Abstract
In this paper, a novel hybrid single-objective metaheuristic, the so called C-DEEPSO (Canonical Differential Evolutionary Particle Swarm Optimization), is proposed and tested. C-DEEPSO can be viewed as an evolutionary algorithm with recombination rules borrowed from PSO, or a swarm optimization method with selection and self-adaptiveness properties proper from DE. A case study on the problem of optimal control for reactive sources in energy production by Wind Power Plants (WPP), solved by means of Optimal Power Flow (OPF-like), is used to test the new hybrid algorithm and to evaluate its performance. C-DEEPSO is compared to the baseline algorithm, DEEPSO, and to a reference algorithm, Mean-Variance Mapping Optimization (MVMO). The experiments indicate that the proposed algorithm is efficient and competitive, capable to tackle this large-scale problem. The results also show that the new approach exhibits better results, when compared to MVMO.
Original language | English |
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Title of host publication | 2016 IEEE Congress on Evolutionary Computation, CEC 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1547-1554 |
Number of pages | 8 |
ISBN (Electronic) | 9781509006229 |
DOIs | |
Publication status | Published - 14 Nov 2016 |
Externally published | Yes |
Event | 2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016 |
Publication series
Name | 2016 IEEE Congress on Evolutionary Computation, CEC 2016 |
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Conference
Conference | 2016 IEEE Congress on Evolutionary Computation, CEC 2016 |
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Country/Territory | Canada |
City | Vancouver |
Period | 24/07/16 → 29/07/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.