期刊
APPLIED INTELLIGENCE
卷 45, 期 2, 页码 322-332出版社
SPRINGER
DOI: 10.1007/s10489-016-0767-1
关键词
Multi-verse optimizer; MVO; Multilayer perceptron; MLP; Training neural network; Evolutionary algorithm
This paper employs the recently proposed nature-inspired algorithm called Multi-Verse Optimizer (MVO) for training the Multi-layer Perceptron (MLP) neural network. The new training approach is benchmarked and evaluated using nine different bio-medical datasets selected from the UCI machine learning repository. The results are compared to five classical and recent evolutionary metaheuristic algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), FireFly (FF) Algorithm and Cuckoo Search (CS). In addition, the results are compared with two well-regarded conventional gradient-based training methods: the conventional Back-Propagation (BP) and the Levenberg-Marquardt (LM) algorithms. The comparative study demonstrates that MVO is very competitive and outperforms other training algorithms in the majority of datasets in terms of improved local optima avoidance and convergence speed.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据