群智能算法在螺旋桨参数优化设计中的应用

计算机科学 ›› 2013, Vol. 40 ›› Issue (Z11): 73-76.

群智能算法在螺旋桨参数优化设计中的应用

王鹏,黄帅,朱舟全

西北工业大学航海学院西安710072;西北工业大学航海学院西安710072;西北工业大学航海学院西安710072

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受国家高技术研究发展计划(863计划)(2011AA09A104)资助

Application of Swarm Intelligence Optimization Algorithm in Parameter Optimization Design of Propeller

WANG Peng,HUANG Shuai and ZHU Zhou-quan

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 螺旋桨参数优化设计一般是复杂的非线性问题,设计的难点在于如何在各种非线性约束条件下找到一组适当的参数,使得螺旋桨性能最佳。群智能算法作为一种新兴演化计算技术,能有效解决全局优化问题,是优化算法研究的新热点。首先介绍了粒子群算法和蜂群算法两种群智能算法的工作原理；然后在建立螺旋桨参数优化数学模型的基础上,将群智能算法运用到螺旋桨初步和终结设计优化问题中,并通过实例进行对比分析,结果表明群智能算法解决螺旋桨参数优化问题是实用且高效的。

关键词: 群智能,粒子群算法,蜂群算法,螺旋桨参数优化,船舶

Abstract: In general,parameter optimization design of propeller is a nonlinear problem,and the key to the problem is how to find a set of appropriate parameters under various constraint conditions to make propeller performance best．As a novel evolutionary computation technology,swarm intelligence is now becoming a new research hotspot,and has been successfully applied in many fields．Practice shows that swarm intelligence optimization algorithm is an effective method to solve global optimization problems．In this paper,the principles of particle swarm optimization and artificial bee colony algorithm were introduced．Then on the basis of establishing mathematical model of parameter optimization design of propeller,the swarm intelligence optimization algorithm was employed to solve the problem of parameter optimization design of propeller,and the experimental results indicate that the swarm intelligence optimization algorithm is an effective and potential method for this problem.

Key words: Swarm intelligence,Particle swarm optimization,Artificial bee colony algorithm,Parameter optimization design of propeller,Shipping

王鹏,黄帅,朱舟全. 群智能算法在螺旋桨参数优化设计中的应用[J]. 计算机科学, 2013, 40(Z11): 73-76. https://doi.org/

WANG Peng,HUANG Shuai and ZHU Zhou-quan. Application of Swarm Intelligence Optimization Algorithm in Parameter Optimization Design of Propeller[J]. Computer Science, 2013, 40(Z11): 73-76. https://doi.org/

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