A General Intelligent Optimization Algorithm Combination Framework with Application in Economic Load Dispatch Problems

Recently, a population-based intelligent optimization algorithm research has been combined with multiple algorithms or algorithm components in order to improve the performance and robustness of an optimization algorithm. This paper introduces the idea into real world application. Different from trad...

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Veröffentlicht in:Energies (Basel) Jg. 12; H. 11; S. 2175
Hauptverfasser: Zhang, Jinghua, Dong, Ze
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 06.06.2019
Schlagworte:
Adaptation
algorithm framework
Behavior
Biogeography
Design
Economic load dispatch (ELD)
Genetic algorithms
Intelligence
multi-behavior combination
Mutation
Optimization algorithms
Population
population-based intelligent optimization algorithm
Researchers
ISSN:1996-1073, 1996-1073
Online-Zugang:Volltext
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Zusammenfassung:Recently, a population-based intelligent optimization algorithm research has been combined with multiple algorithms or algorithm components in order to improve the performance and robustness of an optimization algorithm. This paper introduces the idea into real world application. Different from traditional algorithm research, this paper implements this idea as a general framework. The combination of multiple algorithms or algorithm components is regarded as a complex multi-behavior population, and a unified multi-behavior combination model is proposed. A general agent-based algorithm framework is designed to support the model, and various multi-behavior combination algorithms can be customized under the framework. Then, the paper customizes a multi-behavior combination algorithm and applies the algorithm to solve the economic load dispatch problems. The algorithm has been tested with four test systems. The test results prove that the multi-behavior combination idea is meaningful which also indicates the significance of the framework.
Bibliographie:ObjectType-Article-1
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ISSN:1996-1073
1996-1073
DOI:10.3390/en12112175