基于量子粒子群算法的船舶机舱布局方法  

A method of ship engine room layout based on quantum particle swarm algorithm

作　　者：崔奥 黄金娥 黄祥兵[1] 江杰 闵少松[1] CUI Ao;HUANG Jin-e;HUANG Xiang-bing;JIANG Jie;MIN Shao-song(Academy of Naval Architecture and Ocean,Naval University of Engineering,Wuhan 430033,China;No.92942 Unit of PLA,Beijing 100161,China)

机构地区：[1]海军工程大学舰船与海洋学院,湖北武汉430033 [2]中国人民解放军92942部队,北京100161

出　　处：《舰船科学技术》2022年第18期51-56,共6页Ship Science and Technology

基　　金：军队维修类项目(NA-J2018-02)。

摘　　要：针对运用智能算法对船舶舱室进行布局的过程中,主要研究方向聚焦于对现有智能算法的改进,但是忽略了初始解的生成问题,提出在二维空间中关于船舶机舱布局初始解的生成方法。分别利用遗传算法、粒子群算法以及量子粒子群算法对机舱设备进行布置,并对布置后的结果进行比较。结果表明,在运用智能算法求解布局问题时,运用不同初始解的生成方法,得到的智能算法优劣性不同。在本文提出的初始解生成方法前提下,相较于遗传算法和粒子群算法,量子粒子群算法具有更强的适用性。研究结果可为船舶舱室的布置和设计提供参考方案。In the process of using intelligent algorithm for ship cabin layout, the main research direction focuses on the improvement of existing intelligent algorithm, but ignores the problem of generating initial solution. Referring to the way of dividing layout space in three-dimensional packing problem, this paper proposed a method for generating initial solution of ship cabin layout in two-dimensional space. Genetic algorithm(GA), particle swarm optimization algorithm(PSO) and quantum particle swarm optimization algorithm(QPSO) were used to arrange the cabin equipment. Compared with the results referred to other research conclusions, the results showed that the advantages and disadvantages of the intelligent algorithm are different when using different initial solution generation methods to solve the layout problem. Compared with genetic algorithm and particle swarm optimization algorithm, quantum particle swarm optimization algorithm had stronger applicability. The research results could provide a reference for the layout and design of ship cabins.

关 键 词：机舱布局 启发式算法 量子粒子群算法 船舶舱室 

分 类 号：U662[交通运输工程—船舶及航道工程]