Particle Swarm Optimization Algorithm For Satellite Module Layout Optimization

The layout design of satellite module takes an important role in the satellite schematicdesign based on the public platform. In general, this layout design concerns how to locate theapparatus and equipments in the limited space of a satellite module, satisfying variousbehavioral constraints of the interior and exterior environment and optimizing theperformance indexes of layout scheme. This study has the important affections for reducingthe design period, saving the cost, improving the performance of the satellite, and so on. Inmathematics, the layout design of satellite module belongs to a combinatorial optimizationand NP-hard problem. In engineering, it belongs to a complex engineering system. The maindifficulty for solving this problem is that it is related to not only the combinatorial explosionin mathematics, but also the engineering complexity. Moreover it needs to be applied topractical engineering.Taking the project, i.e. the study and development of design and simulation system forthe satellite layout optimization, as engineering background, and being supported by theNational Nature Science Foundation of China, this dissertation studies the fast, practicallayout optimization algorithms. The main contributions are as follows:(1) A pyramid model based particle swarm optimization (PPSO) is presented, where anode of pyramid model corresponds to a subpopulation. This algorithm is used to solve thesmall-scale satellite module layout problems. Particle Swarm Optimization (PSO) is easy touse and its convergence speed is fast, but PSO is easy to fall into premature, so two improvedmethods are given out here. First, multi-population search based on pyramid model is givenout to enrich the diversity of the population. Second, mutation operation is executed by theparticles that have no ability to evolve, and its aim is to expand the search space of theparticles. Reference to coarse-grain genetic algorithm, PPSO can be taken as a kind ofcoarse-grain particle swarm optimization algorithm. The experimental results showed thatPPSO is feasible in five classic functions and two packing problems.(2) A co-evolutionary particle swarm optimization with heuristic rules (CEPSO_HR) ispresented. This algorithm is used to solve the large-scale satellite module layout problemswhich are suitable to decompose. The satellite module layout is decomposed into severalsmall-scale layout problems. Then the co-evolutionary framework is adopted, and PSO andheuristic rules for layout are integrated to solve this problem. Here, heuristic rules mainlyinclude position jumping, direction repulsion and transposition rules of objects, and they areused to improve the calculati0nal precision, efficiency and robustness of the algorithm. It isstudied the decomposition of the population, selection of the collaborative individual,collaborative evaluation of the fitness of CEPSO_HR, especially explained how to generatenew positions of objects using layout heuristic rules.At last, through the numerical experiments of the recoverable satellite and internationalcommunication satellite, it is verified of the feasibility and validity of the two algorithmspresented by this dissertation,In theory, this dissertation studies a coarse-grain particle swarm optimization algorithmand a co-evolutionary particle swarm optimization with heuristic rules. It is helpful to the further study of theory for PSO and co-evolutionary algorithm. In practice, the algorithms canafford the technology support for "the design and simulation system for the satellite layoutoptimization", and it's hoped that they can be applied to the layout design of other types ofspacecraft.