Marco Antonio Perez-Cisneros

Dr. Marco Antonio Perez-Cisneros

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Research Focus & Metrics

Academic Career Length

27Years (2000-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 November 2013 in Expert Systems with Applications
Verified
Journal Article
Closed access
Erik CuevasErik Cuevas
Erik Cuevas
MCMiguel Cienfuegos
Miguel Cienfuegos
DNDaniel Zaldivar Navarro
Daniel Zaldivar Navarro
Marco Antonio Perez-CisnerosMarco Antonio Perez-Cisneros
Marco Antonio Perez-Cisneros

1 October 2020 in Expert Systems with Applications
Verified
Journal Article
Closed access
EEErick Rodriguez Esparza
Erick Rodriguez Esparza
LZLaura A. Zanella-Calzada
Laura A. Zanella-Calzada
Diego OlivaDiego Oliva
Diego Oliva
Ali Asghar HeidariAli Asghar Heidari
Ali Asghar Heidari
DZDaniel Zaldivar
Daniel Zaldivar
LFLoke Kok Foong
Loke Kok Foong

1 July 2010 in Expert Systems with Applications
Verified
Journal Article
Closed access
Erik CuevasErik Cuevas
Erik Cuevas
DNDaniel Zaldivar Navarro
Daniel Zaldivar Navarro
Marco Antonio Perez-CisnerosMarco Antonio Perez-Cisneros
Marco Antonio Perez-Cisneros

1 January 2012 in Information Sciences
Verified
Journal Article
Closed access
Erik CuevasErik Cuevas
Erik Cuevas
Diego OlivaDiego Oliva
Diego Oliva
DNDaniel Zaldivar Navarro
Daniel Zaldivar Navarro
Marco Antonio Perez-CisnerosMarco Antonio Perez-Cisneros
Marco Antonio Perez-Cisneros
Juan Humberto Sossa AzuelaJuan Humberto Sossa Azuela
Juan Humberto Sossa Azuela

23 October 2013 in Journal of Applied Mathematics
Verified
Journal Article
Open access
PDF file
Diego OlivaDiego Oliva
Diego Oliva
Erik CuevasErik Cuevas
Erik Cuevas
Gonzalo Pajares MartinsanzGonzalo Pajares Martinsanz
Gonzalo Pajares Martinsanz
DNDaniel Zaldivar Navarro
Daniel Zaldivar Navarro
Marco Antonio Perez-CisnerosMarco Antonio Perez-Cisneros
Marco Antonio Perez-Cisneros
In this paper, a multilevel thresholding (MT) algorithm based on the harmony search algorithm (HSA) is introduced. HSA is an evolutionary method which is inspired in musicians improvising new harmonies while playing. Different to other evolutionary algorithms, HSA exhibits interesting search capabilities still keeping a low computational overhead. The proposed algorithm encodes random samples from a feasible search space inside the image histogram as candidate solutions, whereas their quality is evaluated considering the objective functions that are employed by the Otsu’s or Kapur’s methods. Guided by these objective values, the set of candidate solutions are evolved through the HSA operators until an optimal solution is found. Experimental results demonstrate the high performance of the proposed method for the segmentation of digital images.

28 June 2017 in Energies
Verified
Journal Article
Open access
PDF file
Diego OlivaDiego Oliva
Diego Oliva
AEAhmed Ewees
Ahmed Ewees
Mohamed Abd ElazizMohamed Abd Elaziz
Mohamed Abd Elaziz
Abou ELLa HassanienAbou ELLa Hassanien
Abou ELLa Hassanien
Marco Antonio Perez-CisnerosMarco Antonio Perez-Cisneros
Marco Antonio Perez-Cisneros
The search for new energy resources is a crucial task nowadays. Research on the use of solar energy is growing every year. The aim is the design of devices that can produce a considerable amount of energy using the Sun’s radiation. The modeling of solar cells (SCs) is based on the estimation of the intrinsic parameters of electrical circuits that simulate their behavior based on the current vs. voltage characteristics. The problem of SC design is defined by highly nonlinear and multimodal objective functions. Most of the algorithms proposed to find the best solutions become trapped into local solutions. This paper introduces the Chaotic Improved Artificial Bee Colony (CIABC) algorithm for the estimation of SC parameters. It combines the use of chaotic maps instead random variables with the search capabilities of the Artificial Bee Colony approach. CIABC has also been modified to avoid the generation of new random solutions, preserving the information of previous iterations. In comparison with similar optimization methods, CIABC is able to find the global solution of complex and multimodal objective functions. Experimental results and comparisons prove that the proposed technique can design SCs, even with the presence of noise.