Chaos Ant Colony Algorithm And Its Application To Design The Deepwater Motor

A new hybrid optimum algorithm is presented in this paper, that is chaos ant colony algorithm(CACA). By using the method, good results are obtained in optimizing the deepwater thruster motor. The optimization is an application technology based on mathematics to solve different engineering optimization problems. However, more and more people pay attention to a great parallel intelligent algorithm that is needed by the practical engineering problems with complicated, constrained, multi-minimum characters. It's necessary to develop a fast effective optimum algorithm to solve motor design problem, which is a multivariable, nonlinear and complex optimum problem.In order to obtain a feasible optimum algorithm, four algorithms' theories, searching steps and features are discussed in the paper. These algorithms are Genetic Algorithm(GA), Simulated Annealing(SA), Chaos Algorithm(CA) and Ant Colony Algorithm(ACA). The advantages and disadvantages are pointed out through optimizing the test functions.Then the characters of chaos and ant colony algorithm are especially discussed. Because the slow convergence speed constrains the ACA's application to many problems, a series of improvement to the basic ACA is presented in this paper. The improved ACA is used to optimize the test functions and the magnetic pole dimensions of permanent magnet synchronous motor (PMSM). Some effect is obtained in improving convergence speed.Because of the limitation of the improved ACA, Logistic mapping and Ulam-von Neumann mapping are analyzed systematically in this paper. The intrinsic stochastic property, ergodicity, regularity and sensitivity to initial values are shown in this paper. Then a hybrid algorithm of chaos and ant colony is presented, that is chaotic ant colony algorithm. The optimum solution of test function shows that the hybrid algorithm has advantages of high precision, fast convergence and stabilization. The feasibility and utility are confirmed by the optimum example of PMSM. Under the condition of unchanged working performance, the volume and efficiency are decreased and improved respectively. After establishing objective function and constrained conditions of deepwater brushless DC motor (BLDCM), the main dimension, efficiency and volume of deepwater BLDCM are optimized by using the hybrid algorithm. The no-load magnetic field is analyzed by the finite element analysis software. The result of Fourier decomposition for air-gap flux density proves the validity of the optimization.