Enhanced Bat Algorithm for Solving Non-Convex Economic Dispatch Problem

Kashif Hussain, William Zhu, Mohd Najib Mohd Salleh, Haseeb Ali, Noreen Talpur, Rashid Naseem, Arshad Ahmad, Ayaz Ullah

Research output: Chapter in Book/Report/Published conference proceedingConference contributionpeer-review


Bat algorithm lags behind other modern metaheuristic algorithms in terms of search efficiency, due to premature convergence. Once trapped in any sub-optimal region, the algorithm is unable to escape because of deficiency in population diversity. To address this, an enhanced Bat Algorithm (EBA) is introduced in this paper. The EBA algorithm comes with adaptive exploration and exploitation capability, as well as, additional population diversity. This enables EBA improve its convergence ability to find even better solutions towards the end of search process, where standard BA is often trapped. To illustrate effectiveness of the proposed method, EBA is applied on non-linear, non-convex economic dispatch problem with a power generation system comprising of twenty thermal units. The experimental results suggest that EBA not only saved power generation cost but also reduced transmission losses, more efficiently as compared to original BA and other methods reported in literature. The EBA algorithm also showed enhanced convergence ability than BA towards the end of iterations.
Original languageEnglish
Title of host publicationRecent Advances on Soft Computing and Data Mining
Subtitle of host publicationProceedings of the Fourth International Conference on Soft Computing and Data Mining (SCDM 2020), Melaka, Malaysia, January 22–⁠23, 2020
EditorsRozaida Ghazali, Nazri Mohd Nawi, Mustafa Mat Deris, Jemal H. Abawajy
Number of pages10
ISBN (Electronic)978-3-030-36056-6
ISBN (Print)978-3-030-36055-9
Publication statusPublished - 5 Dec 2019

Publication series

NameAdvances in Intelligent Systems and Computing

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