Applying C-DEEPSO to Solve Large Scale Global Optimization Problems

Carolina Marcelino; Paulo Almeida; Carlos Pedreira; Leonel Caroalha; Elizabeth Wanner

doi:10.1109/CEC.2018.8477854

Applying C-DEEPSO to Solve Large Scale Global Optimization Problems

Carolina Marcelino, Paulo Almeida, Carlos Pedreira, Leonel Caroalha, Elizabeth Wanner

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

Abstract

In this paper, a hybrid single-objective metaheuristic, named as C-DEEPSO (Canonical Differential Evolutionary Particle Swarm Optimization), is proposed to solve large-scale optimization problems. C-DEEPSO can be viewed as an evolutionary algorithm with recombination rules borrowed from PSO or an swarm optimization method with selection and self-adaptiveness properties. To assess the algorithm performance, the algorithm is run over 15 benchmark continuous problems presented in CEC'2015. The algorithm is also applied over a real world large-scale problem. The results indicate that the proposed algorithm is an efficient and competitive method to handle such problems. The experimental results also show that the new approach reaches competitive results when compared to the reference algorithm DECC-G. An application of C-DEEPSO were perform to solve the electric dispatch in a large scale energy network, the IEEE 57 Bus-System. The results show that the algorithm is a good way to solve nonlinear problems respecting many constraints associated.

Original language	English
Title of host publication	2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
Publisher	IEEE
ISBN (Electronic)	9781509060177
DOIs	https://doi.org/10.1109/CEC.2018.8477854
Publication status	Published - 28 Sept 2018
Event	2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018

Conference

Conference	2018 IEEE Congress on Evolutionary Computation, CEC 2018
Country/Territory	Brazil
City	Rio de Janeiro
Period	8/07/18 → 13/07/18

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10.1109/CEC.2018.8477854

Cite this

@inproceedings{27c75bdd4f1b431e97c1e633aec1e94a,

title = "Applying C-DEEPSO to Solve Large Scale Global Optimization Problems",

abstract = "In this paper, a hybrid single-objective metaheuristic, named as C-DEEPSO (Canonical Differential Evolutionary Particle Swarm Optimization), is proposed to solve large-scale optimization problems. C-DEEPSO can be viewed as an evolutionary algorithm with recombination rules borrowed from PSO or an swarm optimization method with selection and self-adaptiveness properties. To assess the algorithm performance, the algorithm is run over 15 benchmark continuous problems presented in CEC'2015. The algorithm is also applied over a real world large-scale problem. The results indicate that the proposed algorithm is an efficient and competitive method to handle such problems. The experimental results also show that the new approach reaches competitive results when compared to the reference algorithm DECC-G. An application of C-DEEPSO were perform to solve the electric dispatch in a large scale energy network, the IEEE 57 Bus-System. The results show that the algorithm is a good way to solve nonlinear problems respecting many constraints associated.",

author = "Carolina Marcelino and Paulo Almeida and Carlos Pedreira and Leonel Caroalha and Elizabeth Wanner",

year = "2018",

month = sep,

day = "28",

doi = "10.1109/CEC.2018.8477854",

language = "English",

booktitle = "2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings",

publisher = "IEEE",

address = "United States",

note = "2018 IEEE Congress on Evolutionary Computation, CEC 2018 ; Conference date: 08-07-2018 Through 13-07-2018",

}

TY - GEN

T1 - Applying C-DEEPSO to Solve Large Scale Global Optimization Problems

AU - Marcelino, Carolina

AU - Almeida, Paulo

AU - Pedreira, Carlos

AU - Caroalha, Leonel

AU - Wanner, Elizabeth

PY - 2018/9/28

Y1 - 2018/9/28

N2 - In this paper, a hybrid single-objective metaheuristic, named as C-DEEPSO (Canonical Differential Evolutionary Particle Swarm Optimization), is proposed to solve large-scale optimization problems. C-DEEPSO can be viewed as an evolutionary algorithm with recombination rules borrowed from PSO or an swarm optimization method with selection and self-adaptiveness properties. To assess the algorithm performance, the algorithm is run over 15 benchmark continuous problems presented in CEC'2015. The algorithm is also applied over a real world large-scale problem. The results indicate that the proposed algorithm is an efficient and competitive method to handle such problems. The experimental results also show that the new approach reaches competitive results when compared to the reference algorithm DECC-G. An application of C-DEEPSO were perform to solve the electric dispatch in a large scale energy network, the IEEE 57 Bus-System. The results show that the algorithm is a good way to solve nonlinear problems respecting many constraints associated.

AB - In this paper, a hybrid single-objective metaheuristic, named as C-DEEPSO (Canonical Differential Evolutionary Particle Swarm Optimization), is proposed to solve large-scale optimization problems. C-DEEPSO can be viewed as an evolutionary algorithm with recombination rules borrowed from PSO or an swarm optimization method with selection and self-adaptiveness properties. To assess the algorithm performance, the algorithm is run over 15 benchmark continuous problems presented in CEC'2015. The algorithm is also applied over a real world large-scale problem. The results indicate that the proposed algorithm is an efficient and competitive method to handle such problems. The experimental results also show that the new approach reaches competitive results when compared to the reference algorithm DECC-G. An application of C-DEEPSO were perform to solve the electric dispatch in a large scale energy network, the IEEE 57 Bus-System. The results show that the algorithm is a good way to solve nonlinear problems respecting many constraints associated.

UR - http://www.scopus.com/inward/record.url?scp=85056265801&partnerID=8YFLogxK

UR - https://ieeexplore.ieee.org/document/8477854

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M3 - Conference publication

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BT - 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings

PB - IEEE

T2 - 2018 IEEE Congress on Evolutionary Computation, CEC 2018

Y2 - 8 July 2018 through 13 July 2018

ER -

Applying C-DEEPSO to Solve Large Scale Global Optimization Problems

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