Process parameter optimization of lap joint fillet weld based on FEM–RSM–GA integration technique

This study introduces a welding process design tool to determine optimal arc welding process parameters based on Finite Element Method (FEM), Response Surface Method (RSM) and Genetic Algorithms (GA). Here, a sequentially integrated FEM–RSM–GA framework has been developed and implemented to reduce the weld induced distortion in the final welded structure. It efficiently incorporates finite element based numerical welding simulations to investigate the desired responses and the effect of design variables without expensive trial experiments. To demonstrate the effectiveness of the proposed methodology, a lap joint fillet weld specimen has been used in this paper. Four process parameters namely arc voltage, input current, welding speed and welding direction have been optimized to minimize the distortion of the structure. The optimization results revealed the effectiveness of the methodology for welding process design with reduced cost and time.     

线性时滞系统稳定的充分必要条件

提出一个新的线性时滞系统稳定的充分必要条件,与以前的结论不同的是,我们的结论的表达形式更容易数值计算。开发相应的二种收敛算法,解决系统时滞独立稳定判断和时滞相关稳定的时滞界估计。本文给出范数有界不确定性时,系统稳定的充分条件。文末给出的数值实例证明本文结果降低计算复杂度,改进了时滞估计保守性。     

Nature inspired algorithms to optimize robot workcell layouts

Multi-objective layout optimization methods for the conceptual design of robot cellular manufacturing systems are proposed in this paper. Robot cellular manufacturing systems utilize one or more flexible robots which can carry out a large number of operations, and can conduct flexible assemble processes. The layout design stage of such manufacturing systems is especially important since fundamental performances of the manufacturing system under consideration are determined at this stage. Layout area, operation time and manipulability of robot are the three important criteria when it comes to designing manufacturing system. The use of nature inspired algorithms are not extensively explored to optimize robot workcell layouts. The contribution in this paper is the use of five nature-inspired algorithms, viz. genetic algorithm (GA), differential evolution (DE), artificial bee colony (ABC), charge search system (CSS) and particle swarm optimization (PSO) algorithms and to optimize the three design criteria simultaneously. Non-dominated sorting genetic algorithm-II is used to handle multiple objectives and to obtain pareto solutions for the problems considered. The performance of sequence pair and B*-Tree layout representation schemes are also evaluated. It is found that sequence pair scheme performs better than B*-Tree representation and it is used in the algorithms. Numerical examples are provided to illustrate the effectiveness and usefulness of the proposed methods. It is observed that PSO performs better over the other algorithms in terms of solution quality.     

Optimal design of adaptive type-2 neuro-fuzzy systems: A review

Type-2 fuzzy logic systems have extensively been applied to various engineering problems, e.g. identification, prediction, control, pattern recognition, etc. in the past two decades, and the results were promising especially in the presence of significant uncertainties in the system. In the design of type-2 fuzzy logic systems, the early applications were realized in a way that both the antecedent and consequent parameters were chosen by the designer with perhaps some inputs from some experts. Since 2000s, a huge number of papers have been published which are based on the adaptation of the parameters of type-2 fuzzy logic systems using the training data either online or offline. Consequently, the major challenge was to design these systems in an optimal way in terms of their optimal structure and their corresponding optimal parameter update rules. In this review, the state of the art of the three major classes of optimization methods are investigated: derivative-based (computational approaches), derivative-free (heuristic methods) and hybrid methods which are the fusion of both the derivative-free and derivative-based methods.     

A robust hybrid approach based on particle swarm optimization and genetic algorithm to minimize the total machine load on unrelated parallel machines

This paper dealt with an unrelated parallel machines scheduling problem with past-sequence-dependent setup times, release dates, deteriorating jobs and learning effects, in which the actual processing time of a job on each machine is given as a function of its starting time, release time and position on the corresponding machine. In addition, the setup time of a job on each machine is proportional to the actual processing times of the already processed jobs on the corresponding machine, i.e., the setup times are past-sequence-dependent (p-s-d). The objective is to determine jointly the jobs assigned to each machine and the order of jobs such that the total machine load is minimized. Since the problem is NP-hard, optimal solution for the instances of realistic size cannot be obtained within a reasonable amount of computational time using exact solution approaches. Hence, an efficient method based on the hybrid particle swarm optimization (PSO) and genetic algorithm (GA), denoted by HPSOGA, is proposed to solve the given problem. In view of the fact that efficiency of the meta-heuristic algorithms is significantly depends on the appropriate design of parameters, the Taguchi method is employed to calibrate and select the optimal levels of parameters. The performance of the proposed method is appraised by comparing its results with GA and PSO with and without local search through computational experiments. The computational results for small sized problems show that the mentioned algorithms are fully effective and viable to generate optimal/near optimal solutions, but when the size of the problem is increased, the HPSOGA obtains better results in comparison with other algorithms.     

High‐precision formation control of nonlinear multi‐agent systems with switching topologies: A learning approach

Arbitrary high precision is considered one of the most desirable control objectives in the relative formation for many networked industrial applications, such as flying spacecrafts and mobile robots. The main purpose of this paper is to present design guidelines of applying the iterative schemes to develop distributed formation algorithms in order to achieve this control objective. If certain conditions are met, then the control input signals can be learned by the developed algorithms to accomplish the desired formations with arbitrary high precision. The systems under consideration are a class of multi‐agent systems under directed networks with switching topologies. The agents have discrete‐time affine nonlinear dynamics, but their state functions do not need to be identical. It is shown that the learning processes resulting from the relative output formation of multi‐agent systems can converge exponentially fast with the increase of the iteration number. In particular, this work induces a distributed algorithm that can simultaneously achieve the desired relative output formation between agents and regulate the movement of multi‐agent formations as desired along the time axis. The illustrative numerical simulations are finally performed to demonstrate the effectiveness and performance of the proposed distributed formation algorithms. Copyright © 2014 John Wiley & Sons, Ltd.     

Switching adaptive learning control for nonlinearly parameterized systems with disturbance of unknown periods

In this paper, a logic‐based switching adaptive learning control mechanism is proposed for a class of nonlinearly parameterized systems with disturbance of unknown periods. The switching algorithms include two parts: one is to stabilize the nonlinearly parameterized uncertainties, the other is to learn the periodic bounded disturbance. An adaptive control method with fully saturated periodic adaptation law is presented to take advantage of the periodic and bounded property of the disturbance. It is shown that under the proposed control designs, the asymptotic convergence is ensured irrespective of initial conditions with all the signals in the closed‐loop system bounded. An illustrative example is given to show the validity of the switching adaptive learning control. Copyright © 2014 John Wiley & Sons, Ltd.     

面向生态的陕西省渭河流域水资源合理配置

结合陕西省渭河流域的水资源开发利用现状,对其水资源优化管理进行研究。通过分析流域社会经济发展的特点及生态环境形势,以定额法为基础对2020年流域的生活、生产和生态需水进行了预测。根据系统概化图和配水目标,综合考虑水资源配置中的生态指标、社会指标和经济指标,建立了面向生态的水资源合理配置模型。将混沌优化算法与遗传算法相结合对模型进行求解,结果表明:陕西省渭河流域2020年75%保证率下的缺水率为18.1%。     

Overview of Information Security Using Genetic Algorithm and Chaos

ABSTRACT

Security, integrity, nonrepudiation, confidentiality, and authentication services are the most important factors in information security. Genetic algorithms (GAs) are a class of optimization algorithms. Many problems can be solved using genetic algorithms through modeling a simplified version of genetic processes. The application of a genetic algorithm to the field of cryptology is unique. Few works exist on this topic. In this article, an attempt has been made to give an overview of genetic algorithm-based cryptography and to propose a new approach to GA with pseudorandom sequence to encrypt data stream. The feature of such an approach includes high data security and high feasibility for easy integration with commercial multimedia transmission applications. The experimental results of the proposed technique confirm that high throughput rate needed for real time data protection is achieved.     

NetCluster: A clustering-based framework to analyze internet passive measurements data

Internet measured data collected via passive measurement are analyzed to obtain localization information on nodes by clustering (i.e., grouping together) nodes that exhibit similar network path properties. Since traditional clustering algorithms fail to correctly identify clusters of homogeneous nodes, we propose the NetCluster novel framework, suited to analyze Internet measurement datasets. We show that the proposed framework correctly analyzes synthetically generated traces. Finally, we apply it to real traces collected at the access link of Politecnico di Torino campus LAN and discuss the network characteristics as seen at the vantage point.