智能算法在齿轮传动优化设计的应用

以齿轮优化设计为例，分别得出用传统机械设计优化方法和用遗传算法与神经网络协同求解的结果，并进行了比较，体现了遗传算法与神经网络协同求解的特点。结果表明．该方法是非常有效的，在求解优化设计时取得了较为满意的结果。     

免疫遗传算法的混合动力汽车多目标优化

以混合动力汽车传动系统参数与控制策略参数为优化变量,以最小燃油消耗和尾气排放量（CO+HC+NOx）为优化目标,以动力性能与电池荷电状态平衡作为约束条件,建立多目标优化模型,并使用权重系数法将多目标函数优化问题转化为单目标问题。提出了基于免疫遗传算法优化混合动力汽车参数的优化方法,该算法采用实数编码,通过调用ADVISOR的后台函数,建立联合优化仿真模型。仿真结果表明,该算法可有效降低车辆的燃油消耗,减少CO与HC排放量,能够较好地解决带有约束的混合动力汽车的多目标多参数优化问题,可以获得一组具有低油耗与低污染物排放的传动系统与控制策略参数,供决策者选择。     

基于整数编码和自适应遗传算法的智能组卷算法

文章给出了一种基于整数编码和自适应遗传算法的智能组卷算法.该算法首先采用整数编码,然后用自适应遗传算法对组卷进行操作,优化了搜索过程,有效地解决了自动组卷问题,具有较好的性能和实用性.     

应用遗传算法进行阵列天线方向图综合

采用一种用于区间控制的遗传算法,该算法采用基于排序的十进制编码,并对遗传参数和遗传操作进行改进,较大地提高搜索效率,较好地克服早熟现象.它用于优化阵列天线方向图在个个给定入射波角度区间上的旁瓣电平.良好的计算实例表明遗传算法是解决此类问题的有效工具.     

基于微粒群算法的自适应ⅡR滤波器的优化设计

针对自适应ⅡR滤波器潜在的不稳定性和性能指标函数容易陷入局部极小点而导致性能下降等问题,用一种新的优化算法-微粒群算法来对自适应ⅡR滤波器进行优化设计,它不依赖于梯度信息,能够有效地实现自适应ⅡR滤波器参数的全局寻优,仿真结果表明用微粒群算法进行参数寻优优于遗传算法,不仅解决了自适应滤波器性能指标函数容易陷入局部极小点的问题,也解决了稳定性问题.     

改进的遗传算法优化二维不规则图形排样

针对大规模零件和不规则石材下料优化排样问题,提出了改进的遗传算法优化排样方法.采取二进制与十进制混合编码的策略,既克服了单独使用二进制编码时,编码串太长且操作不方便的不足,又解决了十进制编码中相近的编码方案获得的材料利用率却相去甚远的问题;通过计算矢量图形的相似度,从而对图形群体进行分类,降低了遗传算法的时间复杂度.实验结果表明,该优化排样算法在时间复杂度和空间占有率上均优于传统的遗传算法优化排样.     

基于遗传算法的螺栓连接钢构件的优化设计

简要介绍遗传算法的原理,并阐述运用遗传算法进行优化设计的流程.在保证螺栓承压、受剪承载力和钢板强度的前提下,应用遗传算法的原理进行螺栓连接钢构件的优化设计,达到降低成本的目的.算例结果表明遗传算法能成功地解决了这类构件的优化问题.     

基于蚁群算法的PID参数优化设计

蚁群算法是一种模拟进化算法,初步的研究表明该算法具有许多优良的性质.针对PID控制器参数优化设计问题,将蚁群算法设计的结果与遗传算法设计的结果进行了比较,数值仿真结果表明,蚁群算法具有一种新的模拟进化优化方法的有效性和应用价值.     

一种新的遗传算法求解有等式约束的优化问题

针对有等式约束的优化问题,提出了一种新的遗传算法.该算法是在种群初始化、交叉、变异操作过程中使用求解参数方程的方法处理等式约束,违反不等式约束的个体用死亡罚函数进行惩罚设计出的实数编码遗传算法.数值实验结果表明,新算法性能优于现有其它算法;它不仅可以处理线性等式约束,而且还可以处理非线性等式约束,同时提高了收敛速度和解的精度,是一种通用强、高效稳健的智能算法.     

基于小生境遗传算法的自动化仓库固定货架拣选优化

对自动化仓库固定货架拣选优化问题进行了描述,提出了求解该问题的小生境遗传算法.算法采用自然数编码,利用共享函数使种群呈现多样性,并且加入了局部扰动操作和改进的交叉、变异操作,提高了算法的全局寻优能力.介绍了算法的原理,对算例进行了计算,并与文献中优化结果进行了比较.对算例结果进行分析表明,该算法可以更有效地求得固定货架拣选问题的优化解,是解决该问题的有效方法.     

Stacking sequence optimization with genetic algorithm using a two-level approximation

We propose a new method for laminate stacking sequence optimization based on a two-level approximation and genetic algorithm (GA), and establish an optimization model including continuous size variables (thicknesses of plies) and discrete variables (0/1 variables that represent the existence of each ply). To solve this problem, a first-level approximate problem is constructed using the branched multipoint approximate (BMA) function. Since mixed-variables are involved in the first-level approximate problem, a new optimization strategy is introduced. The discrete variables are optimized through the GA. When calculating the fitness of each member in the population of GA, a second-level approximate problem that can be solved by the dual method is established to obtain the optimal thicknesses corresponding to the each given ply orientation sequence. The two-level approximation genetic algorithm optimization is performed starting from a ground laminate structure, which could include relatively arbitrarily discrete set of angles. The method is first applied to cylindrical laminate design examples to demonstrate its efficiency and accuracy compared with known methods. The capacity of the optimization strategy to solve more complex problems is then demonstrated using a design example. With the presented method, the stacking sequence in analytical tools can be directly taken as design variables and no intermediate variables need be adopted.     

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem

The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.     

基于混合蚁群算法的物流配送路径优化

基本蚁群算法在优化过程中存在搜索时间长、易陷入局部最优解的缺点.研究构造了一种基于蚁群算法的混合算法,利用蚁群算法首先求出问题的基本可行解,采用遗传变异中的单亲逆转算子进行再次优化,求得问题最优解.对物流配送路径优化的仿真试验表明,相对于基本蚁群算法和遗传算法,混合算法的优化质量和效率更优.     

Sugeno fuzzy PID tuning,by genetic-neutral for AVR in electrical power generation

We report a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an automatic voltage regulator (AVR) system, using a combined genetic algorithm (GA), radial basis function neural network (RBF-NN) and Sugeno fuzzy logic approaches. GA and a RBF-NN with a Sugeno fuzzy logic are proposed to design a PID controller for an AVR system (GNFPID). The problem for obtaining the optimal AVR and PID controller parameters is formulated as an optimization problem and RBF-NN tuned by GA is applied to solve the optimization problem. Whereas, optimal PID gains obtained by the proposed RBF tuning by genetic algorithm for various operating conditions are used to develop the rule base of the Sugeno fuzzy system and design fuzzy PID controller of the AVR system to improve the system's response (∼0.005 s). The proposed approach has superior features, including easy implementation, stable convergence characteristic, good computational efficiency and this algorithm effectively searches for a high-quality solution and improve the transient response of the AVR system (7E−06). Numerical simulation results demonstrate that this is faster and has much less computational cost as compared with the real-code genetic algorithm (RGA) and Sugeno fuzzy logic. The proposed method is indeed more efficient and robust in improving the step response of an AVR system.     

CMAC最优设计及其算法——GA技术优化CMAC偏移矢量分布

采用GA(Genetic Algorithm)技术实现CMAC(cerebellar Model Articulation Controller)最优设计及算法.该方法解决了CMAC与其学习对象的整体优化问题,具有理论 意义和实用价值.仿真结果证明该方法是成功的和有效的.对不同的客观对象(如空间曲面), 可以采用GA技术找到CMAc的最优内部表示(偏移矢量分布),实现一般CMAC难以达到 的精度.该方法比Albus的CMAC和Parks等的CMAC学习效果都有不同程度的提高,适 合于要求高精度学习的情况.同时给出了任意偏移矢量分布的CMAC算法.     

Comparison of evolutionary-based optimization algorithms for structural design optimization

In this paper, a comparison of evolutionary-based optimization techniques for structural design optimization problems is presented. Furthermore, a hybrid optimization technique based on differential evolution algorithm is introduced for structural design optimization problems. In order to evaluate the proposed optimization approach a welded beam design problem taken from the literature is solved. The proposed approach is applied to a welded beam design problem and the optimal design of a vehicle component to illustrate how the present approach can be applied for solving structural design optimization problems. A comparative study of six population-based optimization algorithms for optimal design of the structures is presented. The volume reduction of the vehicle component is 28.4% using the proposed hybrid approach. The results show that the proposed approach gives better solutions compared to genetic algorithm, particle swarm, immune algorithm, artificial bee colony algorithm and differential evolution algorithm that are representative of the state-of-the-art in the evolutionary optimization literature.     

Target-matching test problem for multiobjective topology optimization using genetic algorithms

This paper describes the multiobjective topology optimization of continuum structures solved as a discrete optimization problem using a multiobjective genetic algorithm (GA) with proficient constraint handling. Crucial to the effectiveness of the methodology is the use of a morphological geometry representation that defines valid structural geometries that are inherently free from checkerboard patterns, disconnected segments, or poor connectivity. A graph- theoretic chromosome encoding, together with compatible reproduction operators, helps facilitate the transmission of topological/shape characteristics across generations in the evolutionary process. A multicriterion target-matching problem developed here is a novel test problem, where a predefined target geometry is the known optimum solution, and the good results obtained in solving this problem provide a convincing demonstration and a quantitative measure of how close to the true optimum the solutions achieved by GA methods can be. The methodology is then used to successfully design a path-generating compliant mechanism by solving a multicriterion structural topology optimization problem.     

舰船补给物料搬运顺序优化问题的改进遗传算法

现有的多搬运工具可并行条件下的物料搬运顺序优化模型, 其采用的标准遗传算法收敛速度慢且易陷入局部最优. 提出了该模型的改进遗传算法, 采用精英保留策略代替传统的轮盘选择方法, 使用自适应策略设计交叉算子和变异算子. 以某一具体的舰船补给物料搬运顺序优化问题为背景, 通过实例进行了计算. 结果表明, 改进遗传算法收敛速度大大提高, 具有较高的求解质量和效率.     

用于间歇化工过程最优设计的遗传算法

间歇化工过程的最优设计问题是一类复杂且难以求解的组合优化问题。通过把这类问题分解为只包含离散变量的主导问题和只含连续变量的子问题,把遗传算法和线性规划法结合起来对其进行求解。并在算法中引入了一类新的算子,显著地提高了收敛概率、算例表明,该方法可以避免直接求解过程的复杂性和困难,并且具有很好的全局收敛性。     

Output feedback optimal guaranteed cost control of uncertain piecewise linear systems

This paper proposes the output feedback optimal guaranteed cost controller design method for uncertain piecewise linear systems based on the piecewise quadratic Lyapunov functions technique. By constructing piecewise quadratic Lyapunov functions for the closed‐loop augmented systems, the existence of the guaranteed cost controller for closed‐loop uncertain piecewise linear systems is cast as the feasibility of a set of bilinear matrix inequalities (BMIs). Some of the variables in BMIs are set to be searched by genetic algorithm (GA), then for a given chromosome corresponding to the variables in BMIs, the BMIs turn to be linear matrix inequalities (LMIs), and the corresponding non‐convex optimization problem, which minimizes the upper bound on cost function, reduces to a semidefinite programming (SDP) which is convex and can be solved numerically efficiently with the available software. Thus, the output feedback optimal guaranteed cost controller can be obtained by solving the non‐convex optimization problem using the mixed algorithm that combines GA and SDP. Numerical examples show the effectiveness of the proposed method. Copyright © 2008 John Wiley & Sons, Ltd.