EPSO enhanced by adaptive scaling and sub-swarms

Vladimiro Miranda; Joao Vigo; Leonel Carvalho; Carolina Marcelino; Elizabeth Wanner

doi:10.1109/ISAP48318.2019.9065982

EPSO enhanced by adaptive scaling and sub-swarms

Vladimiro Miranda, Joao Vigo, Leonel Carvalho, Carolina Marcelino, Elizabeth Wanner

College of Engineering and Physical Sciences

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

This paper reports the positive results derived from adopting two variants for the EPSO - Evolutionary Particle Swarm Optimization method: variable's re-scaling and sub-swarms. Sub-swarms launched from the main swarm can be applied to intensify the search in promising regions of the space. Alternatively, the information regarding the dispersion of the particles along the search space can be used to create local landscapes with a spherical/ellipsoid form in an attempt to take advantage of the excellent convergence properties of metaheuristics for spherically-shaped optimization problems. The net improvement in reducing computing effort is observed in several unconstrained optimization problems and verified with ANOVA.

Original language	English
Title of host publication	2019 20th International Conference on Intelligent System Application to Power Systems, ISAP 2019
Publisher	IEEE
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781728131924
DOIs	https://doi.org/10.1109/ISAP48318.2019.9065982
Publication status	Published - 16 Apr 2020
Event	20th International Conference on Intelligent System Application to Power Systems, ISAP 2019 - New Delhi, India Duration: 10 Dec 2019 → 14 Dec 2019

Publication series

Name	2019 20th International Conference on Intelligent System Application to Power Systems, ISAP 2019

Conference

Conference	20th International Conference on Intelligent System Application to Power Systems, ISAP 2019
Country/Territory	India
City	New Delhi
Period	10/12/19 → 14/12/19

Bibliographical note

Funding Information:
This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme, and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project INFUSE (POCI-01-0145-FEDER-016731).

Publisher Copyright:
© 2019 IEEE.

Keywords

ANOVA
DEEPSO
EPSO
Power System State Estimation
PSO

Access to Document

10.1109/ISAP48318.2019.9065982

Cite this

Miranda, V., Vigo, J., Carvalho, L., Marcelino, C., & Wanner, E. (2020). EPSO enhanced by adaptive scaling and sub-swarms. In 2019 20th International Conference on Intelligent System Application to Power Systems, ISAP 2019 (pp. 1-6). Article 9065982 (2019 20th International Conference on Intelligent System Application to Power Systems, ISAP 2019). IEEE. https://doi.org/10.1109/ISAP48318.2019.9065982

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title = "EPSO enhanced by adaptive scaling and sub-swarms",

abstract = "This paper reports the positive results derived from adopting two variants for the EPSO - Evolutionary Particle Swarm Optimization method: variable's re-scaling and sub-swarms. Sub-swarms launched from the main swarm can be applied to intensify the search in promising regions of the space. Alternatively, the information regarding the dispersion of the particles along the search space can be used to create local landscapes with a spherical/ellipsoid form in an attempt to take advantage of the excellent convergence properties of metaheuristics for spherically-shaped optimization problems. The net improvement in reducing computing effort is observed in several unconstrained optimization problems and verified with ANOVA.",

keywords = "ANOVA, DEEPSO, EPSO, Power System State Estimation, PSO",

author = "Vladimiro Miranda and Joao Vigo and Leonel Carvalho and Carolina Marcelino and Elizabeth Wanner",

note = "Funding Information: This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme, and by National Funds through the Portuguese funding agency, FCT - Funda{\c c}{\~a}o para a Ci{\^e}ncia e a Tecnologia, within project INFUSE (POCI-01-0145-FEDER-016731). Publisher Copyright: {\textcopyright} 2019 IEEE.; 20th International Conference on Intelligent System Application to Power Systems, ISAP 2019 ; Conference date: 10-12-2019 Through 14-12-2019",

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doi = "10.1109/ISAP48318.2019.9065982",

language = "English",

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Miranda, V, Vigo, J, Carvalho, L, Marcelino, C & Wanner, E 2020, EPSO enhanced by adaptive scaling and sub-swarms. in 2019 20th International Conference on Intelligent System Application to Power Systems, ISAP 2019., 9065982, 2019 20th International Conference on Intelligent System Application to Power Systems, ISAP 2019, IEEE, pp. 1-6, 20th International Conference on Intelligent System Application to Power Systems, ISAP 2019, New Delhi, India, 10/12/19. https://doi.org/10.1109/ISAP48318.2019.9065982

EPSO enhanced by adaptive scaling and sub-swarms. / Miranda, Vladimiro; Vigo, Joao; Carvalho, Leonel et al.
2019 20th International Conference on Intelligent System Application to Power Systems, ISAP 2019. IEEE, 2020. p. 1-6 9065982 (2019 20th International Conference on Intelligent System Application to Power Systems, ISAP 2019).

Research output: Chapter in Book/Published conference output › Conference publication

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AU - Vigo, Joao

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AU - Marcelino, Carolina

AU - Wanner, Elizabeth

N1 - Funding Information: This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme, and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project INFUSE (POCI-01-0145-FEDER-016731). Publisher Copyright: © 2019 IEEE.

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N2 - This paper reports the positive results derived from adopting two variants for the EPSO - Evolutionary Particle Swarm Optimization method: variable's re-scaling and sub-swarms. Sub-swarms launched from the main swarm can be applied to intensify the search in promising regions of the space. Alternatively, the information regarding the dispersion of the particles along the search space can be used to create local landscapes with a spherical/ellipsoid form in an attempt to take advantage of the excellent convergence properties of metaheuristics for spherically-shaped optimization problems. The net improvement in reducing computing effort is observed in several unconstrained optimization problems and verified with ANOVA.

AB - This paper reports the positive results derived from adopting two variants for the EPSO - Evolutionary Particle Swarm Optimization method: variable's re-scaling and sub-swarms. Sub-swarms launched from the main swarm can be applied to intensify the search in promising regions of the space. Alternatively, the information regarding the dispersion of the particles along the search space can be used to create local landscapes with a spherical/ellipsoid form in an attempt to take advantage of the excellent convergence properties of metaheuristics for spherically-shaped optimization problems. The net improvement in reducing computing effort is observed in several unconstrained optimization problems and verified with ANOVA.

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KW - Power System State Estimation

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ER -

EPSO enhanced by adaptive scaling and sub-swarms

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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