Training recurrent neural networks by using parallel tabu search algorithm based on crossover operation

KALINLI A., Karaboga D.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, vol.17, no.5, pp.529-542, 2004 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 17 Issue: 5
  • Publication Date: 2004
  • Doi Number: 10.1016/j.engappai.2004.04.003
  • Journal Name: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.529-542
  • Keywords: tabu search, parallel tabu search, continuous optimisation, elman networks, system identification, GLOBAL OPTIMIZATION, GENETIC ALGORITHMS, PROP
  • Middle East Technical University Affiliated: No

Abstract

There are several heuristic optimisation techniques used for numeric optimisation problems such as genetic algorithms, neural networks, simulated annealing, ant colony and tabu search algorithms. Tabu search is a quite promising search technique for non-linear numeric problems, especially for the problems where an optimal solution must be determined on-line. However, the converging speed of the basic tabu search to the global optimum is the initial solution dependent since it is a form of iterative search. In order to overcome this drawback of basic tabu search, this paper proposes a new parallel model for the tabu search based on the crossover operator of genetic algorithms. After the performance of the proposed model was evaluated for the well-known numeric test problems, it is applied to training recurrent neural networks to identify linear and non-linear dynamic plants and the results are discussed. (C) 2004 Elsevier Ltd. All rights reserved.