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Energies 2018, 11(3), 571;

An Improved Crow Search Algorithm Applied to Energy Problems

Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, 44430 Guadalajara, Mexico
Departamento de Ingeniería del Software e Inteligencia Artificial, Facultad Informática, Universidad Complutense de Madrid, 28040 Madrid, Spain
Authors to whom correspondence should be addressed.
Received: 2 February 2018 / Revised: 23 February 2018 / Accepted: 28 February 2018 / Published: 6 March 2018
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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. View Full-Text
Keywords: evolutionary computation; Crow Search Algorithm (CSA); induction motors; distribution networks evolutionary computation; Crow Search Algorithm (CSA); induction motors; distribution networks

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Díaz, P.; Pérez-Cisneros, M.; Cuevas, E.; Avalos, O.; Gálvez, J.; Hinojosa, S.; Zaldivar, D. An Improved Crow Search Algorithm Applied to Energy Problems. Energies 2018, 11, 571.

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