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Based On Quantum Particle Swarm Optimization Algorithm In The Process Of System Design And Implementation

Posted on:2012-08-08Degree:MasterType:Thesis
Country:ChinaCandidate:L M GuoFull Text:PDF
GTID:2248330395983515Subject:Computer technology
Abstract/Summary:PDF Full Text Request
University Course Scheduling Management of higher education in a very important part of this paper analyzes the various factors in curriculum issues and artificial Scheduling of simulation to determine the curriculum is an uncertainty, NP complete portfolio optimization problem. In order to effectively address the curriculum, we use quantum-based optimization and particle swarm optimization algorithms based on mixed quantum particle swarm optimization algorithm, and finally to improve on this basis, the improvements out of the new algorithm is applied to the University Course Scheduling Optimization quantum particle swarm optimization is an emerging group of intelligent optimization tools that can greatly reduce the complexity of large-scale multi-objective optimization problem the computational burden, ease of practical application, you can get many good Pareto optimal solution, and can easily to handle large multi-objective optimization problem. Arrangements for the college curriculum, the first established its mathematical model, based on the actual situation on the quantum particle swarm optimization algorithm to do a number of improvements and optimization. For example, the quantum particle algorithm, Scheduling difficult to quantify the calculations to determine the method of class scheduling conflicts, the choice of fitness function, etc., these improvements can be a good quantum particle swarm optimization algorithm to improve the convergence speed, avoidance algorithms appear immature convergence and other issues.In this paper, Scheduling System design and implementation of key technologies in the following areas:First, increase the difficulty of arranging schedule quantitative terms, the more courses arranged in a certain degree of priority to be arranged so as to enhance the efficiency of University Course Scheduling and reduced workload.Second, for the quantum particle swarm optimization algorithm for solving multi-objective optimization problems prone to premature convergence phenomenon, this paper presents an improved quantum particle swarm optimization algorithm, and verified by test functions to improve effectiveness.Third, by considering the Timetable in all aspects of real factors, and the first attempt to improve the quantum particle swarm optimization algorithm is applied to colleges and universities Automated Course Scheduling System, the experiments show that the algorithm is feasible, it is better able to optimize the university Timetable this complex optimization problems.As a global search optimization algorithm, it can effectively avoid the local optimum, but increasing the search time, this paper is to better and faster to find the optimal solution, in the process of adding a local search algorithm, which avoid the local optimum, but also accelerated the rate of global search algorithms.
Keywords/Search Tags:scheduling problem, particle swarm optimization algorithm, combinatorialoptimization, multiobjective optimization
PDF Full Text Request
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