An Elitist Transposon Quantum-Based Particle Swarm Optimization Algorithm for Economic Dispatch Problems

Angus Wu; Zhen-Lun Yang

doi:10.1155/2018/7276585

Author(s)

Angus Wu
Zhen-Lun Yang

Related Research Unit(s)

Department of Electrical Engineering

Detail(s)

Original language	English
Article number	7276585
Journal / Publication	Complexity
Volume	2018
Publication status	Published - 17 Jul 2018

Link(s)

DOI	DOI https://doi.org/10.1155/2018/7276585 Final Published version
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Attachment(s)	Documents 24167272.pdf Final Published version, 2.03 MB, PDF Publisher's Copyright Statement This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/
Link to Scopus	https://www.scopus.com/record/display.uri?eid=2-s2.0-85062347092&origin=recordpage
Permanent Link	https://scholars.cityu.edu.hk/en/publications/publication(22bc0bff-fa96-4d28-9cda-22b4da3bd469).html

Abstract

Population based optimization algorithms are useful tools in solving engineering problems. This paper presents an elitist transposon quantum-based particle swarm algorithm to solve the economic dispatch (ED) problem. It is a complex and highly nonlinear constrained optimization problem. The proposed approach, double elitist breeding quantum-based particle swarm optimization algorithm (DEB-QPSO), makes use of two elitist breeding strategies to promote the diversity of the swarm so as to enhance the global search ability and an improved efficient heuristic handing technique to manage the equality and inequality constraints of ED problems. Investigations on 15-unit, 40-unit, and 140-unit widely used test systems, through performance comparison, the proposed DEB-QPSO is able to obtain higher quality solutions efficiently and stably superior than that of other the state-of-the-art algorithms.