The Study Of Chaos Cloud Particle Swarm Optimization Algorithm And Its Application In Port Management

The port throughput forecasting is the fundamental of the port architecture optimization and infrastructure construction, it plays a important role in port planning-layout, the scale of investment in infrastructure, the development strategy and the transportation system. Berth and quay-crane are as scarce resources of the coastline, whether it can be scientific and reasonable allocation, has important practical significance in improving the efficiency of port production and the service level. In the process of the port throughput prediction and the berth and quay-crane allocation, the determination of model parameters and the solving of the berth and quay-crane allocation such as NP-hard problem have direct effect on the feasibility and effectiveness of optimization model. The intelligent algorithms provide an effective way to solve the above problems, but any kind of intelligent optimization algorithm is not perfect, it has some defects by its own structure. In order to better solve the optimization problem in port planning and operation management, a new hybrid optimization algorithm is proposed, based on the organic bond of the PSO algorithm, the Cat map and the Cloud model, the new hybrid optimization algorithm is applied in the port management, then the application of the new hybrid optimization algorithm in port throughput prediction and berth-quay-crane allocation of the port management is explored and discussed, the major work of this paper is as follow:1) The Cat map is introduced into the hybrid optimization algorithm, used for chaotic disturbance of poor individual in particle swarm, due to its better chaos characteristics. Considering the disadvantages of the poor diversity and the tendency to get trapped into local extremum and slow convergence in the late evolution stage of PSO algorithm, as well as the advantages of the ergodicity of Cat map and the randomness and stable tendency of Cloud model, this paper introduces mixed control parameter mix_gen and population distribution coefficient pop_distr to mix PSO algorithm, Cat map and Cloud model and proposes the CCPSO (Chaos Cloud Particle Swarm Optimization) algorithm, in order to take advantages of the three kinds of algorithm and improve the optimal performance. Classic test functions are selected to analysis the effect on the values of mixed control parameter mix_gen and population distribution coefficient popdistr, and the recommended mix_gen and pop_distr values are given for application to different optimizations. The performance of CCPSO in function optimization, model parameter optimization and integer programming model solving proves the effectiveness of the algorithm.2) Considering the difficulty for selection of the parameter combination, the CCPSO algorithm is used to optimize the parameter combinations of Guass-vSVR model, the Guass-vSVR-CCPSO model is proposed. The Guass-vSVR-CCPSO Model is applied to the prediction of port throughput, according to the structure of the port throughput sequence and the data of its influence factors, so as to deal with the jumping data in the sequence. The input vector of the Gauss-vSVR Model is selected by Principal Component Analysis (PCA) and correlation analysis. Then, the example analysis was made to assess the feasibility and effectiveness of the prediction model proposed in this paper.3) Considering the fact that the ships shall be close to their preferred berths when berthing can reduce the transportation distance of container truck and stay time of ships in terminal, a new berth and quay-crane allocation mode is established for diminishing the additional trucking distance incurred by failure of berthing at the preferred berths and stay time of ships in terminal.4) The CCPSO algorithm is used to solve the berth and quay-crane allocation mode, the feasible-integer processing module for particles is developed, the encoding rules of particles are established, the algorithm for the calculation method of the historical and the global extremum of particles is determined, the strategy of the Cat map global chaos disturbance and Cloud model local search for solution of berth and quay-crane allocation mode is designed, the algorithm based on the CCPSO algorithm for solving the allocation model is implemented. According to the statistical law of ship arrival at the container wharf and the technical parameters of handling equipment in the wharf, the numerical example is designed to verify the feasibility and effectiveness of proposed model and algorithm.