Equilibrium Optimizer-Based Optimal Allocation of SVCs for Voltage Regulation and Loss Minimization

Joshi, Prachi; Verma, H. K.

doi:10.1007/978-981-16-1220-6_26

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…Shail Kumar Dinkar et al [25] solved Image Segmentation using Multilevel Thresholding by Oppositionbased Laplacian Equilibrium Optimizer. Joshi et al [26] used the equilibrium optimizer to optimize the allocation of the static varariable compensator to achieve voltage control and reduce loss.…”

Section: Introductionmentioning

confidence: 99%

A Modified Equilibrium Optimizer Using Opposition-Based Learning and Teaching-Learning Strategy

Wang

et al. 2022

IEEE Access

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Equilibrium Optimizer (EO) is a newly developed intelligent optimization algorithm inspired by control volume mass balance models. EO has been proven to have an excellent solution effect on some optimization problems, with the advantages of ease of implementation and strong adaptability. However, the original EO has some disadvantages when solving complex multimodal problems, including an immature balance between exploration and exploitation, the high probability of falling into local optima entrapment, and the slow rate of convergence. In order to address these shortcomings, a modified equilibrium optimizer (OTLEO) is proposed using teaching-learning-based optimization (TLBO) and opposition-based learning (OBL). These modifications aim to maintain the diversity of the solutions, expand the algorithm's search range, improve exploration and exploitation capabilities, and avoid local optima. To verify and analyze the modified equilibrium optimizer algorithm's performance, the OTLEO was tested for 32 classical benchmark functions. All algorithms are independently run 30 times in the same environment. Thereafter, the comparative evaluation against the OTLEO and other six representative algorithms is conducted. Four real-world engineering application problems, including multiple sequence alignment and so on, are used for additional validation. The experimental results, statistical tests, qualitative analysis, and stability analysis demonstrate that the proposed OTLEO outperforms the original EO and other algorithms.INDEX TERMS Equilibrium optimizer, multiple sequence alignment, opposition based learning, teaching and learning optimization algorithm.

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Section: Introductionmentioning

confidence: 99%

A Modified Equilibrium Optimizer Using Opposition-Based Learning and Teaching-Learning Strategy

Wang

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

Equilibrium Optimizer-Based Optimal Allocation of SVCs for Voltage Regulation and Loss Minimization

Cited by 1 publication

References 14 publications

A Modified Equilibrium Optimizer Using Opposition-Based Learning and Teaching-Learning Strategy

A Modified Equilibrium Optimizer Using Opposition-Based Learning and Teaching-Learning Strategy

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