Fuzzy logic controller design for spacecraft proximity operations

A Genetic Algorithms (GAs) based method is presented in this paper for concurrent design of rule sets and membership functions for a fuzzy logic controllers to be used in spacecraft proximity operations. The heuristic nature of fuzzy logic makes GAs a natural candidate for logic design in which both rule sets and membership functions are optimized simultaneously. The employment of GAs natural genetic operations provides a means to search in a complex system space that is difficult to described mathematically. A one-dimensional controller for spacecraft proximity operations is implemented for examination in detail. The expension of the algorithm for a 6 DOP controller is discussed.     

Multi-objective genetic local search algorithm using Kohonen’s neural map

Genetic Algorithms (GAs) are population based global search methods that can escape from local optima traps and find the global optima regions. However, near the optimum set their intensification process is often inaccurate. This is because the search strategy of GAs is completely probabilistic. With a random search near the optimum sets, there is a small probability to improve current solution. Another drawback of the GAs is genetic drift. The GAs search process is a black box process and no one knows that which region is being searched by the algorithm and it is possible that GAs search only a small region in the feasible space. On the other hand, GAs usually do not use the existing information about the optimality regions in past iterations.In this paper, a new method called SOM-Based Multi-Objective GA (SBMOGA) is proposed to improve the genetic diversity. In SBMOGA, a grid of neurons use the concept of learning rule of Self-Organizing Map (SOM) supporting by Variable Neighborhood Search (VNS) learn from genetic algorithm improving both local and global search. SOM is a neural network which is capable of learning and can improve the efficiency of data processing algorithms. The VNS algorithm is developed to enhance the local search efficiency in the Evolutionary Algorithms (EAs). The SOM uses a multi-objective learning rule based-on Pareto dominance to train its neurons. The neurons gradually move toward better fitness areas in some trajectories in feasible space. The knowledge of optimum front in past generations is saved in form of trajectories. The final state of the neurons determines a set of new solutions that can be regarded as the probability density distribution function of the high fitness areas in the multi-objective space. The new set of solutions potentially can improve the GAs overall efficiency. In the last section of this paper, the applicability of the proposed algorithm is examined in developing optimal policies for a real world multi-objective multi-reservoir system which is a non-linear, non-convex, multi-objective optimization problem.     

Utilizing Genetic Algorithms to Optimize Membership Functions for Fuzzy Weighted Association Rules Mining

It is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for fuzzy association rules mining. In general, it is unrealistic that experts can always provide such sets. And finding the most appropriate fuzzy sets becomes a more complex problem when items are not considered to have equal importance and the support and confidence parameters required for the association rules mining process are specified as linguistic terms. Existing clustering based automated methods are not satisfactory because they do not consider the optimization of the discovered membership functions. In order to tackle this problem, we propose Genetic Algorithms (GAs) based clustering method, which dynamically adjusts the fuzzy sets to provide maximum profit based on user specified linguistic minimum support and confidence terms. This is achieved by tuning the base values of the membership functions for each quantitative attribute with respect to two different evaluation functions maximizing the number of large itemsets and the average of the confidence intervals of the generated rules. To the best of our knowledge, this is the first effort in this direction. Experiments conducted on 100 K transactions from the adult database of United States census in year 2000 demonstrate that the proposed clustering method exhibits good performance in terms of the number of produced large itemsets and interesting association rules.     

Enhancing genetic algorithms for dependent job scheduling in grid computing environments

Genetic Algorithms (GAs) are stochastic search techniques based on principles of natural selection and recombination that attempt to find optimal solutions in polynomial time by manipulating a population of candidate solutions. GAs have been widely used for job scheduling optimisation in both homogeneous and heterogeneous computing environments. When compared with list scheduling heuristics, GAs can potentially provide better solutions but require much longer processing time and significant experimentation to determine GA parameters. This paper presents a GA for scheduling dependent jobs in grid computing environments. A?number of selection and pre-selection criteria for the GA are evaluated with an aim to improve GA performance in job scheduling optimization. A?Task Matching with Data scheme is proposed as a GA mutation operator. Furthermore, the effect of the choice of heuristics for seeding the GA is investigated.     

A real-time dynamic optimal guidance scheme using a general regression neural network

This paper presents an investigation into the challenges in implementing a hard real-time optimal non-stationary system using general regression neural network (GRNN). This includes investigation into the dynamics of the problem domain, discretisation of the problem domain to reduce the computational complexity, parameters selection of the optimization algorithm, convergence guarantee for real-time solution and off-line optimization for real-time solution. In order to demonstrate these challenges, this investigation considers a real-time optimal missile guidance algorithm using GRNN to achieve an accurate interception of the maneuvering targets in three-dimension. Evolutionary Genetic Algorithms (GAs) are used to generate optimal guidance training data set for a large missile defense space to train the GRNN. The Navigation Constant of the Proportional Navigation Guidance and the target position at launching are considered for optimization using GAs. This is achieved by minimizing the miss distance and missile flight time. Finally, the merits of the proposed schemes for real-time accurate interception are presented and discussed through a set of experiments.     

基于遗传算法的工程项目资源优化

基于遗传算法建立了面向工程项目的资源优化模型。通过在模型中构造一个能反映“资源分配”和“资源均衡”两方面优化程度的适应度函数,并在复制操作中,对群中个体先进行分类再选择复制,有效地解决了多种资源的综合优化问题。给出了利用遗传算法对资源优化问题的求解设计思路,阐述了算法的实现流程,并通过实例验证了该模型的可行性。     

Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers

Type-2 fuzzy sets, which are characterized by membership functions (MFs) that are themselves fuzzy, have been attracting interest. This paper focuses on advancing the understanding of interval type-2 fuzzy logic controllers (FLCs). First, a type-2 FLC is evolved using Genetic Algorithms (GAs). The type-2 FLC is then compared with another three GA evolved type-1 FLCs that have different design parameters. The objective is to examine the amount by which the extra degrees of freedom provided by antecedent type-2 fuzzy sets is able to improve the control performance. Experimental results show that better control can be achieved using a type-2 FLC with fewer fuzzy sets/rules so one benefit of type-2 FLC is a lower trade-off between modeling accuracy and interpretability.     

Optimizing the communication distance of an ad hoc wireless sensor networks by genetic algorithms

In this paper we provide our preliminary idea of using Genetic Algorithms (GAs) to solve the ad hoc Wireless Sensor Networks (WSNs) distance optimization problem. Our objective is to minimize the communication distance over a distributed sensor network. The proposed sensor network will be autonomously divided into set of k-clusters (k is unknown) to reduce the energy consumption for the overall network. On doing this, we use GAs to specify; the location of cluster-heads, the number of clusters and the cluster-mumbers which, if chosen, will minimize the communication distance over the distributed sensor network.     

模糊寻优问题的模糊遗传算法FGA

针对模糊寻优问题，本文基于模糊集的质心概念来确定模糊集的大小，并进而提出了模糊遗传算法ＦＧＡ。算法ＦＧＡ与遗传算法ＧＡ有本制区别，能用效地找到了模糊寻优问题的近似解，本文对算法ＦＧＡ的有效性作了较深入的分析。     

基于HCM聚类的连续域模糊关联算法

针对粗糙集对于连续域属性决策表的处理能力差以及不容易获得模糊集之间关系等问题,提出一种基于连续型属性模糊关联规则约简算法。该算法引入三角隶属度函数将连续属性值转化为模糊值,并使用硬C均值聚类方法获得数据集之间关系,采用遗传算法优化该模型。仿真结果验证了该模型的有效性。     

Feature selection based on rough sets and particle swarm optimization

We propose a new feature selection strategy based on rough sets and particle swarm optimization (PSO). Rough sets have been used as a feature selection method with much success, but current hill-climbing rough set approaches to feature selection are inadequate at finding optimal reductions as no perfect heuristic can guarantee optimality. On the other hand, complete searches are not feasible for even medium-sized datasets. So, stochastic approaches provide a promising feature selection mechanism. Like Genetic Algorithms, PSO is a new evolutionary computation technique, in which each potential solution is seen as a particle with a certain velocity flying through the problem space. The Particle Swarms find optimal regions of the complex search space through the interaction of individuals in the population. PSO is attractive for feature selection in that particle swarms will discover best feature combinations as they fly within the subset space. Compared with GAs, PSO does not need complex operators such as crossover and mutation, it requires only primitive and simple mathematical operators, and is computationally inexpensive in terms of both memory and runtime. Experimentation is carried out, using UCI data, which compares the proposed algorithm with a GA-based approach and other deterministic rough set reduction algorithms. The results show that PSO is efficient for rough set-based feature selection.     

A fuzzy approach to define sample size for attributes control chart in multistage processes: An application in engine valve manufacturing process

Control charts are a basic means for monitoring the quality characteristics of processes to ensure the required quality level. Determine the sample size is a problem for attribute control charts (ACC). Kaya and Engin [I. Kaya, O. Engin, A new approach to define sample size at attributes control chart in multistage processes: an application in engine piston manufacturing process, J. Mater. Process. Technol. 183 (2007) 38–48] developed a model to determine sample size in multistage process and it was solved by Genetic Algorithms (GAs). In their model, the parameters such as defective item rates for raw materials and benches were assumed to be known exactly. But in many real world applications, these parameters may be changed very dynamically due to material, human factors or operating faults. In this study a fuzzy approach for ACC in multistage process is presented and it is solved by GAs. Formulations of this model are calculated based on acceptance sampling approach and, two main parameters are determined for every stage by GAs. These are: sample size, n, and acceptance number, c. The sample size, n, is suggested for ACC. The main contributions of this paper are to develop a fuzzy model for ACC in multistage processes. The proposed approach is applied in an engine valve manufacturing firm and the model is solved by GAs.     

A modified distance method for multicriteria optimization, using genetic algorithms

The common application areas of Genetic Algorithms (GAs) have been to single criterion difficult optimization problems. The GA selection mechanism is often dependent upon a single valued scalar objective funtion. In this paper, we present results of a modified distance method. The distance method was proposed earlier by us, for solving multiple criteria problems with GAs. The Pareto set estimation method, which is fundamental to multicriteria analysis, is used to perform the multicriteria optimization using GAs. First, the Pareto set is found out from the population of the initial generation of the GA. The fitness of a new solution, is calculated by a distance measure with reference to the Pareto set of the previous runs. We calculate the distances of a solution from all the Pareto solutions found since the previous run, but the minimum of these distances is taken under consideration while evaluating the fitness of the solution. Thus the GA tries to maximize the distance of future Pareto solutions from present Pareto solutions in the positive Pareto space of the given problem. Here we modify distance method, by using an improved algorithm to assign and make use of the latent potential of the Pareto solutions which are found during the runs. Two detailed numerical examples and computer generated results are also presented.     

一种基于遗传算法的话者身份确认系统建模方法

本文描述了一种采用短语和基于遗传算法的话者身份确认系统的建模方法,利用遗传算法的全局搜索和优化特性,系统只需利用话者的短语音,就可快速建立话者的一类较优秀的模板。实验结果表明,这种方法既降低了用户的语音数据采集量,有利于话者模板的建立,又提高了系统的确认性能及鲁棒性,较传统方法有明显的优越性。     

基于改进模糊决策粗糙集的最小化决策代价特征选择算法

模糊决策粗糙集是决策粗糙集理论在模糊集环境下的重要延伸,然而该模型对含噪声的数据不具有很好的容忍性。为此在传统的模糊相似关系中引入一个限定阈值,提出一种改进的模糊相似关系。在其基础上对原始的模糊决策粗糙集进行重构,提出一种改进的模糊决策粗糙集模型。根据不同的特征选择方式,利用所提出的改进模型设计出两种搜索策略的最小化决策代价特征选择算法。实验分析表明,该算法比传统算法具有更高的优越性。     

基于智能全间隔自适应模糊支持向量机的水质分类

提出了一种新型具有良好特性的支持向量机--全间隔自适应模糊支持向量机(TAFSVM)。运用实值遗传算法(RGA)对其进行参数优选,得到一种新的智能模型--实值遗传算法优化的全间隔自适应模糊支持向量机(RGATAFSVM)模型,并且应用于四种不同的水质数据分类。实验结果表明,提出的模型相对标准支持向量机、BP神经网络和单因子分类方法具有较高的分类精度和较高的稳定性,是一种有效的水质分类方法。     

Optimization of interval type-2 fuzzy systems for image edge detection

This paper presents the optimization of a fuzzy edge detector based on the traditional Sobel technique combined with interval type-2 fuzzy logic. The goal of using interval type-2 fuzzy logic in edge detection methods is to provide them with the ability to handle uncertainty in processing real world images. However, the optimal design of fuzzy systems is a difficult task and for this reason the use of meta-heuristic optimization techniques is also considered in this paper. For the optimization of the fuzzy inference systems, the Cuckoo Search (CS) and Genetic Algorithms (GAs) are applied. Simulation results show that using an optimal interval type-2 fuzzy system in conjunction with the Sobel technique provides a powerful edge detection method that outperforms its type-1 counterparts and the pure original Sobel technique.     

Tuning certainty factor and local weight of fuzzy production rulesby using fuzzy neural network

Approximate reasoning in a fuzzy system is concerned with inferring an approximate conclusion from fuzzy and vague inputs. There are many ways in which different forms of conclusions can be drawn. Fuzzy sets are usually represented by fuzzy membership functions. These membership functions are assumed to have a clearly defined base. For other fuzzy sets such as intelligent, smart, or beautiful, etc., it would be difficult to define clearly its base because its base may consist of several other fuzzy sets or unclear nonfuzzy bases. A method to handle this kind of fuzzy set is proposed. A fuzzy neural network (FNN) is also proposed to tune knowledge representation parameters (KRPs). The contributions are that we are able to handle a broader range of fuzzy sets and build more powerful fuzzy systems so that the conclusions drawn are more meaningful, reliable, and accurate. An experiment is presented to demonstrate how our method works.     

Self-adjusting the intensity of assortative mating in genetic algorithms

Mate selection plays a crucial role in both natural and artificial systems. While traditional Evolutionary Algorithms (EA) usually engage in random mating strategies, that is, mating chance is independent of genotypic or phenotypic distance between individuals, in natural systems non-random mating is common, which means that somehow this mechanism has been favored during the evolutionary process. In non-random mating, the individuals mate according to their parenthood or likeness. Previous studies indicate that negative assortative mating (AM)—also known as dissortative mating—, which is a specific type of non-random mating, may improve EAs performance by maintaining the genetic diversity of the population at a higher level during the search process. In this paper we present the Variable Dissortative Mating Genetic Algorithm (VDMGA). The algorithm holds a mechanism that varies the GA’s mating restrictions during the run by means of simple rule based on the number of chromosomes created in each generation and indirectly influenced by the genetic diversity of the population. We compare VDMGA not only with traditional Genetic Algorithms (GA) but also with two preceding non-random mating EAs: the CHC algorithm and the negative Assortative Mating Genetic Algorithm (nAMGA). We intend to study the effects of the different methods in the performance of GAs and verify the reliability of the proposed algorithm when facing an heterogeneous set of landscapes. In addition, we include the positive Assortative Mating Genetic Algorithm (pAMGA) in the experiments in order test both negative and positive AM mechanisms, and try to understand if and when negative AM (or DM) speeds up the search process or enables the GAs to escape local optima traps. For these purposes, an extensive set of optimization test problems was chosen to cover a variety of search landscapes with different characteristics. Our results confirm that negative AM is effective in leading EAs out of local optima traps, and show that the proposed VDMGA is at least as efficient as nAMGA when applied to the range of our problems, being more efficient in very hard functions were traditional GAs usually fail to escape local optima. Also, scalability tests have been made that show VDMGA ability to decrease optimal population size, thus reducing the amount of evaluations needed to attain global optima. We like to stress that only two parameters need to be hand-tuned in VDMGA, thus reducing the tuning effort present in traditional GAs and nAMGA.