Study On Hybrid Chaotic Optimization Algorithm And Its Application To Underwater Motor

A hybrid intelligent optimal method based on chaotic dynamic motion is studied and applied to the underwater thruster motor of oceanic robot. The key part of the ocean robot is the thruster motor, whose performance improved is helpful to broaden oceanic exploration. Therefore, improving the performance of the thruster motor is an important project to develop oceanic resource.First, an intelligent heuristic Alopex algorithm is examined. The mathematical model of the present Alopex algorithm is improved so that the original algorithm which only solved for the minimum of a single-modal of a single variable is able to ascend to find the global optimum. Based on the convergence proofed by aid of the probability model of Bernoulli test, some parameters with regards to the convergence of algorithm and convergent speed of iterative process are discussed in details to make the algorithm feasible and effective, such as determination of the initial value of every independent variable, a positive or negative small increment of step-size away from its current position on the path toward the global optimal solution during each iteration, an efficient parameter with characteristic of temperature, as well as a stop criterion of iteration computation. This proposed optimization technology has been demonstrated on considerable mathematical functions.Second, On the basis of analysis of density distribution of sequence points on the chaotic orbit of one-dimension chaotic mapping, a mutative interval chaotic optimization algorithm is presented in search for the solution optimums of the non-linear constrained problems with multi-variables and multi-peak values. With the objective function probability measurement approaching a limit 1, the convergence of the algorithm on the global optimal solution is proofed by aid of Chebyshev inequality. The present chaotic optimal algorithm is improved. The Ulam-von Neumann mapping is introduced into the chaotic algorithm to search for the better solutions on both sides of the present optimal solution, and an attenuation coefficient is also introduced to change the size of initial adjustable coefficient so that the search interval can be shrank. Thus, the search efficiency is enhanced obviously. Sequentially, the fixed points of Logistic mapping are derived. A way to choose the initial values of 1-dimension chaotic mapping and the relationship among the number of chaos sequence points, initial adjustable constant and attenuation coefficient are investigated and illustrated.Third, the purpose of studying a hybrid optimal algorithm is to solve actual and complicated engineering problems involving optimization. The algorithm takes advantage of characteristic of Alopex algorithm and the mutative interval chaoticoptimization algorithm. The proposed algorithm consists of three processes, approximate search, descent search and subtle search. The first step of the algorithm is to define the existing domains and the direction of the optimal solutions in the population of the candidate solutions from the logistic mapping. The second step is to use Alopex algorithm to locate quickly the present optimal solutions. The third step is to employ Ulam-von Neumann chaotic mapping to search subtly in the neighboring domain of the present optimal solution. In principle, the hybrid optimal algorithm guarantees better convergence efficiency and overcomes shortage of trapping into the local minimum.Finally, the hybrid chaotic optimal algorithm, nonlinear weighted least square method for parameter identification, numerical simulation and electromagnetic field analysis are applied to the design of the underwater motor. The designed motor is tested.