, implementation, and validation on multiple engineering problems.

Findings

BA outperforms existing algorithms on benchmark problems.

Optimal solutions obtained are better than previous methods.

The algorithm's search features are analyzed for future research.

Abstract

Nature-inspired algorithms are among the most powerful algorithms for optimization. In this study, a new nature-inspired metaheuristic optimization algorithm, called bat algorithm (BA), is introduced for solving engineering optimization tasks. The proposed BA is based on the echolocation behavior of bats. After a detailed formulation and explanation of its implementation, BA is verified using eight nonlinear engineering optimization problems reported in the specialized literature. BA has been carefully implemented and carried out optimization for eight well-known optimization tasks. Then, a comparison has been made between the proposed algorithm and other existing algorithms. The optimal solutions obtained by the proposed algorithm are better than the best solutions obtained by the existing methods. The unique search features used in BA are analyzed, and their implications for future research are also discussed in detail.