IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Quantum-Behaved Bat Algorithm for Solving the Economic Load Dispatch Problem Considering a Valve-Point Effect

Quantum-Behaved Bat Algorithm for Solving the Economic Load Dispatch Problem Considering a Valve-Point Effect
View Sample PDF
Author(s): Pandian Vasant (Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia), Fahad Parvez Mahdi (University of Hyogo, Kobe, Japan), Jose Antonio Marmolejo-Saucedo (Universidad Panamericana, Facultad de Ingeniería, Ciudad de México, Mexico), Igor Litvinchev (Nuevo Leon State University, San Nicolás de los Garza, Mexico), Roman Rodriguez Aguilar (Universidad Panamericana, Escuela de Ciencias Económicas y Empresariales, Ciudad de México, Mexico) and Junzo Watada (Universiti Teknologi Petronas, Seri Iskandar, Malaysia)
Copyright: 2020
Volume: 11
Issue: 3
Pages: 17
Source title: International Journal of Applied Metaheuristic Computing (IJAMC)
Editor(s)-in-Chief: Peng-Yeng Yin (National Chi Nan University, Taiwan)
DOI: 10.4018/IJAMC.2020070102

Purchase


Abstract

Quantum computing-inspired metaheuristic algorithms have emerged as a powerful computational tool to solve nonlinear optimization problems. In this paper, a quantum-behaved bat algorithm (QBA) is implemented to solve a nonlinear economic load dispatch (ELD) problem. The objective of ELD is to find an optimal combination of power generating units in order to minimize total fuel cost of the system, while satisfying all other constraints. To make the system more applicable to the real-world problem, a valve-point effect is considered here with the ELD problem. QBA is applied in 3-unit, 10-unit, and 40-unit power generation systems for different load demands. The obtained result is then presented and compared with some well-known methods from the literature such as different versions of evolutionary programming (EP) and particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), simulated annealing (SA) and hybrid ABC_PSO. The comparison of results shows that QBA performs better than the above-mentioned methods in terms of solution quality, convergence characteristics and computational efficiency. Thus, QBA proves to be an effective and a robust technique to solve such nonlinear optimization problem.

Related Content

Hassene Faiedh, Wajdi Farhat, Sabrine Hamdi, Chokri Souani. © 2020. 22 pages.
Pankaj P. Prajapati, Mihir V. Shah. © 2020. 9 pages.
Méziane Aïder, Asma Skoudarli. © 2020. 22 pages.
Pandian Vasant, Fahad Parvez Mahdi, Jose Antonio Marmolejo-Saucedo, Igor Litvinchev, Roman Rodriguez Aguilar, Junzo Watada. © 2020. 17 pages.
Patrick Kenekayoro, Promise Mebine, Bodouowei Godswill Zipamone. © 2020. 16 pages.
Dalia Fendri, Maher Chaabene. © 2020. 12 pages.
Sana Frifita, Ines Mathlouthi, Abdelaziz Dammak. © 2020. 13 pages.
Body Bottom