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/Report/Conference proceedingConference 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 languageEnglish
Title of host publication2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
PublisherIEEE
ISBN (Electronic)9781509060177
DOIs
Publication statusPublished - 28 Sep 2018
Event2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Conference

Conference2018 IEEE Congress on Evolutionary Computation, CEC 2018
CountryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

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