Distributed Coevolutionary Multidisciplinary Design Optimization Methods For Flying Vehicles

The background of this dissertation is the multidisciplinary design optimization (MDO) of the space equipment system and other complex flying vehicle systems. In the design optimization of space equipment satellite systems, there are lots of discrete and integer design variables, the design space is nonconvex and even disjointed, and has multimodality. Unfortunately, current MDO procedures or strategies have difficulty to deal with discrete or integer design variables, they are very sensitive to complex design space, have propensity to converge to local optima near the starting point, and can not handle multiple objectives effectively. To overcome these difficulties, this dissertation adopt the idea of coevolution to systematically develop new multidisciplinary design optimization methods based on decomposition and coordination.A new way of decomposing and coordinating MDO problems was given. It is suitable for both single objective and multiobjective MDO problems. Proofs were made that global optimum (or global Pareto optimum) of the decomposed problem and the original MDO problem are equivalent, and the decomposed problem retains all local optima (or local Pareto optima) of the original MDO problem.By solving the decomposed problem using coevolutionary algorithms, the revolutionary MDO algorithms were formed. The basic procedure of coevolutionary MDO algorithms was given and two coevolutionary MDO algorithms were constructed, i.e. cooperate coevolutionary MDO algorithm and distributed coevolutionary MDO algorithm. Test result on four MDO test problems from the MDO Test Suite of NASA Langley Research Center MDO Branch shows that coevolutionary MDO algorithms are evidently better than other MDO methods in searching global optimal solutions, and distributed coevolutionary MDO algorithm has obvious advantage than cooperate coevolutionary MDO algorithm.An asynchronous parallel distributed coevolutionary MDO algorithm was proposed and was implemented in network distributed environment using CORBA. Not only inter disciplinary design optimization was parallelized, but also inner disciplinary design optimization was executed in parallel, thus the optimization procedure can be speeded up greatly. The asynchronous parallel distributed coevolutionary MDO algorithm also has good flexibility, scalability and fault tolerance. Test result shows that convergence performance of the asynchronous parallel version of distributed coevolutionary MDO algorithm is similar to the sequential version.By introducing distributed evolution to Deb's NSGA-II, a distributed multiobjective evolutionary algorithm was given. It was tested on five difficult multiobjective optimization test problems, and the result shows that distributed evolution does improve the performance of the algorithm. Comparing with the result of other multiobjective evolutionary algorithms reveals that the algorithm is powerful in solving multiobjective optimization problems. Furthermore, thealgorithm is very suitable for asynchronous parallel implementation in network distributed environment. On the basis of distributed multiobjective evolutionary algorithm, the nondominated sorting and crowding multiobjective handling mechanism was introduced to distributed coevolutionary MDO algorithm, and the multiobjective distributed coevolutionary MDO algorithm was formed.Above methods were applied to a missile design problem. It was also a further test to those methods. Computing result shows that: coevolutionary MDO algorithms are effective on this problem; distributed coevolutionary MDO algorithm is better than cooperate coevolutionary MDO algorithm; asynchronous parallel version of distributed coevolutionary MDO algorithm speeds up the optimization procedure greatly while maintains good convergence performance; multiobjective distributed coevolutionary MDO algorithm approximates the whole Pareto optimal front well in only one single run, saves much computing cost than constraint method to obtain Pareto optimal set, and greatly shortens search time by distri...