New directional bat algorithm for continuous optimization problems

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.

References in corpus (1)

• Bat Algorithm: A Novel Approach for Global Engineering Optimization

Cited by in corpus (4)

• Nature-Inspired Optimization Algorithms: Challenges and Open Problems
• The Global Convergence Analysis of the Bat Algorithm Using a Markovian Framework and Dynamical System Theory
• Reliability based-design optimization using the directional bat algorithm
• Critical Analysis: Bat Algorithm based Investigation and Application on Several Domains