A genetic algorithm approach to optimising component placement and retrieval sequence for chip shooter machines

A chip shooter machine in printed circuit board (PCB) assembly has three movable mechanisms: an X-Y table carrying a PCB, a feeder carrier with several feeders holding components and a rotary turret with multiple assembly heads to pick up and place components. In order to get the minimal placement or assembly time for a PCB on the machine, all the components on the board should be placed in a perfect sequence, and the components should be set up on a right feeder, or feeders since two feeders can hold the same type of components, and additionally, the assembly head should retrieve or pick up a component from a right feeder. The entire problem is very complicated, and this paper presents a genetic algorithm approach to tackle it.     

Optimization of slider air bearing contours using the combined genetic algorithm-subregion approach

A hybrid optimization approach, the combined genetic algorithm-subregion method, which combines the advantage of the genetic algorithm and the subregion approach, is presented. Using a binary string to represent a selected design space, the combined genetic algorithm-subregion method adopts the genetic algorithm to perform the optimization process. Starting from a pico slider design originally flying at 14 nm, optimized designs were obtained for sliders with target flying heights of 7, 5 and 3.5 nm, respectively. The results show that the combined genetic algorithm-subregion method has good convergence with a substantial reduction of computation time.     

An optimisation approach to the contour error control of CNC machine tools using genetic algorithms

A contour control strategy has been studied in this paper to improve the contour error of CNC machine tools. First, a single axis controller is analysed and then a velocity feedforward controller is added in the velocity loop. To further reduce the contour error, a cross-coupled controller is adopted and an algorithm for an on-line estimation of the contour error in arbitrary curved contouring is proposed. These controller parameters are all optimised by an efficient robust optimisation technique using genetic algorithms. Experimental results are provided to illustrate the proposed methods.     

基于PetriNets和混合遗传算法的双资源JSP动态优化调度

以带有控制器的Petri网为建模工具对柔性生产调度中的离散事件建模,利用遗传算法和模拟退火算法获得调度结果,并通过Petri网进行控制.用于解决作业车间的加工受到机床、操作工人等生产资源制约条件下的优化调度.以生产周期为目标进行的优化调度,将遗传算法和模拟退火相结合.通过多种交叉、变异、概率更新选择、再分配策略等遗传和模拟操作,得到目标的最优或次优解.对算法进行了仿真研究,仿真结果表明该算法是有效性.     

A hybrid approach of rough set theory and genetic algorithm for fault diagnosis

This paper proposes an integrated intelligent system that builds a fault diagnosis inference model based on the advantage of rough set theory and genetic algorithms (GAs). Rough set theory is a novel data mining approach that deals with vagueness and can be used to find hidden patterns in data sets. Based on this approach, minimal condition variable subsets and induction rules are established and illustrated using an application for motherboard electromagnetic interference (EMI) test fault diagnosis. This integrated system successfully integrated the rough set theory for handling uncertainty with a robust search engine, GA. The result shows that the proposed method can reduce the number of conditional attributes used in motherboard EMI fault diagnosis and maintain acceptable classification accuracy. The average diagnostic accuracy of 80% shows that this hybrid model is a promising approach to EMI diagnostic support systems .     

Developing a diagnostic system through the integration of ant colony optimization systems and case-based reasoning

This study is dedicated to integrating both the clustering method and case-based reasoning (CBR) for developing a diagnostic system in maintenance. The reason for this is that searching similar cases for CBR is time consuming if the case base is fairly large. It is necessary to cluster the cases into some groups, and then perform the search for the most appropriate possible group. A novel approach, the ant colony system clustering algorithm (ASCA), is employed for this purpose. The main advantage of this technique is the reduction in the amount of time used in comparison. A real-life problem for car maintenance has shown evidence of this advantage as well as its precision ability.     

Control of a manipulator robot by neuro-fuzzy subsets form approach control optimized by the genetic algorithms

In this paper, we describe a new form of neuro-fuzzy-genetic controller design for nonlinear system derived from a manipulator robot. The proposed method combines fuzzy logic and neuronal networks which are of growing interest in robotics, the neuro-fuzzy controller does not require the knowledge of the robot parameters values. Furtheremore, the genetic algorithms (GAs) for complex motion planning of robots require an evaluation function which takes into account multiple factors. An optimizing algorithm based on the genetic algorithms is applied in order to provide the most adequate shape of the fuzzy subsets that are considered as an interpolation functions. The proposed approach provides a well learning of the manipulator robot dynamics whatever the assigned task. Simulation and practical results illustrate the effectiveness of the proposed strategy. The advantages of the proposed method and the possibilities of further improvements are discussed.