A learning and niching based backtracking search optimisation algorithm and its applications in global optimisation and ANN training

Abstract

A backtracking search optimisation algorithm that uses historic population information for learning was proposed recently for solving optimisation problems. However, the learning ability and the robustness of this algorithm remain relatively poor. To improve the performance of the backtracking search algorithm (BSA), a modified backtracking search optimisation algorithm (MBSA), based on learning and niching strategies, is presented in this paper. Three main strategies, a learning strategy, a niching strategy, and a mutation strategy, are incorporated into the proposed MBSA algorithm. Learning the best individual in current generation and the best position achieved so far is used to improve the convergence speed. Niching and mutation strategies are used to improve the diversity of the MBSA. Finally, some benchmark functions and three chaotic time series prediction problems based on neural networks are simulated to test the effectiveness of MBSA, and the results are compared with those obtained using some other evolutionary algorithms (EAs). The simulation results indicate that the MBSA outperforms other EAs for most functions and chaotic time series.