Extreme learning machine based genetic algorithm and its application in power system economic dispatch

In this paper a novel optimization algorithm, which utilizes the key ideas of both genetic algorithm (GA) and extreme learning machine (ELM), is proposed. Traditional genetic algorithm employs genetic operations, such as selection, mutation and crossover to generate the optimal solution. In practice...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2013-02, Vol.102, p.154-162
Main Authors: Yang, Hongming, Yi, Jun, Zhao, Junhua, Dong, ZhaoYang
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Convergence
Economics
Electrical engineering. Electrical power engineering
Electrical power engineering
Elm
Exact sciences and technology
Extreme learning machine
Genetic algorithm
Genetic algorithms
Genetics
Learning
Neural networks
Operation. Load control. Reliability
Optimization
Power networks and lines
Power system economic dispatch
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Summary:In this paper a novel optimization algorithm, which utilizes the key ideas of both genetic algorithm (GA) and extreme learning machine (ELM), is proposed. Traditional genetic algorithm employs genetic operations, such as selection, mutation and crossover to generate the optimal solution. In practice, the child solutions generated by crossover and mutation are largely random and therefore cannot ensure the fast convergence of the algorithm. To tackle the weakness of traditional GA, the ELM is introduced to estimate the nonlinear functional relationships between the parent population and child population generated by genetic operations. The trained downward-climbing and upward-climbing ELMs are then employed to generate candidate solutions, which forms the new population together with the solutions given by genetic operations. The proposed algorithm is applied to the power system economic dispatch problem. As demonstrated in case studies, the modified genetic algorithm is able to locate local minima faster and escape from local minima with a greater probability. The proposed algorithm can therefore ensure the faster convergence and provide more economical dispatch plans.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2011.12.054