Learning backtracking search optimisation algorithm and its application

Abstract

The backtracking search algorithm (BSA) is a recently proposed evolutionary algorithm (EA) that has been used for solving optimisation problems. The structure of the algorithm is simple and has only a single control parameter that should be determined. To improve the convergence performance and extend its application domain, a new algorithm called the learning BSA (LBSA) is proposed in this paper. In this method, the globally best information of the current generation and historical information in the BSA are combined to renew individuals according to a random probability, and the remaining individuals have their positions renewed by learning knowledge from the best individual, the worst individual, and another random individual of the current generation. There are two main advantages of the algorithm. First, some individuals update their positions with the guidance of the best individual (the teacher), which makes the convergence faster, and second, learning from different individuals, especially when avoiding the worst individual, increases the diversity of the population. To test the performance of the LBSA, benchmark functions in CEC2005 and CEC2014 were tested, and the algorithm was also used to train artificial neural networks for chaotic time series prediction and nonlinear system modelling problems. To evaluate the performance of LBSA with some other EAs, several comparisons between LBSA and other classical algorithms were conducted. The results indicate that LBSA performs well with respect to other algorithms and improves the performance of BSA.