Ce+Epso: A merged approach to solve SCOPF problem

  • Carolina G. Marcelino
  • , Carlos Pedreira
  • , Elizabeth F. Wanner
  • , Leonel M. Carvalho
  • , Vladimiro Miranda
  • , Armando L. Da Silva

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This work discusses the solution of a Large-scale global optimization problem named Security Constrained Optimal Power Flow (SCOPF) using a method based on Cross Entropy (CE) and Evolutionary Particle Swarm Optimization (EPSO). The obtained solution is compared to the Entropy Enhanced Covariance Matrix Adaptation Evolution Strategy (EE-CMAES) and Shrinking Net Algorithm (SNA). Experiments show the approach reaches competitive results.

Original languageEnglish
Title of host publicationGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages69-70
Number of pages2
ISBN (Electronic)9781450367486
DOIs
Publication statusPublished - 13 Jul 2019
Externally publishedYes
Event2019 Genetic and Evolutionary Computation Conference, GECCO 2019 - Prague, Czech Republic
Duration: 13 Jul 201917 Jul 2019

Publication series

NameGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2019 Genetic and Evolutionary Computation Conference, GECCO 2019
Country/TerritoryCzech Republic
CityPrague
Period13/07/1917/07/19

Bibliographical note

Publisher Copyright:
© 2019 Copyright held by the owner/author(s).

Funding

This work investigated CE+EPSO for solving SCOPF problems. CE+EPSO is single-objective metaheuristic that incorporates some features of evolutionary algorithms, swarm intelligence and cross entropy methods. The results indicated that the CE+EPSO algorithm is an efficient and competitive technique to tackle large-scale problems as SCOPF problem. The experimental results also showed that the proposed approach reached competitive results when compared to the other algorithms considered as state-of-art for solving the proposed test beds. ACKNOWLEDGMENT The authors would like to thank the CAPES, CNPq, FAPEMIG and FAPERJ for financial support. V. Miranda and L. Carvalho acknowledge funding from FCT within project UID/EEA/50014/2019.

FundersFunder number
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro
Fundação de Amparo à Pesquisa do Estado de Minas Gerais
Fundació Catalana de TrasplantamentUID/EEA/50014/2019

Keywords

  • CE+EPSO
  • LSGO
  • SCOPF

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