Quantum-Behaved PSO And Its Application To Distribution Scheme Design Of Refined Oil

This thesis mainly researches the improvements about the quantum-behaved particle swarm algorithm and proposes two improved algorithms. It also brings the theory into a practical application by combining with the designing issues of the distribution scheme of the refined oil.Particle swarm optimization is a typical representative of swarm intelligence algorithms and the quantum behavior of particles in the swarm algorithm which based on the quantum potential well of the dynamic behavior has aroused a wide attention of scholars both in the nation and abroad by its excellent performance. Now it has become a hot research in the world. However, the algorithm also belongs to the swarm intelligence algorithm and never gets rid of the disadvantage which is easy to be of premature convergence as the swarm intelligence algorithm. So, there is a remarkable significance about this algorithm of improvement both in the enriching the theory of swarm intelligence optimization and expanding the application of swarm intelligence optimization. The main contents of this thesis as follows:Firstly, in the view of the issue about the diversity of particles falling rapidly and the phenomenon that algorithm is easily to converge to local optimal solution during the iteration process of the quantum-behaved particle swarm optimization, the thesis proposes a quantum behavior particle swarm optimization algorithm based on the strategy of choice. The proposed method also uses quantum potential well as optimization mechanism, but a new method of establishing the center of the potential well is employed. In each of iteration, first, the fitness of each individual is calculated, and then the first K individuals with the greatest fitness are taken as a candidate set. Secondly, take an individual as the center of the Delta potential well through roulette select. By adjusting the other individuals move to the centre of potential well, a single-step optimization is completed. In the process of optimization, we make the K value decreases monotonically to achieve a balance of exploration and exploitation. The proposed approach is applied to the extreme optimization of the standard benchmark functions and the parameters optimization of the quantum-inspired neural network, and the experimental results show that the proposed algorithm is obviously superior to the original one.Secondly, the current quantum-behaved particle swarm algorithm doesn't have an ideal searching ability because of using the real number coding. In order to solve this issue, the thesis proposes a quantum-behaved particle swarm algorithm which uses the quantum bit coding. This algorithm establishes the search mechanism in the Bloch sphere, establishes the axis of rotation by Pauli matrix, calculates the rotation angle by using the Delta potential well model, uses quantum bit pivoting of the Bloch sphere to search, uses the Hadamard to realize the variations, to avoid the premature convergence. This algorithm can enhance the ergodicity of solution space and the probability of convergence. The experimental result shows that the algorithm's optimization ability is better than the original algorithm.Finally, according to the disadvantages about the current optimization algorithm on refined oil to distribute, the thesis researches the engineering application of the proposed algorithm in this kind of issues. The thesis uses the proposed algorithm in the practice to solve the refined oil distribution vehicle routing optimization issue in Daqing Oilfield Storage and Transportation Sales Company. The experiment result shows that the optimization result of the new algorithm is obviously better than the existing design result of the company. At the same time, the result also proves that the new algorithm is effective methods to solve this kind of issues. And this research has a certain reference value to solve similar combinatorial optimization issues in the future.