Voltage Profile Enhancement Using Multi-Objective Sine Cosine Algorithm for Optimal Installation of DSTACOMs into Distribution Systems

Selim, Ali; Kamel, Salah

doi:10.1109/irec.2019.8754525

Cited by 7 publications

(2 citation statements)

References 21 publications

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“…In Ref. (Selim et al 2019), multi-objective based on SCA (MOSCA) using fuzzy logic decision making is proposed to find the optimal size and location of multiple Distribution STATic COMpensators (DSTATCOMs) within the distribution network for minimizing the power losses, reducing the Voltage Deviation (VD), as well as maximizing the Voltage Stability Index (VSI). The fuzzy logic shows the decision maker's preferences among the three objective functions to obtain the operating point based on ac certain membership function.…”

Section: Multi-objective-based Scamentioning

confidence: 99%

A comprehensive survey on the sine–cosine optimization algorithm

Rizk‐Allah

Hassanien

2022

Artif Intell Rev

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Metaheuristic algorithms based on intelligent rules have been successfully developed and applied to solve many optimization areas over the past few decades. The sine–cosine algorithm (SCA) imitates the behaviour of transcendental functions while the sine and cosine functions are presented to explore and exploit the search space. SCA starts by random population and executes iterative evolution processes to update the standard evolutionary algorithm’s destination or the best location. SCA used linear transition rules to balance the exploration and exploitation searches while searching for the best or optimal solutions. Since Mirjalili proposed it in 2016, SCA has attracted many researchers’ attention to deal with several optimization problems in many fields due to its strengths in solving optimization tasks that include the simple concept, easiness of implementation, and rapid convergence. This paper aims to provide researchers with a relatively comprehensive and extensive overview of the Sine–Cosine optimization algorithm in the literature to inspire further research. It examines the available publications, including improvements, binary, chaotic, hybridizations, multi-objective variants, and different applications. Some optimization formulations regarding single-objective optimization problems, multi-objective optimization problems, binary-objective optimization problems, and more classifications regarding the optimization types are discussed. An extensive bibliography is also included.

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Section: Multi-objective-based Scamentioning

confidence: 99%

A comprehensive survey on the sine–cosine optimization algorithm

Rizk‐Allah

Hassanien

2022

Artif Intell Rev

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“…Simulation results showed the better performance of MOSCA compared to Sequential Forward Selection (SFS) algorithm, Dominant Set Extraction-Based Selector (DSEBS), PSO, and basic SCA. Selim et al (2019) proposed a multi-objective SCA (MOSCA) for optimum size and location of multiple Distribution STATic COMpensators (DSTATCOMs) in radial distribution networks. The performance of MOSCA was evaluated using IEEE 33-bus and IEEE 69-bus distribution systems taken into account 3 main objective functions such as total active power losses, total voltage deviation, and voltage stability index.…”

Section: Multi-objective Sine Cosine Algorithmmentioning

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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