عنوان

An enhanced artificial neural network with a shuffled complex evolutionary global optimization with principal component analysis

Number

LA3nr3z92n

Title Proper

An enhanced artificial neural network with a shuffled complex evolutionary global optimization with principal component analysis

General Material Designation

[Article]

First Statement of Responsibility

Yang, T; Asanjan, AA; Faridzad, M; Hayatbini, N; Gao, X; Sorooshian, S

Text of Note

© 2017 Elsevier Inc. The classical Back-Propagation (BP) scheme with gradient-based optimization in training Artificial Neural Networks (ANNs) suffers from many drawbacks, such as the premature convergence, and the tendency of being trapped in local optimums. Therefore, as an alternative for the BP and gradient-based optimization schemes, various Evolutionary Algorithms (EAs), i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), and Differential Evolution (DE), have gained popularity in the field of ANN weight training. This study applied a new efficient and effective Shuffled Complex Evolutionary Global Optimization Algorithm with Principal Component Analysis - University of California Irvine (SP-UCI) to the weight training process of a three-layer feed-forward ANN. A large-scale numerical comparison is conducted among the SP-UCI-, PSO-, GA-, SA-, and DE-based ANNs on 17 benchmark, complex, and real-world datasets. Results show that SP-UCI-based ANN outperforms other EA-based ANNs in the context of convergence and generalization. Results suggest that the SP-UCI algorithm possesses good potential in support of the weight training of ANN in real-word problems. In addition, the suitability of different kinds of EAs on training ANN is discussed. The large-scale comparison experiments conducted in this paper are fundamental references for selecting proper ANN weight training algorithms in practice.

Date of Publication

2017

Title

UC Irvine

Electronic name

[Article]

275578

a

Y