摘要
针对粒子群优化算法易出现早熟和细菌觅食算法全局优化能力差的问题,提出一种基于细菌觅食与粒子群的改进混合优化算法。建立以降低推进器功率消耗和减少各推进器的分配误差为优化目标的数学模型,约束条件包括推力分配目标的推力和扭矩限制、偏转角限制。基于改进的粒子群-细菌觅食混合算法进行推力分配优化,以算法结果的适应度值、计算消耗时间对优化算法的收敛性和实时性进行分析和比较。结果对比表明,对改进的群智能算法解决推力分配问题有一定参考价值。
An improved hybrid optimization algorithm based on bacterial feeding and particle swarm optimization is proposed to solve the problems of premature and poor global optimization ability of particle swarm optimization algorithm.Mathematical model with optimum target of total power and minimum loss of propeller is established.Restraints include thrust and torque limitation and deflection angle limitation of thrust distribution target.The thrust distribution optimization is carried out based on the improved particle swarm optimization-bacteria foraging hybrid algorithm.The convergence and real-time performance of the optimization algorithm are analyzed and compared with the fitness value of the algorithm results and the calculation time consumed.The comparison of the results shows that the improved swarm intelligence algorithm has certain reference value for solving the thrust distribution problem.
作者
李冬琴
陈文文
LI Dong-qin;CHEN Wen-wen(School of Ship and Ocean Engineering,Jiangsu University of Science and Technology,Zhenjiang 212000,China)
出处
《舰船科学技术》
北大核心
2023年第20期121-126,共6页
Ship Science and Technology
关键词
动力定位
混合优化算法
推力分配优化
dynamic positioning
hybrid optimization algorithm
thrust distribution optimization
