A Hybrid Algorithm of Particle Swarm Optimization and Tabu Search for Distribution Network Reconfiguration
This paper deals with the distribution network reconfiguration problem. A hybrid algorithm of particle swarm optimization (PSO) and tabu search (TS) is proposed as the searching algorithm. The new algorithm shares the advantages of PSO and TS, which has a fast computation speed and a strong ability...
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2016-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2016/7410293 |
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doaj-b2320785588d41f29a3147a753ef36ae2020-11-25T00:34:42ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472016-01-01201610.1155/2016/74102937410293A Hybrid Algorithm of Particle Swarm Optimization and Tabu Search for Distribution Network ReconfigurationSidun Fang0Xiaochen Zhang1School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaThis paper deals with the distribution network reconfiguration problem. A hybrid algorithm of particle swarm optimization (PSO) and tabu search (TS) is proposed as the searching algorithm. The new algorithm shares the advantages of PSO and TS, which has a fast computation speed and a strong ability to avoid local optimal solution. After a thorough comparison, network random key (NRK) is introduced as the corresponding coding strategy among various tree representation strategies. NRK could completely avoid the generation of infeasible solutions during the searching process and has a good locality property, which allows the new hybrid algorithm to perform to its fullest potential. The proposed algorithm has been validated through an IEEE 33 bus test case. Compared with other algorithms, the proposed method is both accurate and computationally efficient. Furthermore, a test to solve another problem also proves the robustness of the proposed algorithm for a different problem.http://dx.doi.org/10.1155/2016/7410293 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sidun Fang Xiaochen Zhang |
spellingShingle |
Sidun Fang Xiaochen Zhang A Hybrid Algorithm of Particle Swarm Optimization and Tabu Search for Distribution Network Reconfiguration Mathematical Problems in Engineering |
author_facet |
Sidun Fang Xiaochen Zhang |
author_sort |
Sidun Fang |
title |
A Hybrid Algorithm of Particle Swarm Optimization and Tabu Search for Distribution Network Reconfiguration |
title_short |
A Hybrid Algorithm of Particle Swarm Optimization and Tabu Search for Distribution Network Reconfiguration |
title_full |
A Hybrid Algorithm of Particle Swarm Optimization and Tabu Search for Distribution Network Reconfiguration |
title_fullStr |
A Hybrid Algorithm of Particle Swarm Optimization and Tabu Search for Distribution Network Reconfiguration |
title_full_unstemmed |
A Hybrid Algorithm of Particle Swarm Optimization and Tabu Search for Distribution Network Reconfiguration |
title_sort |
hybrid algorithm of particle swarm optimization and tabu search for distribution network reconfiguration |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2016-01-01 |
description |
This paper deals with the distribution network reconfiguration problem. A hybrid algorithm of particle swarm optimization (PSO) and tabu search (TS) is proposed as the searching algorithm. The new algorithm shares the advantages of PSO and TS, which has a fast computation speed and a strong ability to avoid local optimal solution. After a thorough comparison, network random key (NRK) is introduced as the corresponding coding strategy among various tree representation strategies. NRK could completely avoid the generation of infeasible solutions during the searching process and has a good locality property, which allows the new hybrid algorithm to perform to its fullest potential. The proposed algorithm has been validated through an IEEE 33 bus test case. Compared with other algorithms, the proposed method is both accurate and computationally efficient. Furthermore, a test to solve another problem also proves the robustness of the proposed algorithm for a different problem. |
url |
http://dx.doi.org/10.1155/2016/7410293 |
work_keys_str_mv |
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