An improved particle swarm optimization algorithm

被引：267

作者：

Jiang, Yan ^{[1
,2
]}

Hu, Tiesong ^{[1
]}

Huang, ChongChao ^{[3
]}

Wu, Xianing ^{[1
]}

机构：

[1] Wuhan Univ, State Key Lab Water Resource & Hydropower Engn Sc, Wuhan 430072, Peoples R China

[2] Beijing Normal Univ, Coll Water Sci, Beijing 100875, Peoples R China

[3] Wuhan Univ, Sch Math & Stat, Wuhan 430072, Peoples R China

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2007年 / 193卷 / 01期

关键词：

particle swarm optimization; improved particle swarm optimization; global optimization; hydrologic model; parameters calibration;

D O I：

10.1016/j.amc.2007.03.047

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

An improved particle swarm optimization (IPSO) is proposed in this paper. In the new algorithm, a population of points sampled randomly from the feasible space. Then the population is partitioned into several sub-swarms, each of which is made to evolve based on particle swarm optimization (PSO) algorithm. At periodic stages in the evolution, the entire population is shuffled, and then points are reassigned to sub-swarms to ensure information sharing. This method greatly elevates the ability of exploration and exploitation. Simulations for three benchmark test functions show that IPSO possesses better ability to find the global optimum than that of the standard PSO algorithm. Compared with PSO, IPSO is also applied to identify the hydrologic model. The results show that IPSO remarkably improves the calculation accuracy and is an effective global optimization to calibrate hydrologic model. (C) 2007 Elsevier Inc. All rights reserved.

引用

页码：231 / 239

页数：9

共 15 条

[1] Using selection to improve particle swarm optimization
Angeline, PJ
[J]. 1998 IEEE INTERNATIONAL CONFERENCE ON EVOLUTIONARY COMPUTATION - PROCEEDINGS, 1998, : 84 - 89
[2] [Anonymous], P GEN EV COMP C
[3] EFFECTIVE AND EFFICIENT GLOBAL OPTIMIZATION FOR CONCEPTUAL RAINFALL-RUNOFF MODELS
DUAN, QY
SOROOSHIAN, S
GUPTA, V
[J]. WATER RESOURCES RESEARCH, 1992, 28 (04) : 1015 - 1031
[4] Eberhart R., 1995, MHS 95 P 6 INT S MIC, DOI DOI 10.1109/MHS.1995.494215
[5] Particle swarm optimization with Gaussian mutation
Higashi, N
Iba, H
[J]. PROCEEDINGS OF THE 2003 IEEE SWARM INTELLIGENCE SYMPOSIUM (SIS 03), 2003, : 72 - 79
[6] Kennedy J, 1995, 1995 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS PROCEEDINGS, VOLS 1-6, P1942, DOI 10.1109/icnn.1995.488968
[7] Parsopoulos K.E., 2001, Advances in Convex Analysis and Global Optimization Nonconvex Optimization and Its Applications, V54, P445, DOI [10.1007/978- 1- 4613- 0279-7_28, DOI 10.1007/978-1-4613-0279-7_28]
[8] Parsopoulos K.E., 2002, Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, P216
[9] Parsopoulos K. E., 2001, P WORKSH PART SWARM
[10] Parallel mesh algorithms for grid graph shortest paths with application to separation of touching chromosomes
Shi, HC
Gader, P
Li, HZ
[J]. JOURNAL OF SUPERCOMPUTING, 1998, 12 (1-2) : 69 - 83

← 1 2 →