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Noninferior Solution Grey Wolf Optimizer with an Independent Local Search Mechanism for Solving Economic Load Dispatch Problems

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  • Jianzhong Xu

    (School of Economics and Management, Harbin Engineering University, 145 Nantong Street, Harbin 150001, China)

  • Fu Yan

    (School of Economics and Management, Harbin Engineering University, 145 Nantong Street, Harbin 150001, China)

  • Kumchol Yun

    (School of Economics and Management, Harbin Engineering University, 145 Nantong Street, Harbin 150001, China
    Faculty of Mechanics, Kim Il Sung University, Pyongyang 950003, Democratic People’s Republic of Korea)

  • Lifei Su

    (College of Resources and Environment, Northeast Agricultural University, 600 Changjiang Road, Harbin 150030, China)

  • Fengshu Li

    (School of Economics and Management, Harbin Engineering University, 145 Nantong Street, Harbin 150001, China)

  • Jun Guan

    (College of Economics and Management, Northeast Forestry University, 26 Hexing Road, Harbin 150040, China)

Abstract

The economic load dispatch (ELD) problem is a complex optimization problem in power systems. The main task for this optimization problem is to minimize the total fuel cost of generators while also meeting the conditional constraints of valve-point loading effects, prohibited operating zones, and nonsmooth cost functions. In this paper, a novel grey wolf optimization (GWO), abbreviated as NGWO, is proposed to solve the ELD problem by introducing an independent local search strategy and a noninferior solution neighborhood independent local search technique to the original GWO algorithm to achieve the best problem solution. A local search strategy is added to the standard GWO algorithm in the NGWO, which is called GWOI, to search the local neighborhood of the global optimal point in depth and to guarantee a better candidate. In addition, a noninferior solution neighborhood independent local search method is introduced into the GWOI algorithm to find a better solution in the noninferior solution neighborhood and ensure the high probability of jumping out of the local optimum. The feasibility of the proposed NGWO method is verified on five different power systems, and it is compared with other selected methods in terms of the solution quality, convergence rate, and robustness. The compared experimental results indicate that the proposed NGWO method can efficiently solve ELD problems with higher-quality solutions.

Suggested Citation

  • Jianzhong Xu & Fu Yan & Kumchol Yun & Lifei Su & Fengshu Li & Jun Guan, 2019. "Noninferior Solution Grey Wolf Optimizer with an Independent Local Search Mechanism for Solving Economic Load Dispatch Problems," Energies, MDPI, vol. 12(12), pages 1-26, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2274-:d:239656
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    References listed on IDEAS

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    Cited by:

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    3. Vikram Kumar Kamboj & Challa Leela Kumari & Sarbjeet Kaur Bath & Deepak Prashar & Mamoon Rashid & Sultan S. Alshamrani & Ahmed Saeed AlGhamdi, 2022. "A Cost-Effective Solution for Non-Convex Economic Load Dispatch Problems in Power Systems Using Slime Mould Algorithm," Sustainability, MDPI, vol. 14(5), pages 1-36, February.
    4. Aokang Pang & Huijun Liang & Chenhao Lin & Lei Yao, 2023. "A Surrogate-Assisted Adaptive Bat Algorithm for Large-Scale Economic Dispatch," Energies, MDPI, vol. 16(2), pages 1-23, January.
    5. Fu Yan & Jianzhong Xu & Kumchol Yun, 2019. "Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information," Complexity, Hindawi, vol. 2019, pages 1-36, December.
    6. Dinu Calin Secui & Cristina Hora & Codruta Bendea & Monica Liana Secui & Gabriel Bendea & Florin Ciprian Dan, 2024. "Modified Social Group Optimization to Solve the Problem of Economic Emission Dispatch with the Incorporation of Wind Power," Sustainability, MDPI, vol. 16(1), pages 1-35, January.

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