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

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.