 An Adaptive Differential Evolution Algorithm Based on Data Preprocessing Method and a New Mutation Strategy to Solve Dynamic Economic Dispatch Considering Generator ConstraintsRuxin Zhao (),
Wei Wang,
Tingting Zhang,
Chang Liu,
Lixiang Fu,
Jiajie Kang,
Hongtan Zhang,
Yang Shi and
Chao Jiang
Computational Economics, 2025, vol. 66, issue 1, No 7, 207-240 Abstract: Abstract Differential evolution (DE) algorithm is a classical natural-inspired optimization algorithm which has a good. However, with the deepening of research, some researchers found that the quality of the candidate solution of the population in the differential evolution algorithm is poor and its global search ability is not enough when solving the global optimization problem. Therefore, in order to solve the above problems, we proposed an adaptive differential evolution algorithm based on the data processing method and a new mutation strategy (ADEDPMS). In this paper, the data preprocessing method is implemented by k-means clustering algorithm, which is used to divide the initial population into multiple clusters according to the average value of fitness, and select candidate solutions in each cluster according to different proportions. This method improves the quality of candidate solutions of the population to a certain extent. In addition, in order to solve the problem of insufficient global search ability in differential evolution algorithm, we also proposed a new mutation strategy, which is called “DE/current-to- $${p}_{1}$$ p 1 best& $${p}_{2}$$ p 2 best”. This strategy guides the search direction of the differential evolution algorithm by selecting individuals with good fitness, so that its search range is in the most promising candidate solution region, and indirectly increases the population diversity of the algorithm. We also proposed an adaptive parameter control method, which can effectively balance the relationship between the exploration process and the exploitation process to achieve the best performance. In order to verify the effectiveness of the proposed algorithm, the ADEDPMS is compared with five optimization algorithms of the same type in the past three years, which are AAGSA, DFPSO, HGASSO, HHO and VAGWO. In the simulation experiment, 6 benchmark test functions and 4 engineering example problems are used, and the convergence accuracy, convergence speed and stability are fully compared. We used ADEDPMS to solve the dynamic economic dispatch (ED) problem with generator constraints. It is compared with the optimization algorithms used to solve the ED problem in the last three years which are AEFA, AVOA, OOA, SCA and TLBO. The experimental results show that compared with the five latest optimization algorithms proposed in the past three years to solve benchmark functions, engineering example problems and the ED problem, the proposed algorithm has strong competitiveness in each test index. Keywords: Differential evolution algorithm; Data preprocessing method; DE/current-to- $${p}_{1}$$ p 1 best& $${p}_{2}$$ p 2 best; Adaptive parameter control method; Dynamic economic dispatch problem (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:66:y:2025:i:1:d:10.1007_s10614-024-10705-2 Ordering information: This journal article can be ordered from DOI: 10.1007/s10614-024-10705-2 Access Statistics for this article Computational Economics is currently edited by Hans Amman More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC. |
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