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This thesis carrys out thorough investigation and discussion to the optimum savings ratio of our country economy making use of qualitative analysis. We put forward that the really savings ratio have exceeded the optimum savings ratio needed by balanced growth of our country economy owing to a series of unbalance in economy reality.Then,we built the economic model through demonstration analysis to analyse the cause of high savings ratio according to classical west deposit theory and our country economy reality.finally,in view of the main factors bringing about the deposit problem,we pose the practical policy to resolve it.

A new hybrid algorithm is introduced into solving job shop scheduling problems, which combines knowledge evolution algorithm(KEA) and particle swarm optimization(PSO) algorithm. By the mechanism of KEA, its global search ability is fully utilized for finding the global solution. By the operating characteristic of PSO, the local search ability is also made full use. Through the combination, better convergence property is obtained for job shop scheduling with the criterion of minimization the maximum completion time (makespan). Simulation results based on well-known benchmarks and comparisons with standard genetic algorithm demonstrate the feasibility and effectiveness of the proposed hybrid algorithm.

Based on Plant Growth Simulation Algorithm, we propose an intelligence optimization algorithm for solving job shop scheduling problems. Starting with the characteristic of job shop scheduling problem, combining disjunctive graph according to the sequence of scheduling, analyzing Scheduling options that decided by exchangeable set and alternative set, by simulating the process of plant growth, and then we can determine the optimal scheduling. Simulation results based on well-known benchmarks and comparisons with standard genetic algorithm demonstrate the effectiveness of the proposed bionic algorithm.

Recommendation technology is an important technology to solve problems of information overwhelmed. However, recommendation system has many shortages such as lack of ability to discover knowledge hidden in data of users' online behaviours, poor personalized service, weak recommendation efficiency and effectiveness. In this paper, we put forward a personalized recommendation system based on multi_agent. We use JADE to build a learner agent (LA) and three types of recommendation agent (RA). RA takes advantage of rough set theory to discover pattern of user's interest based on user's online behaviors. We adopt Lucene to analyse materials, which are accessed by users, and extract information from them in order to build decision table and support RA to carry on knowledge discovery work. In the meantime, we use Lucene to build personalized retrieval function to support recommendation. The system integrates advantages of various recommended methods combining with the advantages of knowledge discovery work. Experiments based on real data show that this mechanism can recommend proper learning resources to users.

coping with such disadvantages of particle swarm optimization algorithm and GA as being easy to run into local optima, the method of cooperative optimization is proposed to solve the job shop scheduling problem by combing the quantum-behaved particle swarm optimization and GA. The algorithm applied the parallel hybrid architecture of collaborative quantum particle swarm and GA, in which a kind migration operator was designed to associate all population, and the result shows that this algorithm has better answers and more rapid convergence.

The project management optimization for an important aspect of the scheduling scheme is reasonable to reduce costs, improve quality and shorten the cycle. Traditional project scheduling and optimization methods have been unable to fully meet the rapid development of modern project management needs. The PSO (Particle Swarm Optimization) is a simulation of birds the heuristic search algorithm mechanisms, which function optimization, constrained optimization, minimax problems, such as multi-objective optimization problem. It has become an important branch of the many related optimization fields. Although the project is to optimize the scheduling, many traditional methods can achieve good results, but the particle swarm algorithm can achieve a greater degree of optimization. In this paper, research on the particle swarm optimization of the basic principles of their algorithm for the initial exploration process, compared the effectiveness simulation of particle swarm optimization and traditional genetic algorithm in optimal scheduling of the project. Therefore, the original project plan with the optimal scheduling on the basis of introduction of particle swarm optimization algorithm can get better quality, shorter cycle and fewer costs, and ultimately get the entire optimal project cycle, project quality and project cost.