基于动态惩罚因子的改进蚱蜢算法求解经济负荷调度和经济排放联合调度问题
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1.东北财经大学;2.辽宁科技大学

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TP18

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Improved Grasshopper Optimization Algorithm Based on Dynamic Penalty Factors to Solve Economic Load Dispatch and Combined Economic Emission Dispatch Problem
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1.DongbeiUniversityofFinanceandEconomics;2.UniversityofScienceandTechnologyLiaoning

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    摘要:

    电力生产装置运行中各种燃料的成本逐步增加,需要最小化成本函数以求解此类复杂经济负荷调度问 题. 鉴于此,提出一种基于动态惩罚因子的改进蚱蜢算法求解经济负荷调度(economic load dispatch, ELD)问题和 经济排放联合调度(combined economic emission dispatch, CEED)问题. 为了提高蚱蜢算法(grasshopper optimization algorithm, GOA)性能,提出一种改进的混合蚱蜢算法(hybrid grasshopper optimization algorithm, HGOA),将重力搜 索算子和鸽群搜索算子-地标算子加入GOA中,增强算法的搜索能力,平衡算法的勘探和开发. 同时,为了更好地 解决ELD和CEED问题中的约束问题,提出6个惩罚函数,包括2个V型函数、反正切函数、反正弦函数、线性函数 和二次函数,并使用动态惩罚策略代替传统的固定值惩罚策略. 选取3个ELD问题案例和4个CEED问题案例验 证所提出方法的有效性,实验结果表明, HGOA相较于其他元启发式算法在求解质量上表现更好,且动态惩罚策略 比固定值惩罚策略效果更好.

    Abstract:

    The cost of various fuels is increasing gradually in the operation of the electric power production unit, so it is necessary to minimize the cost function to solve this kind of complex economic load dispatch problem. An improved grasshopper optimization algorithm (GOA) based on dynamic penalty factors is proposed to solve the economic load dispatch (ELD) problem and the combined economic emission dispatch (CEED) problem. In order to improve the performance of the GOA, an improved hybrid grasshopper optimization algorithm (HGOA) is proposed. The gravity search operator and pigeon landmark operator are added into the GOA to enhance the search ability of the algorithm, and balance the exploration and development of the algorithm. At the same time, in order to solve the constraints in the ELD and CEED problems, six penalty functions are proposed, including two V-shaped functions, arc tangent functions, arc sine functions, linear functions and the quadratic function, and the dynamic penalty strategy is used to replace the traditional fixed value penalty strategy. Three cases of the ELD problem and four cases of the CEED problem are presented to verify the effectiveness of the proposed method. Experimental results show that the HGOA performs better in terms of the solving quality than other meta-heuristic algorithms, and the dynamic penalty strategy performs better than the fixed value penalty strategy.

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历史
  • 收稿日期:2022-01-11
  • 最后修改日期:2022-04-11
  • 录用日期:2022-03-14
  • 在线发布日期: 2022-03-25
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