Effective Self-learning Backtracking Search Optimization Algorithm

For slow convergence speedof Backtracking Search Optimization Algorithm( BSA),this paper makes some improvements on mutation operator and crossover operator on base of theoretical analysis. Firstly,a mutation operator with two-population guided form is designed,anda novel mutation scale factor based on Maxwell-Boltzmann distribution is introduced, which enhance search efficiency of mutation equation effectively. Secondly, crossover strategy is designed with self-learning property,both them enhance the performance of BSA,and numericalexperiments for testing the improved BSA are given in the end.