New directional bat algorithm for continuous optimization problems

TL;DR

The paper introduces a directional bat algorithm (dBA) that enhances exploration and exploitation in continuous optimization, outperforming existing algorithms through directional echolocation and additional improvements.

Contribution

It proposes a novel directional echolocation mechanism and three enhancements to the standard bat algorithm, improving its optimization performance.

Findings

dBA outperforms ten other algorithms on benchmark tests

Statistical tests confirm the superiority of dBA

Enhanced exploration reduces premature convergence

Abstract

Bat algorithm (BA) is a recent optimization algorithm based on swarm intelligence and inspiration from the echolocation behavior of bats. One of the issues in the standard bat algorithm is the premature convergence that can occur due to the low exploration ability of the algorithm under some conditions. To overcome this deficiency, directional echolocation is introduced to the standard bat algorithm to enhance its exploration and exploitation capabilities. In addition to such directional echolocation, three other improvements have been embedded into the standard bat algorithm to enhance its performance. The new proposed approach, namely the directional Bat Algorithm (dBA), has been then tested using several standard and non-standard benchmarks from the CEC2005 benchmark suite. The performance of dBA has been compared with ten other algorithms and BA variants using non-parametric statistical tests. The statistical test results show the superiority of the directional bat algorithm.