TY  - BOOK
AV  - none
N2  - The practical Economic Dispatch (ED) problems have non-convex objective functions with complex constraints due to the effects of valve point loadings, multiple fuels, and prohibited zones. This leads to difficulty in finding the global optimal solution of the ED problems. This chapter proposes a new swarm-based Mean-Variance Mapping Optimization (MVMOS) for solving the non-convex ED. The proposed algorithm is a new population-based meta-heuristic optimization technique. Its special feature is a mapping function applied for the mutation. The proposed MVMOS is tested on several test systems and the comparisons of numerical obtained results between MVMOS and other optimization techniques are carried out. The comparisons show that the proposed method is more robust and provides better solution quality than most of the other methods. Therefore, the MVMOS is very favorable for solving non-convex ED problems. © 2015, IGI Global. All rights reserved.
N1  - cited By 5
KW  - Mapping; Optimization; Scheduling
KW  -  Complex constraints; Economic dispatch problems; Global optimal solutions; Mapping optimization; Meta-heuristic optimization techniques; Non-convex economic dispatches; Non-convex objective functions; Optimization techniques
KW  -  Electric load dispatching
SP  - 211
ID  - scholars5959
TI  - Swarm-based mean-variance mapping optimization (MVMOS) for solving non-convex economic dispatch problems
Y1  - 2015///
SN  - 9781466682924; 1466682914; 9781466682917
PB  - IGI Global
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84957398364&doi=10.4018%2f978-1-4666-8291-7.ch007&partnerID=40&md5=6b898dbbf2ec18646719b25b6c2cbcb0
A1  - Khoa, T.H.
A1  - Vasant, P.M.
A1  - Singh, B.S.M.
A1  - Dieu, V.N.
EP  - 251
ER  -