Optimization of process route by Genetic Algorithms

Process route sequencing is considered as the key technology for computer aided process planning (CAPP) and is very complex and difficult. In this paper, based on the analyzing of various constraints in process route sequencing and the astringency of Genetic Algorithms (GAs), the GA is reconstructed, including the establishing of the coding strategy, the evaluation operator and the fitness function. The new GAs can meet the requirement of sequencing work and can meet the requirement of astringency. The natural number is adopted in coding strategy, the “elitist model” and the “tournament selection” are adopted as selection operators, the nonconforming sequential searching crossover operator is used and the inconsistent mutation operator is adopted, the fitness function is defined as a formula of the sum of compulsive constraints with each weighing, and these constraints are used as the control strategy for GAs in the searching process. By using GAs in the optimization, the optimal or near-optimal process route is obtained finally.     

基于遗传算法的不确定系统的鲁棒控制参数选择

遗传算法是一种基于生物自然选择与遗传机理的随机搜索算法。本文利用遗传算法对不确定系统中的鲁棒控制参数进行选择。结果表明,此算法不仅可以得到满足要求的解,而且该算法的简单易行性也是其它算法所无法比拟的。     

Integrating relevance vector machines and genetic algorithms for optimization of seed-separating process

A hybrid intelligent approach based on relevance vector machines (RVMs) and genetic algorithms (GAs) has been developed for optimal control of parameters of nonlinear manufacturing processes. It concerns the finding of the near-optimal control parameters of the nonlinear discrete manufacturing process with a specific objective. First, the nonlinear process with measurement noise is regressed by the relevance vector learning mechanism based on a kernel-based Bayesian framework. For minimizing the approximate error, uniform design sampling, online incremental learning and cross-validation are used in the learning process of RVMs. Such well-trained models become a specialized process simulation tool, which is valuable in prediction and optimization of nonlinear processes. Next, the near-optimal setpoints of the control system, which maximize the objective function, are sought by GAs from the numerous values of the objective function obtained from the simulation. As a case study, the seed separator system (5XZW-1.5) is used for evaluating the proposed intelligent approach. The control parameters to reach the maximum weighted objective, which combine the system output and evaluation functions, are optimized. The experimental results show the effectiveness of the proposed hybrid approach.     

A Tabu-enhanced genetic algorithm approach for assembly process planning

Over the past decade, much work has been done to optimize assembly process plans to improve productivity. Among them, genetic algorithms (GAs) are one of the most widely used techniques. Basically, GAs are optimization methodologies based on a direct analogy to Darwinian natural selection and genetics in biological systems. They can deal with complex product assembly planning. However, during the process, the neighborhood may converge too fast and limit the search to a local optimum prematurely. In a similar domain, Tabu search (TS) constitutes a meta-procedure that organizes and directs the operation of a search process. It is able to systematically impose and release constraints so as to permit the exploration of otherwise forbidden regions in a search space. This study attempts to combine the strengths of GAs and TS to realize a hybrid approach for optimal assembly process planning. More robust search behavior can possibly be obtained by incorporating the Tabus intensification and diversification strategies into GAs. The hybrid approach also takes into account assembly guidelines and assembly constraints in the derivation of near optimal assembly process plans. A case study on a cordless telephone assembly is used to demonstrate the approach. Results show that the assembly process plans obtained are superior to those derived by GA alone. The details of the hybrid approach and the case study are presented.     

一种基于遗传算法的双T-Snake模型图像分割方法

Snake的初衷是为了进行图像分割，但它对初始位置过于敏感，且不能处理拓扑结构改变的问题。初始位置的敏感性可以用遗传算法来克服，因为它是一种全局优化算法，且有良好的数值稳定性。为了更精确地进行图像分割，本文提出了一种基于遗传算法的双T—Snake模型图像分割方法，它将双T—Snake模型解作为遗传算法的搜索空间，这既继承了T—Snake模型的拓扑改变能力，又加快了遗传算法的收敛速度。由于它利用遗传算法的全局优化性能，克服了Snake轮廓局部极小化的缺陷，从而可得到对目标的更精确的分割。将其应用于左心室MRI图像的分割，取得了较好的效果。     

桁架结构振动的主动模糊控制中主动杆数目与位置优化

研究了采用自适应模糊控制器抑制桁架结构振动时的主动杆数目与位置优化问题.通过定义输入能量相关矩阵优化了主动杆的数目.基于主动杆的控制能量配置准则,给出了主动杆优化配置的模型.研究基于整数编码的遗传算法用于大型离散体中的作动器组合优化问题.最后针对挠性空间智能桁架结构的振动控制仿真,使用基于整数编码的遗传算法(GAs)优化主动杆位置.结果表明对于采用自适应模糊控制律的离散体结构振动控制是行之有效的.     

Evolutionary Optimization of Machining Processes

Optimization of machining processes plays a key role in meeting the demands for high precision and productivity. The primary challenge for machining process optimization often stems from the fact that the procedure is typically highly constrained and highly non-linear, involving mixed-integer-discrete-continuous design variables. Additionally, machining process models are likely discontinuous, non-explicit, or not analytically differentiable with the design variables. Traditional non-linear optimization techniques are mostly gradient-based, posing many limitations upon application to today’s complex machining models. Genetic Algorithms (GAs) has distinguished itself as a method with the potential for solving highly non-linear, ill-behaved complex machining optimization problems. Unlike traditional optimization techniques, GAs start with a population of different designs and use direct search methods stochastically and deterministically toward optimal and feasible direction. However, GAs still has its own drawbacks when it is applied to machining process optimization, including the lack of efficiency due to its binary representation scheme for continuous design variables, a lack of local fine-tuning capabilities, a lack of a self-adaptation mechanism, and a lack of an effective constraint handling method. A novel and systematic evolutionary algorithm based on GAs is presented in this paper in the areas of problem representation; selection scheme; genetic operators for integer, discrete, and continuous variables; constraint handling method; and population initialization to overcome the underlying drawbacks. The proposed scheme has been applied to two machining problems to demonstrate its superior performance.     

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T细胞调节的免疫遗传优化算法对逆变器PWM控制序列的优化

针对遗传算法(GAs)收敛速度慢、易于陷入局部最优等不足,基于单相全桥逆变器输出电流与参考电流的误差模型,提出一种改进的免疫遗传优化算法(IGOAs)用于逆变器PWM最优控制序列优化。算法采用0、1编码,自适应突变概率及T细胞调节算子增强算法的快速收敛性和种群的多样性。数值实验中考虑逆变器负载端电阻为定值和受随机扰动两种情形,将GAs和IGOAs用于此两种情形的PWM控制序列优化,仿真结果统计表明：IGOAs具有较好的收敛性和稳定性,负载电阻受随机扰动时,IGOAs较GAs能快速跟踪参考电流,获得较小的THD电流波。     

A novel parallel hybrid intelligence optimization algorithm for a function approximation problem

A novel parallel hybrid intelligence optimization algorithm (PHIOA) is proposed based on combining the merits of particle swarm optimization with genetic algorithms. The PHIOA uses the ideas of selection, crossover and mutation from genetic algorithms (GAs) and the update velocity and situation of particle swarm optimization (PSO) under the independence of PSO and GAs. The proposed algorithm divides the individuals into two equation groups according to their fitness values. The subgroup of the top fitness values is evolved by GAs and the other subgroup is evolved by the PSO algorithm. The optimal number is selected as a global optimum at every circulation which shows better results than both PSO and GAs, then improves the overall performance of the algorithm. The PHIOA is used to optimize the structure and parameters of the fuzzy neural network. Finally, the experimental results have demonstrated the superiority of the proposed PHIOA to search the global optimal solution. The PHIOA can improve the error accuracy while speeding up the convergence process, and effectively avoid the premature convergence to compare with the existing methods.     

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 genetic algorithm approach to determine the sample size for attribute control charts

Determining the sample size for control charts (CCs) is generally an important problem in the literature. In this paper, Kaya and Engin’s [?. Kaya, O. Engin, A new approach to define sample size at attributes control chart in multistage processes: an application in engine piston manufacturing process, Journal of Materials Processing Technology 183 (2007) 38-48] model based on minimum cost and maximum acceptance probability to determine the sample size for attribute control charts (ACCs), and solved by genetic algorithms (GAs) with linear binary representation structure, is handled to solve it by a linear real-valued representation. A new chromosome structure is also suggested to increase the efficiency of GAs. The performance of GAs depends on mutation and crossover operators, and their ratios. To determine the most appropriate operators, five different mutation and crossover operators are used and they are compared with each other. An application in a motor engine factory is illustrated. u-Control charts are constructed with respect to the sample size determined by GA in the model. The piston production stages in this factory are monitorized using the obtained control charts.     

Optimization of control parameters in genetic algorithms: A stochastic approach

This paper proposes a stochastic approach for optimization of control parameters ( probabilities of crossover and mutation ) in genetic algorithms ( GAs ) . The genetic search can be modelled as a controlled Markovian process, the transition of which depends on the control parameters. A stochastic optimization problem is formed for control of GA parameters, based on a given performance index of populations and analysed as a controlled Markovian process during the genetic search. The optimal values of control parameters can be found from a recursive estimation of control parameters, which is obtained by introducing a stochastic gradient of the performance index and using a stochastic approximation algorithm. The algorithm possesses the capability of finding the stochastic gradient and adapting the control parameters in the direction of descent. A non-stationary Markov model is developed to investigate asymptotic convergence properties of the proposed genetic algorithm. It is proved that the proposed genetic algorithm would asymptotically converge. Numerical results based on the classical functions are obtained to show the potential of the proposed algorithm.     

Fuzzy classifier design using genetic algorithms

A new method for design of a fuzzy-rule-based classifier using genetic algorithms (GAs) is discussed. The optimal parameters of the fuzzy classifier including fuzzy membership functions and the size and structure of fuzzy rules are extracted from the training data using GAs. This is done by introducing new representation schemes for fuzzy membership functions and fuzzy rules. An effectiveness measure for fuzzy rules is developed that allows for systematic addition or deletion of rules during the GA optimization process. A clustering method is utilized for generating new rules to be added when additions are required. The performance of the classifier is tested on two real-world databases (Iris and Wine) and a simulated Gaussian database. The results indicate that highly accurate classifiers could be designed with relatively few fuzzy rules. The performance is also compared to other fuzzy classifiers tested on the same databases.     

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.     

Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation

A traditional approach to segmentation of magnetic resonance (MR) images is the fuzzy c-means (FCM) clustering algorithm. The efficacy of FCM algorithm considerably reduces in the case of noisy data. In order to improve the performance of FCM algorithm, researchers have introduced a neighborhood attraction, which is dependent on the relative location and features of neighboring pixels. However, determination of degree of attraction is a challenging task which can considerably affect the segmentation results.This paper presents a study investigating the potential of genetic algorithms (GAs) and particle swarm optimization (PSO) to determine the optimum value of degree of attraction. The GAs are best at reaching a near optimal solution but have trouble finding an exact solution, while PSO’s-group interactions enhances the search for an optimal solution. Therefore, significant improvements are expected using a hybrid method combining the strengths of PSO with GAs, simultaneously. In this context, a hybrid GAs/PSO (breeding swarms) method is employed for determination of optimum degree of attraction. The quantitative and qualitative comparisons performed on simulated and real brain MR images with different noise levels demonstrate unprecedented improvements in segmentation results compared to other FCM-based methods.     

Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics

A general weapon-target assignment (WTA) problem is to find a proper assignment of weapons to targets with the objective of minimizing the expected damage of own-force asset. Genetic algorithms (GAs) are widely used for solving complicated optimization problems, such as WTA problems. In this paper, a novel GA with greedy eugenics is proposed. Eugenics is a process of improving the quality of offspring. The proposed algorithm is to enhance the performance of GAs by introducing a greedy reformation scheme so as to have locally optimal offspring. This algorithm is successfully applied to general WTA problems. From our simulations for those tested problems, the proposed algorithm has the best performance when compared to other existing search algorithms.     

Genetic algorithms based on an intelligent controller

Genetic algorithms (GAs) have been proven as robust search procedures. Numerous researchers have established the validity of GAs in optimization, machine learning and control applications. This paper presents a new intelligent control scheme using the robust sear h feature of GAs incorporating the basic idea of self-tuning regulators. The proposed controller utilized GAs to search for the changes of system parameters and to calculate the corresponding control law. The optimum parameters and control law are chosen based on the selection mechanism of GAs, which employs the square of the difference between the actual and the estimated outputs as the fitness function. The controller has an on-line parameters identification function and does not require prior knowledge or training data for learning.

The proposed intelligent controller is applied to the load frequency control of a power system to investigate the effectiveness from results obtained from computer simulations, the intelligent controller has been proven to provide good system characteristics.     

Genetic algorithms and job shop scheduling

We describe applications of Genetic Algorithms (GAs) to the Job Shop Scheduling (JSS) problem. More specifically, the task of generating inputs to the GA process for schedule optimization is addressed.

We believe GAs can be employed as an additional tool in the Computer Integrated Manufacturing (CIM) cycle. Our technique employs an extension to the Group Technology (GT) method for generating manufacturing process plans. It positions the GA scheduling process to receive outputs from both the automated process planning function and the order entry function. The GA scheduling process then passes its results to the factory floor in terms of optimal schedules.

An introduction to the GA process is discussed first. Then, an elementary n-task, one processor (machine) problem is provided to demonstrate the GA methodology in the JSS problem arena. The technique is then demonstrated on an n-task, two processor problem, and finally, the technique is generalized to the n-tasks on m-processors (serial) case.     

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.