Enhanced Bat Algorithm for Solving Non-Convex Economic Dispatch Problem

Hussain, K. and Zhu, W. and Salleh, M.N.M. and Ali, H. and Talpur, N. and Naseem, R. and Ahmad, A. and Ullah, A. (2020) Enhanced Bat Algorithm for Solving Non-Convex Economic Dispatch Problem. Advances in Intelligent Systems and Computing, 978 AI. pp. 419-428.

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Abstract

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. © Springer Nature Switzerland AG 2020.

Item Type: Article
Impact Factor: cited By 0
Uncontrolled Keywords: Data mining; Optimization; Scheduling; Soft computing, Bat algorithms; Economic Dispatch; Meta heuristic algorithm; Non-convex; Non-convex economic dispatches; Power generation dispatch; Power generation systems; Pre-mature convergences, Electric load dispatching
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 27 Aug 2021 05:51
Last Modified: 27 Aug 2021 05:51
URI: http://scholars.utp.edu.my/id/eprint/24740

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