In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of sh...In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier （VLCC） ship was carried out according to the direct strength assessment method in common structural rules （CSR）, which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.展开更多
VLCC超大型油轮(Very Large Crude Carrier)的载重量可达30万吨以上,能够装载200万桶原油,其设计和制造难度在船舶工业中具有非常大的挑战性。随着船舶工业的集成化、信息化发展,大型船舶的分段制造和吊装工艺越来越成熟,大量的工艺与...VLCC超大型油轮(Very Large Crude Carrier)的载重量可达30万吨以上,能够装载200万桶原油,其设计和制造难度在船舶工业中具有非常大的挑战性。随着船舶工业的集成化、信息化发展,大型船舶的分段制造和吊装工艺越来越成熟,大量的工艺与装配信息为船舶工业的发展提供了保障。本文研究的目标是VLCC货舱区分段吊装方案自动优化设计,首先介绍VLCC船舶的特点,然后详细分析VLCC船舶的吊装工艺和流程,最后基于粒子群优化算法,研究了VLCC船舶货舱区的吊装方案自动优化。本文对于改善VLCC船舶吊装工艺、提升效率和降低成本有一定意义。展开更多
基金Supported by the Project of Ministry of Education and Finance (No.200512)the Project of the State Key Laboratory of Ocean Engineering (GKZD010053-10)
文摘In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier （VLCC） ship was carried out according to the direct strength assessment method in common structural rules （CSR）, which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.
文摘VLCC超大型油轮(Very Large Crude Carrier)的载重量可达30万吨以上,能够装载200万桶原油,其设计和制造难度在船舶工业中具有非常大的挑战性。随着船舶工业的集成化、信息化发展,大型船舶的分段制造和吊装工艺越来越成熟,大量的工艺与装配信息为船舶工业的发展提供了保障。本文研究的目标是VLCC货舱区分段吊装方案自动优化设计,首先介绍VLCC船舶的特点,然后详细分析VLCC船舶的吊装工艺和流程,最后基于粒子群优化算法,研究了VLCC船舶货舱区的吊装方案自动优化。本文对于改善VLCC船舶吊装工艺、提升效率和降低成本有一定意义。
