Evolving flexible beta basis function neural tree using extended genetic programming & Hybrid Artificial Bee Colony

被引：15

作者：

Bouaziz, Souhir ^{[1
]}

Dhahri, Habib ^{[1
]}

Alimi, Adel M. ^{[1
]}

Abraham, Ajith ^{[2
,3
]}

机构：

[1] Univ Sfax, REs Grp Intelligent Machines REGIM Lab, Natl Sch Engn ENIS, BP 1173, Sfax 3038, Tunisia

[2] Tech Univ Ostrava, Fac Elect Engn & Comp Sci, Ostrava, Czech Republic

[3] Machine Intelligence Res Labs, Sci Network Innovat & Res Excellence, Auburn, WA USA

来源：

APPLIED SOFT COMPUTING | 2016年 / 47卷

关键词：

Flexible beta basis function neural tree model; Extended Genetic Programming; Hybrid Artificial Bee Colony algorithm; Time-series forecasting; OPTIMIZATION; NETWORK; OPPOSITION; ALGORITHM;

D O I：

10.1016/j.asoc.2016.03.006

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a new hybrid learning algorithm is introduced to evolve the flexible beta basis function neural tree (FBBFNT). The structure is developed using the Extended Genetic Programming (EGP) and the Beta parameters and connected weights are optimized by the Hybrid Artificial Bee Colony algorithm. This hybridization is essentially based on replacing the random Artificial Bee Colony (ABC) position with the guided Opposite -based Particle Swarm Optimization (OPSO) position. Such modification can minimize the delay which might be lead by the random position, in reaching the global solution. The performance of the proposed model is evaluated for benchmark problems drawn from time series prediction area and is compared with those of related methods. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：653 / 668

页数：16

共 13 条

[1] A hybrid learning algorithm for evolving Flexible Beta Basis Function Neural Tree Model
Bouaziz, Souhir
Dhahri, Habib
Alimi, Adel M.
Abraham, Ajith
NEUROCOMPUTING, 2013, 117 : 107 - 117
[2] Extended Immune Programming and Opposite-based PSO for Evolving Flexible Beta Basis Function Neural Tree
Bouaziz, Souhir
Alimi, Adel M.
Abraham, Ajith
2013 IEEE INTERNATIONAL CONFERENCE ON CYBERNETICS (CYBCONF), 2013,
[3] Evolving Flexible Beta Basis Function Neural Tree for Nonlinear Systems
Bouaziz, S.
Alimi, A. M.
Abraham, A.
2013 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2013,
[4] Designing Beta Basis Function Neural Network for Optimization Using Artificial Bee Colony (ABC)
Dhahri, Habib
Alimi, Adel. M.
Abraham, Ajith
2012 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2012,
[5] Universal Approximation Propriety of Flexible Beta Basis Function Neural Tree
Bouaziz, S.
Alimi, Adel M.
Abraham, Ajith
PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2014, : 573 - 580
[6] GABC: A Hybrid Approach for Feature Selection Using Artificial Bee Colony and Genetic Operators
Bindu, M. G.
Sabu, M. K.
INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH, 2021, 12 (03) : 78 - 95
[7] Multi-agent Evolutionary Design of Flexible Beta Basis Function Neural Tree
Ammar, M.
Bouaziz, S.
Alimi, Adel M.
Abraham, Ajith
PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2014, : 1265 - 1271
[8] Data-driven artificial bee colony algorithm based on radial basis function neural network
Zeng, Tao
Wang, Hui
Wang, Wenjun
Ye, Tingyu
Zhang, Luqi
Zhao, Jia
INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION, 2022, 20 (01) : 1 - 10
[9] Artificial Bee Colony approach to information granulation-based fuzzy radial basis function neural networks for image fusion
Yu, Jiaqian
Duan, Haibin
OPTIK, 2013, 124 (17): : 3103 - 3111
[10] A comparative study of daily streamflow forecasting using firefly, artificial bee colony, and genetic algorithm-based artificial neural network
Kilinc, Huseyin Cagan
Haznedar, Bulent
Katipoglu, Okan Mert
Ozkan, Furkan
ACTA GEOPHYSICA, 2024, 72 (06) : 4575 - 4595

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