Adaptive Backtracking Search Algorithm for Induction Magnetometer Optimization

Backtracking search algorithm (BSA) is a novel evolutionary algorithm (EA) for solving real-valued numerical optimization problems. In this paper, an adaptive BSA (ABSA) is proposed to solve the optimization problem of an induction magnetometer (IM). In the adaptive algorithm, the probabilities of crossover and mutation are varied depending on the fitness values of the solutions to refine the convergence performance. The proposed ABSA will also be compared with basic BSA and other widely used EA algorithms. Simulation results show that ABSA is better able to solving the IM optimization problems.