artificial bee colony algorithm based on k-means clustering for multiobjective optimal power flow problem

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ID: 177842
2015
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Abstract
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.
Reference Key
sun2015mathematicalartificial Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Liling Sun;Jingtao Hu;Hanning Chen
Journal journal of power sources
Year 2015
DOI
10.1155/2015/762853
URL
http://dx.doi.org/10.1155/2015/762853
Keywords
civil engineering (general)mathematics engineering (general)

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