An Improved Genetic Algorithm for Multiobjective Optimization of Helical Coil Electromagnetic Launchers

Helical coil electromagnetic launchers (HEMLs) using motion-induced commutation strategy solve the problem of synchronization control perfectly. HEMLs have the advantages of symmetric structure, high load impedance, and high energy conversion efficiency. If the structural and launch parameters can be designed reasonably and multiobjective optimization of the velocity, efficiency, and power can be achieved, HEMLs can meet the requirements of multimission applications such as the high-velocity coilgun, electromagnetic mortar, and electromagnetic catapult. In this paper, an improved adaptive genetic algorithm (AGA) based on the solution-reservation strategy to solve the multiobjective optimization problem for HEMLs is presented. The circuit model of the HEML is established and the governing equations are derived. The circuit parameters such as projectile mass, resistance, inductance, and inductance gradient are calculated according to the structural parameters of coils. The classical Runge–Kutta method and the trapezoidal quadrature formula are used to solve the governing equations, besides deriving the velocity, efficiency, and power of the launcher. The AGA is developed in MATLAB. The range of the launch voltage U is 500–5000 V and the number of turns N in coils is 1–500. After the evolution of 14 generations, five noninferior solutions subject to the constraints of temperature rise, launch time, and length are obtained. These different HEML structures can satisfy many different launch applications.