An opposition-based differential evolution clustering algorithm for emotional preference and migratory behavior optimization

Dai, Mingzhi; Feng, Xiang; Yu, Huiqun; Guo, Wei

doi:10.1016/j.knosys.2022.110073

Cited by 10 publications

(2 citation statements)

References 43 publications

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“…ODEO offers a more straightforward implementation for ZIP load modeling and requires significantly less time to achieve optimal solutions. A notable feature of this technique is its opposition strategy, which enables solutions to escape local minima more frequently when encountering them [25,[27][28][29][30][31], thus facilitating convergence towards global minima in a shorter time frame. Hence, the novel ODEO technique is proposed in this manuscript as a parameter-tuning algorithm considering the ZIP load model.…”

Section: Introductionmentioning

confidence: 99%

Measurement-based ZIP load modelling using opposition based differential evolution optimization

Kumar

Mallik

2023

Eng. Res. Express

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With the large integration of distributed generations, power grids are on the verge of instability if the generation and load side is not synchronized properly. To predict the load, a proper load model should be developed for the purpose of power system monitoring. The ZIP load model is considered in this work due to its simplicity. The parameter of the ZIP load corresponding to constant power, constant impedance, and constant current load must be estimated accurately for predicting the load behavior. In this paper, the measurements are being compared with the estimated value for estimating the load parameters using a suitable parameter tuning method. For ZIP load modeling Measurement-based parameter estimation is applied. In this paper, measurements are simulated by statistically infusing the Gaussian noise into the true value (obtained from the load flow study). The parameter-tuning algorithm iteratively feeds the error discovered from the comparison to determine the optimal solution. In this paper, Opposition-based differential evolution optimization (ODEO) is being proposed as a parameter tuning method for estimating the load parameters from the measurements collected at a given load bus. Measurement of the ZIP load is collected at bus 30 of the NE 39 Bus system after applying step disturbance of voltage at generator 4. The proposed ODEO method is validated and compared with the other tuning methods, like Particle Swarm optimization (PSO) and Grey Wolf optimization (GWO). The proposed ODEO method has a better performance as compared to PSO and GWO.

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Section: Introductionmentioning

confidence: 99%

Measurement-based ZIP load modelling using opposition based differential evolution optimization

Kumar

Mallik

2023

Eng. Res. Express

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“…Unlike traditional optimization algorithms, intelligent optimization algorithms have been proved to be one of the most effective methods to resolve such kind of complex engineering problems [5][6][7][8][9]. Due to its simple but robust structure, and few requirement of control parameters, DE algorithm [8] has been widely and successfully applied in the fields of clustering [10][11][12], neural networks [13][14][15], economic load dispatch [16], and so on. Although the above-mentioned extensive application results demonstrate the powerful search capability and application context of DE, it still suffers from falling into local optima [5].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Adaptive Differential Evolution Algorithm With Multi-Mutation Schemes and Weighted Control Parameter Setting

Tian,

Meng,

et al. 2023

IEEE Access

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Differential evolution (DE) algorithm is one of the most effective and efficient heuristic approaches for solving complex black box problems. But it still easily suffers from premature convergence and stagnation. To alleviate these defects, this paper presents a novel DE variant, named enhanced adaptive differential evolution algorithm with multi-mutation schemes and weighted control parameter setting (MWADE), to further strengthen its search capability. In MWADE, a multi-schemes mutation strategy is first proposed to properly exploit or explore the promising information of each individual. Herein, the whole population are dynamically grouped into three subpopulations according to their fitness values and search performance, and three different mutant operators with various search characteristics are respectively adopted for each subpopulation. Meanwhile, in order to ensure the exploration of algorithm at the later evolutionary stage, a weight-controlled parameter setting is proposed to suitably assign scale factors for different differential vectors. Moreover, a random opposition mechanism with greedy selection is introduced to avoid trapping in local optima or stagnation, and an adaptive population size reduction scheme is devised to further promote the search effectiveness of algorithm. Finally, to illustrate the performance of MWADE, thirteen typical algorithms are adopted and compared with MWADE on 30 functions from IEEE CEC 2017 test suite with different dimensions, and the effectiveness of its proposed components are also investigated. Numerical results indicate that the proposed algorithm has a better search performance.

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A novel differential evolution algorithm based on periodic intervention and systematic regulation mechanisms

Yuan,

Sun,

Deng

et al. 2024

Appl Intell

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An opposition-based differential evolution clustering algorithm for emotional preference and migratory behavior optimization

Cited by 10 publications

References 43 publications

Measurement-based ZIP load modelling using opposition based differential evolution optimization

Measurement-based ZIP load modelling using opposition based differential evolution optimization

An Enhanced Adaptive Differential Evolution Algorithm With Multi-Mutation Schemes and Weighted Control Parameter Setting

A novel differential evolution algorithm based on periodic intervention and systematic regulation mechanisms

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