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A hybrid invasive weed optimization algorithm for the economic load dispatch problem in power systems

1 College of Computer Science, Liaocheng University, Liaocheng 252059, China
2 School of information science and engineering, Shandong Normal University, 250014, China
3 China Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 211189, China

Special Issues: Artificial Intelligence and Optimization in Sustainable Manufacturing

In this study, a hybrid invasive weed optimization (HIWO) algorithm that hybridizes the invasive weed optimization (IWO) algorithm and genetic algorithm (GA) has been proposed to solve economic dispatch (ED) problems in power systems. In the proposed algorithm, the IWO algorithm is used as the main optimizer to explore the solution space, whereas the crossover and mutation operations of the GA are developed to significantly improve the optimization ability of IWO. In addition, an effective repair method is embedded in the proposed algorithm to repair infeasible solutions by handing various practical constraints of ED problems. To verify the optimization performance of the proposed algorithm and the effectiveness of the repair method, six ED problems in the different-scale power systems were tested and compared with other algorithms proposed in the literature. The experimental results indicated that the proposed HIWO algorithm can obtain the more economical dispatch solutions, and the proposed repair method can effectively repair each infeasible dispatch solution to a feasible solution. The convergence capability, applicability and effectiveness of HIWO were also demonstrated through the comprehensive comparison results.
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Keywords economic dispatch; hybrid invasive weed optimization; crossover operation; mutation operation; power system

Citation: Zhi-xin Zheng, Jun-qing Li, Hong-yan Sang. A hybrid invasive weed optimization algorithm for the economic load dispatch problem in power systems. Mathematical Biosciences and Engineering, 2019, 16(4): 2775-2794. doi: 10.3934/mbe.2019138

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