Salp Swarm Algorithm (SSA) for Training Feed-Forward Neural Networks

被引：38
作者：
Bairathi, Divya [1 ]
Gopalani, Dinesh [1 ]
机构：
[1] MNIT Jaipur, Jaipur, Rajasthan, India
来源：
SOFT COMPUTING FOR PROBLEM SOLVING, SOCPROS 2017, VOL 1 | 2019年 / 816卷
关键词：
Optimization; Metaheuristics; Salp swarm algorithm; Feed-forward neural networks-training; Classification; Regression; OPTIMIZER; DESIGN;
D O I：
10.1007/978-981-13-1592-3_41
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Artificial neural networks (ANNs) have shown efficient results in statistics and computer science applications. Feed-forward neural network (FNN) is the most popular and simplest neural network architecture, capable of solving nonlinearity. In this paper, feed-forward neural networks' weight and bias figuring using a newly proposed metaheuristic Salp Swarm Algorithm (SSA) are proposed. SSA is a swarm-based metaheuristic inspired by the navigating and foraging behaviour of salp swarm. The performance is evaluated for some of the benchmarked datasets and compared with some well-known metaheuristics.
引用
收藏
页码：521 / 534
页数：14
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