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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51482

Title: A Q-learning-based swarm optimization algorithm for economic dispatch problem
Authors: Yi-Zeng Hsieh
Mu-Chun Su
Contributors: 國立臺灣海洋大學:電機工程學系
Keywords: Optimization
Particle swarm optimization
Swarm intelligence
Q-learning
Date: 2015-10-30
Issue Date: 2018-11-30T07:53:43Z
Publisher: Neural Computing and Applications
Abstract: Abstract: In this paper, we treat optimization problems as a kind of reinforcement learning problems regarding an optimization procedure for searching an optimal solution as a reinforcement learning procedure for finding the best policy to maximize the expected rewards. This viewpoint motivated us to propose a Q-learning-based swarm optimization (QSO) algorithm. The proposed QSO algorithm is a population-based optimization algorithm which integrates the essential properties of Q-learning and particle swarm optimization. The optimization procedure of the QSO algorithm proceeds as each individual imitates the behavior of the global best one in the swarm. The best individual is chosen based on its accumulated performance instead of its momentary performance at each evaluation. Two data sets including a set of benchmark functions and a real-world problem—the economic dispatch (ED) problem for power systems—were used to test the performance of the proposed QSO algorithm. The simulation results on the benchmark functions show that the proposed QSO algorithm is comparable to or even outperforms several existing optimization algorithms. As for the ED problem, the proposed QSO algorithm has found solutions better than all previously found solutions.
Relation: 27(8) pp.2333–2350
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51482
Appears in Collections:[電機工程學系] 期刊論文

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