A hierarchical knowledge guided backtracking search algorithm with self-learning strategy

To improve the performance of the backtracking search optimization algorithm (BSA), a multi-population cooperative evolution strategy guided BSA with hierarchical knowledge (HKBSA) is proposed in this paper. According to the domain knowledge of the candidates in objective space, the population is divided into the dominant population, the ordinary population and the inferior population. The information between the sub populations has interacted with the evolution processes of the three sub-populations. The individuals in the dominant population are maintained as the optimal solutions and are utilized to guide the evolution of the other two sub-populations. A multi-strategy mutation mechanism is applied to solve non-separable problems. The distribution vector of inferior individuals is constructed by sampling, and a mechanism of the individual generation with feedback is proposed by combining self-learning strategy and elite learning strategy. The convergence of HKBSA is analyzed with the Markov model. Compared with the state-of-the-art BSA variants, HKBSA outperforms other algorithms in terms of the speed of convergence, solution accuracy and stability.

Automated summary

This paper proposes a hierarchical knowledge-based multi-population cooperative evolution strategy guided backtracking search optimization algorithm (HKBSA) to improve the performance of the BSA. By utilizing domain knowledge and a multi-strategy mutation mechanism, HKBSA achieves better convergence speed, solution accuracy, and stability compared to other BSA variants.