An Elitist Transposon Quantum-Based Particle Swarm Optimization Algorithm for Economic Dispatch Problems
作者： Angus Wu , Zhen-Lun Yang
DOI: 10.1155/2018/7276585
关键词: Heuristic (computer science) 、 Computer science 、 Swarm behaviour 、 Quantum 、 Population 、 Heuristic 、 Economic dispatch 、 Particle swarm optimization 、 Algorithm
摘要: Population-based optimization algorithms are useful tools in solving engineering problems. This paper presents an elitist transposon quantum-based particle swarm algorithm to solve economic dispatch (ED) It is a complex and highly nonlinear constrained problem. The proposed approach, double breeding (DEB-QPSO), makes use of two strategies promote the diversity so as enhance global search ability improved efficient heuristic handling technique manage equality inequality constraints ED Investigating on 15-unit, 40-unit, 140-unit widely used test systems, through performance comparison, DEB-QPSO able obtain higher-quality solutions efficiently stably superior than other state-of-the-art algorithms.
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