New directional bat algorithm for continuous optimization problems
This is an incremental improvement for researchers and practitioners in optimization algorithms, addressing a specific bottleneck in swarm intelligence methods.
The paper tackled the problem of premature convergence in the standard bat algorithm by introducing directional echolocation and three other improvements, resulting in a new directional bat algorithm (dBA) that showed statistical superiority over ten other algorithms and variants in benchmarks from the CEC2005 suite.
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.