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.     

The optimisation of space structures using evolution strategies with functional networks

In this work, the input for large space structures is created using the Formex algebra of the Formian software. The different search and optimisation algorithm known as evolution strategies (ESs) has been applied to find the optimal design of the space trusses considering the areas of the members of the space structures as discrete variables. The objective function is obtained for first few generations by using a structural analysis package such as Feast and for other generations by functional networks (FNs). Initially, to obtain the data for a functional network, a structural package such as Feast is used. The use of a functional network is motivated by time consuming repeated analyses required by evolution strategies during the optimisation process. In addition, a multilevel optimisation approach is implemented by reducing the size of the search space for individual design variables in each successive level of the optimisation process for the first example; for the remaining three examples, a functional network has been combined with evolution strategies to get away with the use of a structural analysis package and a multilevel optimisation technique. The numerical tests presented demonstrate the computational advantage of the proposed approach of ESs combined with functional networks (FNs) which become pronounced for fairly large scale optimisation problems involving about 700 degrees of freedom.     

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.     

广域光传送网逻辑拓扑的优化设计

基于波分复用的光组网技术是下一代传送网的最佳解决方案，分组业务光传送网结构设计的核心是虚拓扑的最优化问题．描述了最优虚拓扑问题的主要特点，提出了一种广域光传送网优化虚拓扑的设计原则，优化的目标是使吞吐量最大并且使延迟最小,并采用Prufer数的方法来随机产生一组可行的虚拓扑算法，给出了将虚拓扑嵌入到给定的物理网络的一种启发式算法，并使用遗传算法(GA)来优化虚拓扑，从而得到最优解．光传送网虚拓扑的设计问题在实践上非常重要，提出的方法对于实践者具有重要的指导作用．     

Automated optimal design of double-layer latticed domes using particle swarm optimization

Most of the conventional design methods of large-scale domes need deep engineering insight; furthermore, they hardly give the most economical solutions. Therefore, in this paper, a new practical design algorithm is presented to automate optimal geometry and sizing design of the latticed space domes through the idea of using parametric mathematical functions. Moreover, a simple approach is developed for the optimal sizing design of trusses with outsized number of elements. The robust technique of particle swarm optimization is employed to find the solution of the propounded optimization problem. Some numerical examples on the minimum weight design of several famous domes are provided to demonstrate the efficiency of the proposed design algorithm.     

An new immune genetic algorithm based on uniform design sampling

The deficiencies of keeping population diversity, prematurity and low success rate of searching the global optimal solution are the shortcomings of genetic algorithm (GA). Based on the bias of samples in the uniform design sampling (UDS) point set, the crossover operation in GA is redesigned. Using the concentrations of antibodies in artificial immune system (AIS), the chromosomes concentration in GA is defined and the clonal selection strategy is designed. In order to solve the maximum clique problem (MCP), an new immune GA (UIGA) is presented based on the clonal selection strategy and UDS. The simulation results show that the UIGA provides superior solution quality, convergence rate, and other various indices to those of the simple and good point GA when solving MCPs.     

Artificial immune algorithm for IIR filter design

Over the recent years, several studies have been carried out by the researchers to describe a general, flexible and powerful design method based on modern heuristic optimisation algorithms for infinite impulse response (IIR) digital filters since these algorithms have the ability of finding global optimal solution in a nonlinear search space. One of the modern heuristic algorithms is the artificial immune algorithm which implements a learning technique inspired by human immune system. However, the immune system has not attracted the same kind of interest from researchers as other heuristic algorithms. In this work, an artificial immune algorithm is described and applied to the design of IIR filters, and its performance is compared to that of genetic and touring ant colony optimisation algorithms.     

一种小波网络设计新方法

小波网络有许多优良性质，为了便于应用和推广，本文提出一种新颖的两步设计方法．首 先，用修正的GS方法与AIC相结合构造小波网络，目的是获得经济的网络结构和初始参数； 然后，采用结合GA的分层优化算法优化小波网络的两个内部参数——平移和伸缩参数，目 的是在不增加小波元的情况下获得更高的精度．最后，用于辨识非线性动态系统；仿 真结果证明了这种学习方法的可行性和有效性．     

Competitive genetic algorithms with application to reliability optimal design

Competition is introduced among the populations of a number of genetic algorithms (GAs) having different sets of parameters. The aim is to calibrate the population size of the GAs by altering the resources of the system, i.e. the allocated computing time. The co-evolution of the different populations is controlled at the level of the union of populations, i.e. the metapopulation, on the basis of statistics and trends of the evolution of every population. Evolution dynamics improve the capacity of the optimization algorithm to find optimum solutions and results in statistically better designs as compared to the standard GA with any of the fixed parameters considered. The method is applied to the reliability based optimal design of simple trusses. Numerical results are presented and the robustness of the proposed algorithm is discussed.     

Optimization of electronics component placement design on PCB using self organizing genetic algorithm (SOGA)

The optimal placement of electronic components on a printed circuit board (PCB) requires satisfying multiple conflicting design objectives as most of the components have different power dissipation, operating temperature, types of material and dimension. In addition, most electronic companies are currently emphasizing on designing a smaller package electronic system in order to increase the system performance. This paper presents a new self organizing genetic algorithm (SOGA) method for solving this multi-objective optimization problem. The SOGA can be viewed as a cascade of two GAs which consists of two steps fitness evaluation process to ensure that the fitness of selected chromosomes for each iteration process is optimally selected. The algorithm is developed based on weighted sum approach genetic algorithm (WSGA) where an inner loop GA is used to optimize the selection of weights of the WSGA. Experiments are conducted to evaluate the performance of SOGA. Four objective functions are formulated in the experiments which are temperature of components, area of PCB, high power component placement and high potential critical components distance. Comparisons of the performance of SOGA are made with two well known methods namely fixed weight GA (FWGA) and random weighted GA (RWGA). The results show that the SOGA gives a better optimal solution as compared to the other methods.     

基于状态空间模型的智能优化算法及其应用

针对城市公共交通系统中公交优化调度问题的具体特征,提出一种基于状态空间模型的实数编码智能优化算法(SIA)。SIA引入遗传算法(GA)的基本理念。通过构造状态进化矩阵来指导算法的搜索方向,再通过选种池的优胜劣汰的选择机理来实现算法朝最优解逼近。将该算法与GA分别应用到公交优化调度问题中,考虑发车时间间隔的约束,建立以企业和乘客的利益最大化为目标的数学模型。实例仿真结果表明,SIA在寻优精度和计算量方面优于GA,验证了该算法的有效性。     

On using genetic algorithms for optimum damper placement in space trusses

Although similar in some ways to the design of aircraft and other lightweight structures, the optimal design of space structures has several unique challenges. A flexible optimization system allowing for multiple analysis techniques and including continuous and discrete design variables is desired. Although not as computationally efficient as traditional optimization techniques, genetic algorithms meet this requirement.The present investigation used a genetic algorithm to place passive viscous dampers in space trusses. The flexibility of the system was demonstrated through the use of fixed and free boundary conditions. The results showed that four dampers are generally sufficient to suppress bending motion in a seventy-two-bar fixed truss and a seventy-eight-bar free truss. The results were intuitive, demonstrating the suitability of the genetic algorithm to this class of problem.     

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。     

An integrated approach for optimum design of bridge decks using genetic algorithms and artificial neural networks

The objective of this paper is to develop an integrated approach using artificial neural networks (ANN) and genetic algorithms (GA) for cost optimization of bridge deck configurations. In the present work, ANN is used to predict the structural design responses which are used further in evaluation of fitness and constraint violation in GA process. A multilayer back-propagation neural network is trained with the results obtained using grillage analysis program for different bridge deck configurations and the correlation between sectional parameters and design responses has been established. Subsequently, GA is employed for arriving at optimum configuration of the bridge deck system by minimizing the total cost. By integrating ANN with GA, the computational time required for obtaining optimal solution could be reduced substantially. The efficacy of this approach is demonstrated by carrying out studies on cost optimization of T-girder bridge deck system for different spans. The method presented in this paper, would greatly reduce the computational effort required to find the optimum solution and guarantees bridge engineers to arrive at the near-optimal solution that could not be easily obtained using general modeling programs or by trial-and-error.     

An efficient method for optimal structural design by substructuring

An optimal structural design technique incorporating the concept of substructuring in its formulation is presented. The method is developed using functional analysis techniques and the state space formulation of the optimal design problem. Design sensitivity analysis for the problem is discussed and an integrated computational algorithm for optimal design is presented. As an application of the method, optimal design of general trusses is presented. Numerical results for two standard truss structures of 25 and 200 members are obtained, using substructuring, and are compared with results obtained without substructuring. It is shown that the algorithm with substructuring is up to 66% more efficient.     

Optimum design of geometrically nonlinear space trusses

A structural optimization algorithm is developed for geometrically nonlinear three-dimensional trusses subject to displacement, stress and cross-sectional area constraints. The method is obtained by coupling the nonlinear analysis technique with the optimality criteria approach. The nonlinear behaviour of the space truss which was required for the steps of optimality criteria method was obtained by using iterative linear analysis. In each iteration the geometric stiffness matrix is constructed for the deformed structure and compensating load vector is applied to the system in order to adjust the joint displacements. During nonlinear analysis, tension members are loaded up to yield stress and compression members are stressed until their critical limits. The overall loss of elastic stability is checked throughout the steps of algorithm. The member forces resulted at the end of nonlinear analysis are used to obtain the new values of design variables for the next cycle. Number of design examples are presented to demonstrate the application of the algorithm. It is shown that the consideration of nonlinear behaviour of the space trusses in their optimum design makes it possible to achieve further reduction in the overall weight. The other advantage of the algorithm is that it takes into account the realistic behaviour of the structure, without which an optimum design might lead to erroneous result. This is noticed in one of the design example where a tension member changed into a compression one at the end of nonlinear analysis.     

Force density method for simultaneous optimization of geometry and topology of trusses

A new method of simultaneous optimization of geometry and topology is presented for plane and spatial trusses. Compliance under single loading condition is minimized for specified structural volume. The difficulties due to existence of melting nodes are successfully avoided by considering force density, which is the ratio of axial force to the member length, as design variable. By using the fact that the optimal truss is statically determinate with the same absolute value of stress in existing members, the compliance and structural volume are expressed as explicit functions of force density only. After obtaining optimal cross-sectional area, nodal locations, and topology, the cross-sectional areas and nodal coordinates are further optimized using a conventional method of nonlinear programming. Accuracy of the optimal solution is verified through examples of plane trusses and a spatial truss. It is shown that various nearly optimal solutions can be found using the proposed method.     

A PSO-based adaptive fuzzy PID-controllers

In this paper, a novel design method for determining the optimal fuzzy PID-controller parameters of active automobile suspension system using the particle swarm optimization (PSO) reinforcement evolutionary algorithm is presented. This paper demonstrated in detail how to help the PSO with Q-learning cooperation method to search efficiently the optimal fuzzy-PID controller parameters of a suspension system. The design of a fuzzy system can be formulated as a search problem in high-dimensional space where each point represents a rule set, membership functions, and the corresponding system’s behavior. In order to avoid obtaining the local optimum solution, we adopted a pure PSO global exploration method to search fuzzy-PID parameter. Later this paper explored the improved the limitation between suspension and tire deflection in active automobile suspension system with nonlinearity, which needs to be solved ride comfort and road holding ability problems, and so on. These studies presented many ideas to solve these existing problems, but they need much evolution time to obtain the solution. Motivated by above discussions this paper propose a novel algorithm which can decrease the number of evolution generation, and can also evolve the fuzzy system for obtaining a better performance.     

基于免疫遗传算法的复杂网络社区发现

针对大部分基于智能优化算法的社区发现方法存在的种群退化、寻优能力不强、计算过程复杂、需要先验知识等问题,提出了一种基于免疫遗传算法(GA)的复杂网络社区发现方法。算法将改进的字符编码和相应的遗传算子相结合,在不需要先验知识的情况下可自动获得最优社区数和社区划分方案;将免疫原理引入遗传算法的选择操作中,保持了群体多样性,改善了遗传算法所固有的退化现象;在初始化种群及交叉和变异算子中利用网络拓扑结构的局部信息,有效缩小了搜索空间,增强了寻优能力。计算机生成网络和真实网络上的仿真实验结果表明算法可自动获取最优社区数和社区划分方案并具有较高的精度,说明算法具有可行性和有效性。     

基于人工免疫多目标寻优算法的PID自整定

提出了一种基于人工免疫多目标寻优算法(AIMOA)的PID参数自适应整定的设计方法.利用生物免疫系统的免疫机理设计系统响应的目标函数,再通过AIMOA算法搜索PID控制器的优化参数组,最后将基于AIMOA算法同基于遗传算法(GA) 和齐格勒-尼柯尔斯(Zi-Ni)方法的PID自整定进行了仿真比较.结果表明:AIMOA算法具有快速收敛性,能够较快地搜索到PID参数自适应整定的最优或者次最优解,体现了算法的优越性、实用性和有效性.