Optimizing the Learning Process of Feedforward Neural Networks Using Lightning Search Algorithm

Hossam Faris, Ibrahim Aljarah, Nailah Al-Madi, Seyedali Mirjalili

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM). According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems. This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems. This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) in training Neural Network for the first time in the literature. The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers. The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed. In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets.

Original languageEnglish
Article number1650033
JournalInternational Journal on Artificial Intelligence Tools
Volume25
Issue number6
DOIs
Publication statusPublished - 1 Dec 2016
Externally publishedYes

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Keywords

  • learning
  • lightning search algorithm
  • LSA, optimization
  • metaheuristic
  • multilayer perceptron
  • neural network
  • Optimization

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