The Chaotic EM Algorithm based on the L-BFGS Operator and its Application in the Path Optimization

Aim at the class electromagnetic algorithm (EM) late for “mining” ability is insufficient, solution precision is not higher, and easy in premature problem, this paper proposes a combination of chaotic map and confined quasi-Newton (L-BFGS) local optimization operator of the chaotic class electromagnetism algorithm. Its main idea is in the late class electromagnetism algorithm using limit domain quasi-Newton operator to replace the class electromagnetism algorithm local optimization operator for local search; In the algorithm, the optimization process to join the chaos mapping, using chaos mapping of the random traversal characteristics, generate new individual and jump out of local to maintain the species diversity. The simulation through the simulation comparison of three consecutive field test functions, which show that the algorithm late can effectively jump out of local optimum, the basic type of electromagnetism algorithm has obvious advantages in convergence speed, a particle swarm optimization (PSO) algorithm and acceleration coefficient changing with time of particle swarm algorithm (TVAC) in terms of solution accuracy and fast convergence is better; Through the application in the path optimization results of comparison show that the algorithm is a cellular ant colony algorithm (ACO), particle swarm algorithm can get the best path in path optimization, it has better applicability in the discrete domain problem.