Power Distribution Network Reconfiguration Using an Improved Sine–Cosine Algorithm-Based Meta-Heuristic Search

Raut, Usharani; Mishra, Sivkumar

doi:10.1007/978-981-13-1592-3_1

Cited by 15 publications

(4 citation statements)

References 30 publications

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“…M-SCA was applied for maximizing energy production of a row of ten turbines and results showed that M-SCA gives the highest total energy production when compared with other existing techniques. Raut and Mishra (2019) proposed an improved version of SCA (ISCA) for solving the Power Distribution Network Reconfiguration (PDNR) problem. In ISCA, the conversion parameter r 1 is defined using the nonlinear decreasing method, the simple branch exchange-based heuristic is also used to generate the initial population.…”

Section: Other Improved Sine Cosine Algorithmsmentioning

confidence: 99%

A comprehensive survey of sine cosine algorithm: variants and applications

et al. 2021

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Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions. Since its introduction by Mirjalili in 2016, SCA has attracted great attention from researchers and has been widely used to solve different optimization problems in several fields. This attention is due to its reasonable execution time, good convergence acceleration rate, and high efficiency compared to several well-regarded optimization algorithms available in the literature. This paper presents a brief overview of the basic SCA and its variants divided into modified, multi-objective, and hybridized versions. Furthermore, the applications of SCA in several domains such as classification, image processing, robot path planning, scheduling, radial distribution networks, and other engineering problems are described. Finally, the paper recommended some potential future research directions for SCA.

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Section: Other Improved Sine Cosine Algorithmsmentioning

confidence: 99%

A comprehensive survey of sine cosine algorithm: variants and applications

et al. 2021

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“…In [6], SCA was used to select features for text categorization. The author of [8] used SCA to solve a power distribution network problem. In [10] SCA was implemented to solve the problem of network model.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing energy consumption patterns of smart home based on Sine Cosine Algorithm

Youssef

Kamel

Hassan

et al. 2021

IET Generation Trans & Dist

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This paper presents a new application of the sine cosine algorithm (SCA) to obtain optimal home energy management systems (HEMS) that use the load shifting strategy of demand side management (DSM) to optimize the energy consumption patterns of a smart home. It aims to manage the load demand in an efficient way to decrease electricity bills and peak to average ratio (PAR) while maintaining user comfort through coordination among home devices. In order to meet the load demand of electricity consumers, this study schedules the load on a day-ahead and real-time basis. The main objective of the paper is to help balance the load through ON-peak hours and OFF-peak hours. Three price signals, namely: realtime pricing (RTP), time of use (TOU), and critical peak pricing (CPP), are used to evaluate the proposed algorithm. The results show that the electricity bill and PAR are minimized by up to 40% and 50%, respectively. The scheduling using coordination between devices in real-time does not significantly impact the cost of electricity and the peak to average ratio.

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“…Tawhid and Savsani 29 developed an enhanced SCA for the optimization of engineering design tasks with multiple objectives. Finally, Raut and Mishra 30 proposed an advanced SCA modification that incorporates a load flow methodology leveraging data structures to optimize power distribution network reconfiguration tasks.…”

Section: Introductionmentioning

confidence: 99%

Enhancing engineering optimization using hybrid sine cosine algorithm with Roulette wheel selection and opposition-based learning

Pham,

Nguyen Dang,

Nguyen

2024

Sci Rep

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Meta-heuristic algorithms distinguish themselves from conventional optimization methods owing to their intrinsic adaptability and straightforward implementation. Among them, the sine cosine algorithm (SCA) is lauded for its ability to transition seamlessly between exploration and exploitation phases throughout the optimization process. However, there exists potential for enhancing the balance that SCA maintains between exploration and exploitation. To augment the proficiency in global optimization of SCA, an innovative strategy—nSCA—that integrates the roulette wheel selection (RWS) with opposition-based learning was formulated. The robustness of nSCA was rigorously evaluated against leading-edge methods such as the genetic algorithm (GA), particle swarm optimization, moth-flame optimization, ant lion optimization, and multi-verse optimizer, as well as the foundational SCA. This evaluation included benchmarks set by both CEC 2019 and CEC 2021 test functions. Additionally, the performance of nSCA was confirmed through numerous practical optimization problems, emphasizing its effectiveness in applied settings. In all evaluations, nSCA consistently showcased superior performance compared to its evolutionary algorithm counterparts, delivering top-tier solutions for both benchmark functions and real-world optimization challenges. Given this compelling evidence, one can posit that nSCA serves as a strong candidate for addressing intricate optimization challenges found in real-world contexts, regardless of whether they are of a discrete or continuous nature.

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Power Distribution Network Reconfiguration Using an Improved Sine–Cosine Algorithm-Based Meta-Heuristic Search

Cited by 15 publications

References 30 publications

A comprehensive survey of sine cosine algorithm: variants and applications

A comprehensive survey of sine cosine algorithm: variants and applications

Optimizing energy consumption patterns of smart home based on Sine Cosine Algorithm

Enhancing engineering optimization using hybrid sine cosine algorithm with Roulette wheel selection and opposition-based learning

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