摘要
现有的比例积分微分（PID）优化设计算法难以兼顾系统对快速性、稳定性和鲁棒性的要求。为此,提出一种改进的Pareto遗传算法。该算法采用新的拥挤距离计算算法,改进非支配性的比较算法,引入双重精英机制,提高进化效率和解的质量,并且解的多样性好。将该算法应用于PID多目标优化设计,仿真结果表明,决策者可根据当前工作需求在所得的Pareto解集中选择最优的满意解。
The current Proportion Integration Differentiation（PID） optimization Design methods are often difficult to consider the system requirements for quickness,reliability and robustness.So this paper proposes an Improved Pareto Genetic Algorithm（IPGA）,which uses a new method to calculate crowding distance,improves the comparative method of non-domination,introduces double elitism mechanism to improve efficiency of evolution and quality of solution,and increases diversity of the solution.The algorithm is applied to multi-objective optimization design of PID.Simulation results indicate that a satisfactory solution is selected in Pareto optimum set according to the requirements of the present system.
出处
《计算机工程》
CAS
CSCD
北大核心
2011年第17期149-151,共3页
Computer Engineering
基金
国家自然科学基金资助项目（60501022）
关键词
最优解
遗传算法
双重精英机制
比例积分微分控制器
多目标优化
optimal solution
genetic algorithm
double elitism mechanism
Proportion Integration Differentiation（PID） controller
multi-objective optimization
