Magnetic Dipole Target Localization Using Improved Salp Swarm Algorithm

Aiming at the problem of solving the nonlinear optimization of magnetic dipole target location, this paper proposes an improved salp swarm algorithm (ISSA) with multiple strategies. Firstly, in the initial population generation step, the tent chaotic mapping and the reverse learning are employed to increase the diversity of population individuals. Secondly, combining with Levy Flight, the leader position of salp swarm is updated which can prevent the algorithm from finding the local optimum. Finally, the quantum local search is introduced to further reduce the probability of the algorithm falling into the local optimum. The simulation results show that compared with the traditional particle swarm optimization (PSO) algorithm, gray wolf (GWO) algorithm and conventional SSA, the proposed ISSA algorithm can quickly and stably find the real target position.