An Improved Crow Search Algorithm Applied to Energy Problems

Primitivo Díaz, Marco Pérez-Cisneros, Erik Cuevas, Omar Avalos, Jorge Gálvez, Salvador Hinojosa and Daniel Zaldivar
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Primitivo Díaz: Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, 44430 Guadalajara, Mexico
Marco Pérez-Cisneros: Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, 44430 Guadalajara, Mexico
Erik Cuevas: Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, 44430 Guadalajara, Mexico
Omar Avalos: Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, 44430 Guadalajara, Mexico
Jorge Gálvez: Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, 44430 Guadalajara, Mexico
Salvador Hinojosa: Departamento de Ingeniería del Software e Inteligencia Artificial, Facultad Informática, Universidad Complutense de Madrid, 28040 Madrid, Spain
Daniel Zaldivar: Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, 44430 Guadalajara, Mexico

Energies, 2018, vol. 11, issue 3, 1-22

Abstract: The efficient use of energy in electrical systems has become a relevant topic due to its environmental impact. Parameter identification in induction motors and capacitor allocation in distribution networks are two representative problems that have strong implications in the massive use of energy. From an optimization perspective, both problems are considered extremely complex due to their non-linearity, discontinuity, and high multi-modality. These characteristics make difficult to solve them by using standard optimization techniques. On the other hand, metaheuristic methods have been widely used as alternative optimization algorithms to solve complex engineering problems. The Crow Search Algorithm (CSA) is a recent metaheuristic method based on the intelligent group behavior of crows. Although CSA presents interesting characteristics, its search strategy presents great difficulties when it faces high multi-modal formulations. In this paper, an improved version of the CSA method is presented to solve complex optimization problems of energy. In the new algorithm, two features of the original CSA are modified: (I) the awareness probability (AP) and (II) the random perturbation. With such adaptations, the new approach preserves solution diversity and improves the convergence to difficult high multi-modal optima. In order to evaluate its performance, the proposed algorithm has been tested in a set of four optimization problems which involve induction motors and distribution networks. The results demonstrate the high performance of the proposed method when it is compared with other popular approaches.

Keywords: evolutionary computation; Crow Search Algorithm (CSA); induction motors; distribution networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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