Chicken S-BP: An Efficient Chicken Swarm Based Back-Propagation Algorithm

被引：4

作者：

Khan, Abdullah ^{[1
]}

Nawi, Nazri Mohd ^{[2
]}

Shah, Rahmat ^{[1
]}

Akhter, Nasreen ^{[1
]}

Ullah, Atta ^{[1
]}

Rehman, M. Z. ^{[2
]}

AbdulHamid, Norhamreeza ^{[2
]}

Chiroma, Haruna ^{[3
]}

机构：

[1] AUP, Inst Business & Management Sci, Peshawar, Kpk, Pakistan

[2] Univ Tun Hussein Onn Malaysia UTHM, Fac Comp Sci & Informat Technol, SCDM Ctr, Batu Pahat 86400, Johor, Malaysia

[3] UM, Fac Comp Sci & IT, Kuala Lumpur, Malaysia

来源：

RECENT ADVANCES ON SOFT COMPUTING AND DATA MINING | 2017年 / 549卷

关键词：

Global minima; Gradient descent; Back-propagation; Chicken swarm optimization; Artificial bee colony; Genetic algorithm; BACK-PROPAGATION ALGORITHM; NEURAL-NETWORKS; INSPIRED ALGORITHM; OPTIMIZATION; CLASSIFICATION; MOMENTUM;

D O I：

10.1007/978-3-319-51281-5_13

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An innovative metaheuristic based algorithm Chicken Swarm Optimization (CSO) is inspired by characteristics of chicken flock. CSO is particularly suitable for the investigation in candidate solutions for large spaces. This paper hybridize the CSO algorithm with the Back Propagation (BP) algorithm to solve the local minimum problem and to enhance convergence to global minimum in BP algorithm. The proposed Chicken Swarm Back Propagation (Chicken S-BP) is compared with the Artificial Bee Colony Back-Propagation (ABCBP), Genetic Algorithm Neural Network (GANN) and traditional BPNN algorithms. In particular Iris, Australian Credit Card, and 7-Bit Party classification datasets are used in training and testing the performance of the Chicken S-BP hybrid network. Results of simulation illustrates that Chicken S-BP algorithm efficiently prevents local minima and provides optimal solution.

引用

页码：122 / 129

页数：8

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