An improved genetic bat algorithm for unconstrained global optimization problems

Muhammad Zubair, Rehman and Kamal Z., Zamli and Abdullah, Nasser (2020) An improved genetic bat algorithm for unconstrained global optimization problems. In: 9th International Conference on Software and Computer Applications (ICSCA 2020) , 18 - 21 Feb. 2020 , Langkawi, Malaysia. pp. 94-98.. ISBN 978-145037665-5

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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.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Genetic Bat algorithm (GBa); Genetic Algorithm (GA); Multi-stage; Swarm intelligent
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computing
Depositing User: Pn. Hazlinda Abd Rahman
Date Deposited: 10 Feb 2021 06:55
Last Modified: 10 Feb 2021 06:55
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