relation: https://khub.utp.edu.my/scholars/8329/
title: Hybrid mean-variance mapping optimization for non-convex economic dispatch problems
creator: Khoa, T.H.
creator: Vasant, P.M.
creator: Singh, B.S.M.
creator: Dieu, V.N.
description: The economic dispatch (ED) is one of the important optimization problems in power system generation for fuel cost saving. This paper proposes a hybrid variant of mean-variance mapping optimization (MVMO-SH) for solving such problem considering the non-convex objective functions. The new proposed method is a hybrid variant of the original mean-variance mapping optimization algorithm (MVMO) with the embedded local search and multi-parent crossover to enhance its global search ability and improve solution quality for optimization problems. The proposed MVMO-SH is tested on different non-convex ED problem including valve point effects, multiple fuels and prohibited operating zones characteristics. The result comparisons from the proposed method with other methods in the literature have indicated that the proposed method is more robust and provides better solution quality than the others. Therefore, the proposed MVMO-SH is a promising method for solving the complex ED problems in power systems. © 2017, IGI Global.
publisher: IGI Global
date: 2017
type: Article
type: PeerReviewed
identifier:   Khoa, T.H. and Vasant, P.M. and Singh, B.S.M. and Dieu, V.N.  (2017) Hybrid mean-variance mapping optimization for non-convex economic dispatch problems.  International Journal of Swarm Intelligence Research, 8 (4).  pp. 34-59.  ISSN 19479263     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048480523&doi=10.4018%2fIJSIR.2017100103&partnerID=40&md5=9d94340213630ca17dbb0130d5c10a42
relation: 10.4018/IJSIR.2017100103
identifier: 10.4018/IJSIR.2017100103