Modified Monkey Search Technique Applied for Planning of Electrical Energy Distribution Systems

  • F. G. Duque
  • L. W. De Oliveira
  • E. J. De Oliveira
  • B. H. DiasEmail author
  • C. A. Moraes
Part of the Springer Tracts in Nature-Inspired Computing book series (STNIC)


This chapter presents applications of the bio-inspired metaheuristic known as monkey search (MS) to the planning of electrical energy distribution systems (EEDS). The technique is inspired by the behavior of a monkey searching for food in a jungle through movements of climbing trees. In this sense, an artificial tree has levels that contain candidate solutions for an optimization problem, in analogy with food sources at a given level of a real tree. The search in an artificial tree begins from a single candidate solution that is considered as its root at the first level. From the root, two other nodes or candidate solutions of the next level are derived through single changes, and this procedure is followed for obtaining all levels, i.e., each node in a given level is obtained from a change in another node at the previous level. The total number of levels is a parameter of the method called the height of the tree. The best candidate solutions found during the search in an artificial tree are updated and stored in an adaptive memory to aid the optimization process in finding promise routes for attractive nodes. The algorithm applied for the planning of energy distribution systems, called modified monkey search (MMS), is an improvement of the original MS method with the purpose of a better fit to the EEDS applications. The planning problems covered by the MMS application are the optimal allocation of fixed and switched capacitor banks, as well as diverse kind of meters as phasor measurement units and smart meters to aid the system state estimation process. Previous results of such applications have shown the potential and effectiveness of the MMS applied for planning EEDS.


Modified monkey search Distribution systems planning Meter allocation Optimization State estimation 



The authors gratefully acknowledge the financial support in part of CAPES—Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil, CNPq—Conselho Nacional de Desenvolvimento Científico e Tecnológico—Brasil, INERGE—Instituto Nacional de Energia Elétrica, and FAPEMIG—Fundação de Amparo à Pesquisa no Estado de Minas Gerais. The authors also express gratitude for the educational support of UFJF—Federal University of Juiz de Fora.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • F. G. Duque
    • 1
  • L. W. De Oliveira
    • 1
  • E. J. De Oliveira
    • 1
  • B. H. Dias
    • 1
    Email author
  • C. A. Moraes
    • 1
  1. 1.Department of Electrical EnergyFederal University at Juiz de Fora (UFJF)Juiz de ForaBrazil

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