research-article

An Improved Genetic Bat algorithm for Unconstrained Global Optimization Problems

Authors:

Muhammad Zubair Rehman,

Kamal Z. Zamli,

Abdullah NasserAuthors Info & Claims

ICSCA '20: Proceedings of the 2020 9th International Conference on Software and Computer Applications

Pages 94 - 98

https://doi.org/10.1145/3384544.3384603

Published: 17 April 2020 Publication History

ICSCA '20: Proceedings of the 2020 9th International Conference on Software and Computer Applications

An Improved Genetic Bat algorithm for Unconstrained Global Optimization Problems

Pages 94 - 98

Abstract
References

Abstract

Metaheuristic search algorithms have been in use for quite a while to optimally solve complex searching problems with ease. Nowadays, nature inspired swarm intelligent algorithms have become quite popular due to their propensity for finding optimal solutions with agility. Genetic algorithm (GA) is successfully applied in several engineering fields for the past four decades but it still has a problem of slow convergence due to its reliability on the initial state of its operators. Therefore, to ensure that GA converges to a global solution, this paper proposed a two-stage improved Genetic Bat algorithm (GBa) in which the GA finds the optimal solution first and then Bat starts from where the GA has converged. This multi-stage optimization ensures that optimal solution is always reached through fine balance in between exploration and exploitation behavior of Genetic algorithm.

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Cited By

Zan ZCong YZhang X(2022)An Improved Bat Algorithm For Solving Nonlinear Algebraic Systems Of EquationsProceedings of the 7th International Conference on Big Data and Computing10.1145/3545801.3545812(75-81)Online publication date: 27-May-2022
https://dl.acm.org/doi/10.1145/3545801.3545812
Bi JYuan HZhai JZhou MPoor H(2022)Self-adaptive Bat Algorithm With Genetic OperationsIEEE/CAA Journal of Automatica Sinica10.1109/JAS.2022.1056959:7(1284-1294)Online publication date: Jul-2022
https://doi.org/10.1109/JAS.2022.105695
Bi JZhai JYuan H(2021)An Adaptive Hybrid Bat Algorithm with Genetic Operations and Dynamic Inertia Weight2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)10.1109/ICNSC52481.2021.9702210(1-6)Online publication date: 3-Dec-2021
https://doi.org/10.1109/ICNSC52481.2021.9702210

Index Terms

An Improved Genetic Bat algorithm for Unconstrained Global Optimization Problems
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Non-parametric optimization

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cover image ACM Other conferences

ICSCA '20: Proceedings of the 2020 9th International Conference on Software and Computer Applications

February 2020

382 pages

ISBN:9781450376655

DOI:10.1145/3384544

Copyright © 2020 ACM.

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Publication History

Published: 17 April 2020

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Funding Sources

Fundamental Research Grant Scheme (FRGS) from the Ministry of Education (MOE), Malaysia

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ICSCA 2020

ICSCA 2020: 2020 9th International Conference on Software and Computer Applications

February 18 - 21, 2020

Langkawi, Malaysia

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Cited By

Zan ZCong YZhang X(2022)An Improved Bat Algorithm For Solving Nonlinear Algebraic Systems Of EquationsProceedings of the 7th International Conference on Big Data and Computing10.1145/3545801.3545812(75-81)Online publication date: 27-May-2022
https://dl.acm.org/doi/10.1145/3545801.3545812
Bi JYuan HZhai JZhou MPoor H(2022)Self-adaptive Bat Algorithm With Genetic OperationsIEEE/CAA Journal of Automatica Sinica10.1109/JAS.2022.1056959:7(1284-1294)Online publication date: Jul-2022
https://doi.org/10.1109/JAS.2022.105695
Bi JZhai JYuan H(2021)An Adaptive Hybrid Bat Algorithm with Genetic Operations and Dynamic Inertia Weight2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)10.1109/ICNSC52481.2021.9702210(1-6)Online publication date: 3-Dec-2021
https://doi.org/10.1109/ICNSC52481.2021.9702210

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