Abstract
To solve the non-convex and non-linear economic dispatch problem efficiently, a chaotic iteration particle swarm optimization algorithm is presented. In the global research of particle swarm optimization and local optimum, ergodicity of chaos can effectively restrain premature. To balance the exploration and exploitation abilities and avoid being trapped into local optimal, a new index, called iteration best, is incorporated into particle swarm optimization, and chaotic mutation with a new Tent map imported can make local search within the prior knowledge, a new strategy is proposed in iteration strategy. The algorithm is validated for two test systems consisting of 6 and 15 generators. Compared with other methods in this literature, the experimental result demonstrates the high convergency and effectiveness of proposed algorithm.
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Yu, Z., Zhou, F. (2015). Chaotic Iteration Particle Swarm Optimization Algorithm Based on Economic Load Dispatch. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_56
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DOI: https://doi.org/10.1007/978-3-319-22180-9_56
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