Artificial Bee Colony Algorithm Based on K-Means Clustering for Multiobjective Optimal Power Flow Problem

An improved multiobjective ABC algorithm based on K-means clustering, called CMOABC, is proposed. To fasten the convergence rate of the canonical MOABC, the way of information communication in the employed bees’ phase is modified. For keeping the population diversity, the multiswarm technology based...

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Bibliographic Details
Published in:Mathematical problems in engineering 2015-01, Vol.2015 (2015), p.1-18
Main Authors: Sun, Liling, Chen, Hanning, Hu, Jing-tao
Format: Article
Language:English
Subjects:
Algorithms
Bees
Cluster analysis
Clustering
Clusters
Computer simulation
Functions (mathematics)
Genetic algorithms
Information communication
Linear programming
Mathematical analysis
Mathematical problems
Multiple objective analysis
Optimization
Optimization techniques
Power
Power flow
Search algorithms
Sorting algorithms
Swarm intelligence
Vector quantization
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Summary:An improved multiobjective ABC algorithm based on K-means clustering, called CMOABC, is proposed. To fasten the convergence rate of the canonical MOABC, the way of information communication in the employed bees’ phase is modified. For keeping the population diversity, the multiswarm technology based on K-means clustering is employed to decompose the population into many clusters. Due to each subcomponent evolving separately, after every specific iteration, the population will be reclustered to facilitate information exchange among different clusters. Application of the new CMOABC on several multiobjective benchmark functions shows a marked improvement in performance over the fast nondominated sorting genetic algorithm (NSGA-II), the multiobjective particle swarm optimizer (MOPSO), and the multiobjective ABC (MOABC). Finally, the CMOABC is applied to solve the real-world optimal power flow (OPF) problem that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results demonstrate that, compared to NSGA-II, MOPSO, and MOABC, the proposed CMOABC is superior for solving OPF problem, in terms of optimization accuracy.
ISSN:1024-123X
1563-5147
DOI:10.1155/2015/762853