Application of improved particle swarm algorithm to power source capacity optimization in multi-energy industrial parks

Aiming at the optimization of power source capacity in multi-energy industrial parks, an economic optimization model with the lowest comprehensive cost of the system as the objective function was established, and an improved particle swarm optimization algorithm with natural selection strategy and c...

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Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2020-01, Vol.38 (1), p.355-363
Main Authors: Xiong, Junhua, Li, Ruisheng, Wang, Tingling, Gao, Jinfeng
Format: Article
Language:English
Subjects:
Algorithms
Chaos theory
Computer simulation
Design optimization
Economic models
Industrial parks
Iterative methods
Mapping
Optimization
Particle swarm optimization
Solution space
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Summary:Aiming at the optimization of power source capacity in multi-energy industrial parks, an economic optimization model with the lowest comprehensive cost of the system as the objective function was established, and an improved particle swarm optimization algorithm with natural selection strategy and chaos theory was proposed to optimize the model. This algorithm initialized particle fitness by chaotic mapping, added natural selection strategy to the iterative optimization process, and used chaotic ergodicity to search solution space. The test function simulation showed that the algorithm had the characteristics of fast convergence, high precision and being not easy to fall into local optimum. A case study of a certain area in Hebei Province, China, was selected to analyze the example, and the power source capacity optimization design scheme was obtained. The analysis results verified the effectiveness of the algorithm.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-179411