A Mooring System Deployment Design Methodology for Vessels at Varying Water Depths

In this paper,a methodology for designing mooring system deployment for vessels at varying water depths is proposed.The Non-dominated Sorting Genetic Algorithm-II(NSGA-II)is combined with a self-dependently developed vessel-mooring coupled program to find the optimal mooring system deployment considering both station-keeping requirements and the safety of the mooring system.Two case studies are presented to demonstrate the methodology by designing the mooring system deployments for a very large floating structure(VLFS)module and a semi-submersible platform respectively at three different water depths.It can be concluded from the obtained results that the mooring system can achieve a better station-keeping ability with relatively shorter mooring line when deployed in the shallow water.The safety factor of mooring line is mainly dominated by the maximum instantaneous tension increment in the shallow water,while the pre-tension has a decisive influence on the safety factor of the mooring line in the deep water.

System deployment optimization in architecture design

Optimization of architecture design has recently drawn research interest. System deployment optimization （SDO） refers to the process of optimizing systems that are being deployed to activi- ties. This paper first formulates a mathematical model to theorize and operationalize the SDO problem and then identifies optimal so- lutions to solve the SDO problem. In the solutions, the success rate of the combat task is maximized, whereas the execution time of the task and the cost of changes in the system structure are mini- mized. The presented optimized algorithm generates an optimal solution without the need to check the entire search space. A novel method is finally proposed based on the combination of heuristic method and genetic algorithm （HGA）, as well as the combination of heuristic method and particle swarm optimization （HPSO）. Experi- ment results show that the HPSO method generates solutions faster than particle swarm optimization （PSO） and genetic algo- rithm （GA） in terms of execution time and performs more efficiently than the heuristic method in terms of determining the best solution.