Genetic algorithms in bidisciplinary (aerodynamics/electro-magnetism ) optimization

The genetic algorithm(GA) is a non-traditional, probability search and global optimization method similar to natural selection and evolution. The key points and control parameters of this method are briefly discussed. To apply it to a multiobjective and multidisciplinary optimization problem a kind of fitness function is suggested, in which the requirements of multiobjects and multiconstraints are considered and the nondimensional coefficients and panalty coefficients of the constraint function are also introduced . Numerical results of bidisciplinary optimization calculation show that the present method is effective, applicable, and robust.     

Optimization of multi-revolution low-thrust transfer based on modified direct method

A modified direct optimization method is proposed to solve the optimal multi-revolution transfer with low-thrust between Earth-orbits. First, through parameterizing the control steering angles by costate variables, the search space of free parameters has been decreased. Then, in order to obtain the global optimal solution effectively and robustly, the simulated annealing and penalty function strategies were used to handle the constraints, and a GA/SQP hybrid optimization algorithm was utilized to solve the parameter optimization problem, in which, a feasible suboptimal solution obtained by GA was submitted as an initial parameter set to SQP for refinement. Comparing to the classical direct method, this novel method has fewer free parameters, needs not initial guesses, and has higher computation precision. An optimal-fuel transfer problem from LEO to GEO was taken as an example to validate the proposed approach. The results of simulation indicate that our approach is available to solve the problem of optimal muhi-revolution transfer between Earth-orbits.     

Extended Range Guided Munition Parameter Optimization Based on Genetic Algorithms

Many factors influencing range of extended range guided munition （ERGM） are analyzed. The definition domain of the most important three parameters are ascertained by preparatory mathematical simulation, the optimized mathematical model of ERGM maximum range with boundary conditions is created, and parameter optimization based on genetic algorithm （GA） is adopted. In the GA design, three-point crossover is used and the best chromosome is kept so that the convergence speed becomes rapid. Simulation result shows that GA is feasible, the result is good and it can be easy to attain global optimization solution, especially when the objective function is not the convex one for independent variables and it is a multi-parameter problem.     

Hydraulic Self Servo Swing Cylinder Structure Optimization and Dynamic Characteristics Analysis Based on Genetic Algorithm

The dynamic characteristics of hydraulic self servo swing cylinder were analyzed according to the hydraulic system natural frequency formula. Based on that, a method of the hydraulic self servo swing cylinder structure optimization based on genetic algorithm was proposed in this paper. By analyzing the four parameters that affect the dynamic characteristics, we had to optimize the structure to obtain as larger the Dm(displacement) as possible under the condition with the purpose of improving the dynamic characteristics of hydraulic self servo swing cylinder. So three state equations were established in this paper. The paper analyzed the effect of the four parameters in hydraulic self servo swing cylinder natural frequency equation and used the genetic algorithm to obtain the optimal solution of structure parameters. The model was simulated by substituting the parameters and initial value to the simulink model. Simulation results show that: using self servo hydraulic swing cylinder natural frequency equation to study its dynamic response characteristics is very effective. Compared with no optimization, the overall system dynamic response speed is significantly improved.     

Performance optimization of electric power steering based on multi-objective genetic algorithm

The vehicle model of the recirculating ball-type electric power steering (EPS) system for the pure electric bus was built. According to the features of constrained optimization for multi-variable function, a multi-objective genetic algorithm (GA) was designed. Based on the model of system, the quantitative formula of the road feel, sensitivity, and operation stability of the steering were induced. Considering the road feel and sensitivity of steering as optimization objectives, and the operation stability of steering as constraint, the multi-objective GA was proposed and the system parameters were optimized. The simulation results show that the system optimized by multi-objective genetic algorithm has better road feel, steering sensibility and steering stability. The energy of steering road feel after optimization is 1.44 times larger than the one before optimization, and the energy of portability after optimization is 0.4 times larger than the one before optimization. The ground test was conducted in order to verify the feasibility of simulation results, and it is shown that the pure electric bus equipped with the recirculating ball-type EPS system can provide better road feel and better steering portability for the drivers, thus the optimization methods can provide a theoretical basis for the design and optimization of the recirculating ball-type EPS system.     

Global optimization for ducted coaxial-rotors aircraft based on Kriging model and improved particle swarm optimization algorithm

To improve the operational efficiency of global optimization in engineering, Kriging model was established to simplify the mathematical model for calculations. Ducted coaxial-rotors aircraft was taken as an example and Fluent software was applied to the virtual prototype simulations. Through simulation sample points, the total lift of the ducted coaxial-rotors aircraft was obtained. The Kriging model was then constructed, and the function was fitted. Improved particle swarm optimization(PSO) was also utilized for the global optimization of the Kriging model of the ducted coaxial-rotors aircraft for the determination of optimized global coordinates. Finally, the optimized results were simulated by Fluent. The results show that the Kriging model and the improved PSO algorithm significantly improve the lift performance of ducted coaxial-rotors aircraft and computer operational efficiency.     

Global annealing genetic algorithm and its convergence analysis

A new selection mechanism termed global annealing selection (GAnS) is proposed for the genetic algorithm. It is proved that the GAnS genetic algorithm converges to the global optimums if and only if the parents are allowed to compete for reproduction, and that the variance of population's fitness can be used as a natural stopping criterion. Numerical simulations show that the new algorithm has stronger ability to escape from local maximum and converges more rapidly than canonical genetic algorithm.     

Fuzzy controller based on chaos optimal design and its application

In order to overcome difficulty of tuning parameters of fuzzy controller, a chaos optimal design method based on annealing strategy is proposed. First, apply the chaotic variables to search for parameters of fuzzy controller, and transform the optimal variables into chaotic variables by carrier-wave method. Making use of the intrinsic stochastic property and ergodicity of chaos movement to escape from the local minimum and direct optimization searching within global range, an approximate global optimal solution is obtained. Then, the chaos local searching and optimization based on annealing strategy are cited, the parameters are optimized again within the limits of the approximate global optimal solution, the optimization is realized by means of combination of global and partial chaos searching, which can converge quickly to global optimal value. Finally, the third order system and discrete nonlinear system are simulated and compared with traditional method of fuzzy control. The results show that the new chaos optimal design method is superior to fuzzy control method, and that the control results are of high precision, with no overshoot and fast response.     

Multi-objective optimization design of airfoil and wing

To extend available monoobjective optimization methods to multiobjective and multidisciplinary optimization, the construction of a suitable resultant objective function(in deterministic method-DM) or a fitness function(in genetic algorithm-GA) is important. An objective function combination method (OFCM) of constructing such a function for constrained optimization problems is suggested. How to use both deterministic and genetic algorithms to biobjective and bidisciplinary optimal design of high performance airfoils and wings is discussed. Numerical results in both 2D (airfoil) and 3D (wing) cases show that the present method can be used to optimaize different kinds of initial airfoils and wings. The performance of optimized shape is improved significantly. The method is successful and effective.     

Application of Improved Genetic Algorithm in Network Fault Diagnosis Expert System

Knowledge acquisition is the “botdeneck“ of building an expert system. Based on the optimization model, an improved genetic algorithm applied to knowledge acquisition of a network fault diagnostic expert system is proposed. The algorithm applies operators such as selection, crossover and mutation to evolve an initial popula-tion of diagnostic rules. Especially, a self-adaptive method is put forward to regulate the crossover rate and muta-tion rate. In the end, a knowledge acquisition problem of a simple network fault diagnostic system is simulated,the results of simulation show that the improved approach can solve the problem of convergence better.     

遗传模拟退火算法在结构优化设计中的应用

遗传算法、模拟退火算法都是随机搜索方法,在处理全局优化、离散变量、多连通可行区等困难问题中,具有传统结构优化算法不可比拟的优势.笔者针对遗传算法和模拟退火算法的特点,取长补短,结合成一种混合遗传算法—遗传模拟退火混合算法.经改进后的混合算法既发挥了遗传算法全局搜索能力强的特点,又保留了模拟退火算法局部寻优效果好的优点.     

电网故障诊断的遗传模拟退火算法研究

针对电网出现的复杂故障,如断路器和保护不正常动作或多重故障等情况,结合新的故障诊断优化模型,应用遗传模拟退火优化算法进行故障诊断,寻找使构造的目标函数最小的最优解.将遗传算法和模拟退火算法结合,有效避免了遗传算法过早收敛和模拟退火算法全局搜索较差的缺点,解决了电网故障诊断结果多解和漏解的情况,实现了电网断路器和保护不正常动作的故障诊断.     

磁性材料成型烧结生产调度优化方法及应用

建立以最小化提前和拖期时间、最小化炉重偏差为目标的混合整数线性规划模型, 解决磁性材料成型-烧结两阶段生产调度问题. 提出一种混合粒子群优化算法(HPSO)进行模型的求解,该算法采用基于订单的编码方式. 针对粒子群算法易陷入局部最优, 在迭代过程中引入模拟退火思想. 改进粒子群算法的全局极值和个体极值选取方式, 使算法尽快收敛到非劣最优解. 生产现场实际数据仿真结果表明: 该混合粒子群算法无论在求解精度, 还是求解速度上均优于普通粒子群算法和遗传算法.     

基于模拟退火遗传算法的功率域DOA估计

针对非均匀高斯白噪声背景,提出一种基于模拟退火遗传算法的功率域最小二乘波达方向（DOA）估计器。首先,介绍了阵列单通道下的信号模型。其次,给出了最小二乘意义下的功率域DOA估计优化目标函数,继而以此为适应度函数,将模拟退火算法引入基本遗传算法得到一种改进的遗传算法,对其进行全局优化,其估计精度优于基本遗传算法。最后,通过仿真结果验证了本文算法的有效性。     

考虑顾客满意度的冷链水果路径优化

针对冷链水果需求的迅速扩大及顾客满意度重要性的不断提升,提出以成本与满意度为双目标的冷链水果运输模型. 为了准确描述顾客满意度水平,提高冷链水果运输服务的响应能力,提出改进的满意度模型;引入灰度白化权函数构造顾客满意度不同等级阶段,设置不同等级分数将影响满意度感知的因素划分成不同等级,利用调研数据支撑顾客真实满意度感知. 提出改进的遗传算法（IGA）求解该冷链水果运输模型. 此遗传算法通过对“超级个体”引入模拟退火的Metropolis准则,随机选择3种邻域搜索之一定期更新染色体群,来避免传统遗传算法的快速收敛问题以及减轻优质种群被破坏程度. 基于实例的对比分析表明,改进遗传算法的求解效果优于传统遗传（GA）、遗传模拟退火算法（GA-SA）,且随着顾客人数增加,改进遗传算法优势更明显.     

一种新的模糊自适应模拟退火遗传算法在配电网重构中的应用

本文对配电网的重构问题进行了研究,提出了结合实际的配电网重构目标函数,并将遗传算法引入其中,用来解决这个复杂的,多目标,多约束的组合优化问题。针对遗传算法收敛速度慢、容易"早熟"等缺点,结合模糊推理、模拟退火算法和自适应机制,采用一种改进的遗传算法——模糊自适应模拟退火遗传算法(FASAGA),实例分析表明,该算法比标准的遗传算法(SGA)具有更快的收敛速度和寻优效果。     

单亲遗传模拟退火及在组合优化问题中的应用

基于模拟退火算法(SA)、遗传算法(GA)、 单亲遗传算法(PGA)、遗传模拟退火算法(SAGA)理论的优缺点,比照SAGA、根据SA和PGA的优势互补性,提出了一种融合SA和PGA的新算法--单亲遗传模拟退火算法(SAPGA).结合SA、PGA的优点,对PGA中每一代操作内部的基因重组操作进行了改进,同时改变了传统的降温方式、在两代操作之间加入染色体按适应度函数大小排列的过程.用3组城市数据的旅行商问题(TSP)对上述5种算法进行仿真实验,SAPGA的平均最优解始终最小,收敛所用时间始终最短.     

基于遗传模拟退火算法的钢管订购和运输优化问题求解

钢管订购和运输中的参数优化问题是个复杂的非线性规划问题.针对路费与路线长度的非线性关系、目的地的需求量及货物的未知价格等影响因素,建立了钢管订购和运输问题的二次规划模型,探讨了利用遗传算法求解该问题的方法,并在此基础上提出利用遗传算法与模拟退火算法相结合的方法对该问题进行求解.实验结果验证了利用遗传模拟退火算法求解该问题的可行性与高效性,为求解该类问题提供了一个有效的新途径.