Comparison of Different Bat Initialization Techniques for Global Optimization Problems

Wasqas Haider Bangyal, Jamil Ahmad and Hafiz Tayyab Rauf
Additional contact information
Wasqas Haider Bangyal: Iqra University, Islamabad, Pakistan
Jamil Ahmad: Hazara University, Mansehra, Pakistan
Hafiz Tayyab Rauf: University of Gujrat, Pakistan

International Journal of Applied Metaheuristic Computing (IJAMC), 2021, vol. 12, issue 1, 157-184

Abstract: Bat algorithm (BA) is a population-based stochastic search technique that has been widely used to solve the diverse kind of optimization problems. Population initialization is the current ongoing research problem in evolutionary computing algorithms. Appropriate population initialization assists the algorithm to investigate the swarm search space effectively. BA faces premature convergence problem to find actual global optimization value. Low discrepancy sequences are slightly lesser random number than pseudo-random; however, they are more powerful for computational approaches. In this work, new population initialization approach Halton (BA-HA), Sobol (BA-SO), and Torus (BA-TO) are proposed, which helps bats to avoid from the premature convergence. The proposed approaches are examined on standard benchmark functions, and simulation results are compared with standard BA initialized with uniform distribution. The results depict that substantial enhancement can be attained in the performance of standard BA while varying the random numbers sequences to low discrepancy sequences.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJAMC.2021010109 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jamc00:v:12:y:2021:i:1:p:157-184

Access Statistics for this article

International Journal of Applied Metaheuristic Computing (IJAMC) is currently edited by Peng-Yeng Yin

More articles in International Journal of Applied Metaheuristic Computing (IJAMC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().