Economic dispatch using stochastic whale optimization algorithm

FatmaAlzahra Mohamed; Mohamed Abdel-Nasser; Karar Mahmoud; Salah Kamel

doi:10.1109/ITCE.2018.8316594

Economic dispatch using stochastic whale optimization algorithm

FatmaAlzahra Mohamed, Mohamed Abdel-Nasser, Karar Mahmoud, Salah Kamel

Not published at Aalto University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

9 Citations (Scopus)

Abstract

Economic dispatch aims to determine the optimal generated power from the generation units to meet the required load at the lowest fuel cost. In this paper, a stochastic whale optimization (SWO) method is proposed to solve the economic dispatch problem. Whale optimization algorithm is enhanced using mutation and crossover operators. To test the proposed method two systems (3 and 10 generating units) are tested. We compare the proposed SWO algorithm with whale optimization algorithm, artificial bee colony algorithm, dragonfly algorithm, ant lion algorithm, gray wolf optimization, and whale optimization algorithm with mutation only. The obtained results demonstrate the high efficiency of the proposed method compared with the other methods.

Original language	English
Title of host publication	Proceedings of 2018 International Conference on Innovative Trends in Computer Engineering, ITCE 2018
Publisher	IEEE
Pages	19-24
Number of pages	6
ISBN (Electronic)	9781538608777
DOIs	https://doi.org/10.1109/ITCE.2018.8316594
Publication status	Published - 14 Mar 2018
MoE publication type	A4 Article in a conference publication
Event	International Conference on Innovative Trends in Computer Engineering - Aswan, Egypt Duration: 19 Feb 2018 → 21 Feb 2018

Conference

Conference	International Conference on Innovative Trends in Computer Engineering
Abbreviated title	ITCE
Country	Egypt
City	Aswan
Period	19/02/2018 → 21/02/2018

Keywords

Economic Dispatch
Smart Grid
Whale Optimization Algorithm

Access to Document

10.1109/ITCE.2018.8316594

Link to publication in Scopus

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Cite this

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title = "Economic dispatch using stochastic whale optimization algorithm",

abstract = "Economic dispatch aims to determine the optimal generated power from the generation units to meet the required load at the lowest fuel cost. In this paper, a stochastic whale optimization (SWO) method is proposed to solve the economic dispatch problem. Whale optimization algorithm is enhanced using mutation and crossover operators. To test the proposed method two systems (3 and 10 generating units) are tested. We compare the proposed SWO algorithm with whale optimization algorithm, artificial bee colony algorithm, dragonfly algorithm, ant lion algorithm, gray wolf optimization, and whale optimization algorithm with mutation only. The obtained results demonstrate the high efficiency of the proposed method compared with the other methods.",

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booktitle = "Proceedings of 2018 International Conference on Innovative Trends in Computer Engineering, ITCE 2018",

publisher = "IEEE",

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Mohamed, F, Abdel-Nasser, M, Mahmoud, K & Kamel, S 2018, Economic dispatch using stochastic whale optimization algorithm. in Proceedings of 2018 International Conference on Innovative Trends in Computer Engineering, ITCE 2018. IEEE, pp. 19-24, International Conference on Innovative Trends in Computer Engineering, Aswan, Egypt, 19/02/2018. https://doi.org/10.1109/ITCE.2018.8316594

Economic dispatch using stochastic whale optimization algorithm. / Mohamed, FatmaAlzahra; Abdel-Nasser, Mohamed; Mahmoud, Karar; Kamel, Salah.

Proceedings of 2018 International Conference on Innovative Trends in Computer Engineering, ITCE 2018. IEEE, 2018. p. 19-24.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Economic dispatch using stochastic whale optimization algorithm

AU - Mohamed, FatmaAlzahra

AU - Abdel-Nasser, Mohamed

AU - Mahmoud, Karar

AU - Kamel, Salah

PY - 2018/3/14

Y1 - 2018/3/14

N2 - Economic dispatch aims to determine the optimal generated power from the generation units to meet the required load at the lowest fuel cost. In this paper, a stochastic whale optimization (SWO) method is proposed to solve the economic dispatch problem. Whale optimization algorithm is enhanced using mutation and crossover operators. To test the proposed method two systems (3 and 10 generating units) are tested. We compare the proposed SWO algorithm with whale optimization algorithm, artificial bee colony algorithm, dragonfly algorithm, ant lion algorithm, gray wolf optimization, and whale optimization algorithm with mutation only. The obtained results demonstrate the high efficiency of the proposed method compared with the other methods.

AB - Economic dispatch aims to determine the optimal generated power from the generation units to meet the required load at the lowest fuel cost. In this paper, a stochastic whale optimization (SWO) method is proposed to solve the economic dispatch problem. Whale optimization algorithm is enhanced using mutation and crossover operators. To test the proposed method two systems (3 and 10 generating units) are tested. We compare the proposed SWO algorithm with whale optimization algorithm, artificial bee colony algorithm, dragonfly algorithm, ant lion algorithm, gray wolf optimization, and whale optimization algorithm with mutation only. The obtained results demonstrate the high efficiency of the proposed method compared with the other methods.

KW - Economic Dispatch

KW - Smart Grid

KW - Whale Optimization Algorithm

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BT - Proceedings of 2018 International Conference on Innovative Trends in Computer Engineering, ITCE 2018

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