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Neural Computing and Applications

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19-08-2019 | Original Article

Formulation and application of quantum-inspired tidal firefly technique for multiple-objective mixed cost-effective emission dispatch

Authors: Kapil Deo Bodha, Vinod Kumar Yadav, Vivekananda Mukherjee

Published in: Neural Computing and Applications | Issue 13/2020

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Abstract

In this manuscript, a new quantum computing-based optimization algorithm is proposed to solve multiple-objective mixed cost-effective emission dispatch (MEED) problem of electrical power system. The MEED problem aims at maintaining proper balance between emission of pollutants and generation of power. The problem has been formulated here using cubic equation to reduce the nonlinearities of the system. It is transformed to single-objective problem by considering max to max penalty factor. The proposed optimization technique is inspired by the concept of quantum mechanics, gravitational force and firefly algorithm (FA) and is termed as quantum-inspired tidal FA (QITFA). The proposed QITFA is tested on IEEE 14-bus and IEEE 30-bus test system for four different load conditions. The obtained results are compared with the results yielded by some other state-of-the-art methods like Lagrangian relaxation method, particle swarm optimization (PSO), simulated annealing, quantum-behaved bat algorithm and quantum PSO. This paper proves the superiority of the proposed QITFA over all these methods. Further, the obtained results also suggest its effective and efficient implementation in MEED problem.

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Title: Formulation and application of quantum-inspired tidal firefly technique for multiple-objective mixed cost-effective emission dispatch
Authors: Kapil Deo Bodha
Vinod Kumar Yadav
Vivekananda Mukherjee
Publication date: 19-08-2019
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 13/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-019-04433-0

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