Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18331
Title: Equilibrium-inspired multiple group search optimization with synergistic learning for multiobjective electric power dispatch
Authors: Zhou, B
Chan, KW 
Yu, T
Chung, CY
Keywords: Multiobjective power dispatch
Multiple group search optimizer
Nash equilibrium
Pareto-optimal front
Synergistic learning
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on power systems, 2013, v. 28, no. 4, p. 3534-3545 How to cite?
Journal: IEEE transactions on power systems 
Abstract: This paper proposes a novel multiple group search optimizer (MGSO) to solve the highly constrained multiobjective power dispatch (MOPD) problem with conflicting and competing objectives. The algorithm employs a stochastic learning automata based synergistic learning to allow information interaction and credit assignment among multi-groups for cooperative search. An alternative constraint handling, which separates constraints and objectives with different searching strategies, has been adopted to produce a more uniformly-distributed Pareto-optimal front (PF). Moreover, two enhancements, namely space reduction and chaotic sequence dispersion, have also been incorporated to facilitate local exploitation and global exploration of Pareto-optimal solutions in the convergence process. Lastly, Nash equilibrium point is first introduced to identify the best compromise solution from the PF. The performance of MGSO has been fully evaluated and benchmarked on the IEEE 30-bus 6-generator system and 118-bus 54-generator system. Comparisons with previous Pareto heuristic techniques demonstrated the superiority of the proposed MGSO and confirm its capability to cope with practical multiobjective optimization problems with multiple high-dimensional objective functions.
URI: http://hdl.handle.net/10397/18331
ISSN: 0885-8950
EISSN: 1558-0679
DOI: 10.1109/TPWRS.2013.2259641
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