基于遗传算法的静电反馈式微传感器多目标优化

讨论了一种新型梳齿式静电反馈微加速度计的多目标参数优化问题，从耦合静电场能量关系出发，建立了系统的动态模型．在此基础上，以灵敏度、分辨率和动态响应为优化目标，通过目标规划法，构建了系统的多目标优化模型．利用遗传算法进行优化求解，并与序列二次规划法的优化解进行了对比分析，研究结果表明，这2种算法均能成功地求解优化问题，但遗传算法可得到设计者更为满意的多目标有效解．研究工作为进一步利用遗传算法的随机性和潜在并行性进行系统的Pareto多目标优化提供了基础．

Multi-Objective Optimization for Electrostatic-Feedback Micro-Sensor Based on Genetic Algorithm

Multi-objective optimization for a novel electrostatic-feedback accelerometer is discussed.From the energy relations of coupled electrostatic-field,the dynamic model of the system is constructed,and a multi-objective optimization model,where the sensitivity,resolution and resonant frequency are selected as objectives,is established via goal programming method.Genetic algorithm(GA) is used to solve this problem,and comparison with a traditional optimization approach,sequence quadratic programming(SQP),is conducted.Both the two algorithms enable to achieve the aim commendably,and the GA optimal solution is more satisfied.The research provides a good foundation to develop the stochastic and implicit parallel properties of GA to gain Pareto optimal solutions.