Design Of Genetic Algorithm Research Articles

Research on the Optimal Layout Problem for NC Machining Based on Improved Genetic Algorithm

In the process of NC machining, the optimization processing of graphic layout is a well-studied problem which has practical application value for improving the utilization rate of raw materials and saving the cost of production. In this paper, a new design of genetic algorithm (GA) is proposed for solving this problem. This improved genetic algorithm combines GA with the improved crossover operator and mutation operator. Moreover, the best individual preservation method is integrated into the algorithm. The improved genetic algorithm expands the search space and enhances the GA’s search capabilities. Furthermore, the maximum matching algorithm is proposed based on the lowest horizontal line algorithm, which effectively avoids blind elevating horizontal lines and improves the utilization rate of the lowest horizontal line. It is integrated with the improved genetic algorithm to solve the two-dimensional rectangular parts optimal layout problem which combines the advantages of two kinds of algorithms. The experimental results show that the algorithm can get a good optimization result. DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.3554 Full Text: PDF

Programmable logic design of a compact Genetic Algorithm for phasor estimation in real-time

The main objective of this work is to present an efficient method for phasor estimation based on a compact Genetic Algorithm (cGA) implemented in Field Programmable Gate Array (FPGA). To validate the proposed method, an Electrical Power System (EPS) simulated by the Alternative Transients Program (ATP) provides data to be used by the cGA. This data is as close as possible to the actual data provided by the EPS. Real life situations such as islanding, sudden load increase and permanent faults were considered. The implementation aims to take advantage of the inherent parallelism in Genetic Algorithms in a compact and optimized way, making them an attractive option for practical applications in real-time estimations concerning Phasor Measurement Units (PMUs).

Architecture-based design and optimization of genetic algorithms on multi- and many-core systems

A Genetic Algorithm (GA) is a heuristic to find exact or approximate solutions to optimization and search problems within an acceptable time. We discuss GAs from an architectural perspective, offering a general analysis of performance of GAs on multi-core CPUs and on many-core GPUs. Based on the widely used Parallel GA (PGA) schemes, we propose the best one for each architecture. More specifically, the Asynchronous Island scheme, Island/Master–Slave Hierarchy PGA and Island/Cellular Hierarchy PGA are the best for multi-core, multi-socket multi-core and many-core architectures, respectively. Optimization approaches and rules based on a deep understanding of multi- and many-core architectures are also analyzed and proposed. Finally, the comparison of GA performance on multi-core and many-core architectures are discussed. Three real GA problems are used as benchmarks to evaluate our analysis and findings.There are three extra contributions compared to previous work. Firstly, our findings based on deeply analyzing architectures can be applied to all GA problems, even for other parallel computing, not for a particular GA problem. Secondly, the performance of GAs in our work not only concerns execution speed, also the solution quality has not been considered seriously enough. Thirdly, we propose the theoretical performance and optimization models of PGA on multi-core and many-core architectures, finding a more practical result of the performance comparison of the GA on these architectures, so that the speedup presented in this work is more reasonable and is a better guide to practical decisions.

Hardware Testable Design of Genetic Algorithm for VLSI Circuits

Accurate and fast testing of digital circuits is very much essential in real time applications. Hardware analysis of digital circuits, which is otherwise very tedious and time consuming, is attempted using the artificial intelligence technique: Genetic Algorithms (GA). GA is used to find an input sequence to a digital circuit for testing, as it reduces the hardware utilization, complexity and computational time of the circuits. All the GA processes are simulated and implemented by using Xilinx 10.1 and SPARTAN 3E.

MULTI-OBJECTIVE OPTIMIZATION FOR DESIGN OF SINGLE ROW LAYOUT IN FLEXIBLE MANUFACTURING SYSTEM WITH SCHEDULING CONSTRAINT:AN APPROACH OF NONTRADITIONAL OPTIMIZATION TECHNIQUES

Single row layout is one of the most usually used layout patterns in industries, particularly in flexible manufacturing systems. Here actual sequencing of machine and arrangement of parts, no doubt, have a great influence on the throughput of the flexible manufacturing system i.e., (F.M.S). This paper discusses the single row layout design in flexible manufacturing system (F.M.S). This paper furnishes the design, development and testing of simulated annealing technique and genetic algorithm to solve the single row layout problem by considering multi-objective i.e., minimizing the make span of jobs on all machines and minimizing the total transportation cost. The various line layout problems are tested for performance of objective function with respect to computational time and number of iterations involved in GA and SA. A necessary code is generated in C++ and the code is run by the IDE tool in which C++ compiler used as plug in. This tool has Eclipse based features which affords the competency to figure, correct, steer, and sort out the tasks that use C++ as a programming language using Intel core i3-380M processor. The results of the different optimization algorithms (Genetic Algorithm and simulated annealing method) are compared and finally, we observed that GA provide optimum results than SA.

On the design of an ECOC-Compliant Genetic Algorithm

Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches.

Using computation to enhance diagnosis and therapy: a novel mutation operator for real-coded adaptive genetic algorithms

The fields of molecular biology and neurobiology have advanced rapidly over the last two decades. These advances have resulted in the development of large proteomic and genetic databases that need to be searched for the prediction, early detection and treatment of neuropathologies and other genetic disorders. This need, in turn, has pushed the development of novel computational algorithms that are critical for searching genetic databases. One successful approach has been to use artificial intelligence and pattern recognition algorithms, such as neural networks and optimization algorithms (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate based on the fitness function of passing generations. We propose a novel pseudo-derivative based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

Fault Tree Analysis Optimized by Genetic Algorithms

Abstract This paper deals with possibilities of using genetic algorithms in design of costs optimization, which are needed to reach given reliability of technical system, respectively system reliability optimization by given amount of investment costs. In following chapters, there is a described design of new method, which will be later implemented in application and verified on an example. Causal relationships between system failures and elements faults can be detected by analytic approach, when at first the undesired system fault is identified and its reasons are progressively detected. The reliability of system and its elements is analyzed using method FTA (fault tree analysis) and system is represented by fault trees. In order to optimize costs, respectively reliability, the genetic algorithms are used.

Integration optimization of novel electric power steering system based on quality engineering theory

The dynamic model of a novel electric power steering (EPS) system integrated with active front steering function (the novel EPS system) is built. The concepts and quantitative expressions of the steering road feel, steering sensibility, and steering operation stability are introduced. Based on quality engineering theory, the optimization algorithm is proposed by integrating the Monte Carlo descriptive sampling, elitist non-dominated sorting genetic algorithm (NSGA-II) and 6-sigma design method. With the steering road feel and the steering portability as optimization targets, the system parameters are optimized by the proposed optimization algorithm. The simulation results show that the system optimized based on quality engineering theory can improve the steering road feel, guarantee steering stability and steering portability and thus provide a theoretical basis for the design and optimization of the novel electric power steering system.

Optimal Software Architecture Design Based on Responsibilities Using Adaptive Genetic Algorithm

The software architecture of a program or computing system is the structure or structures of the system, which comprise software elements, the externally visible properties of those elements, and the relationships among them. Software architecture is developed during the early phases of the development process; it hugely constraints or facilitates the achievement of specific functional requirements, nonfunctional requirements, and business goals. The software architecture can be designed based on the quality attributes, performance, security and manageability. Meta heuristic search algorithm plays a vital role in the software architecture design. In traditional architecture design, the software architect has the requirements for the system, and attempts to piece the respective operations together so the solution reaches high values when “measured” by some attributes. In our research, the Adaptive Genetic Algorithm actually evaluates a large number of architectures simultaneously, based on the responsibilities and dependency values obtained from the Dependency graph. The experimental results shown that the proposed AGA based Software architecture design is compared with conventional genetic algorithm design in terms of time.

Design and analysis of genetic algorithm based Chinese keyword extracting

Analysing and extracting useful knowledge effectively from the web data is becoming more and more important. As the weighted keywords can be considered as the condensed versions of documents, this paper presents the novel Chinese keyword extraction algorithm based on genetic algorithm, together with paragraph analysing, Chinese segmentation, synonymous and unlisted-term processing. On the basis of the genetic algorithm training and the lead of the extracted terms results given by the experts manually, the genetic algorithm based approach can present an optimised and useful results, especially in some domains. It can be used to train the term weights within the lexicons. The experimental results and the analysis show the feasibility of the approach.

BRAIN TUMOR TISSUE CATEGORIZATION IN 3D MAGNETIC RESONANCE IMAGES USING IMPROVED PSO FOR EXTREME LEARNING MACHINE

Magnetic Resonance Imaging (MRI) technique is one of the most useful diagnostic tools for human soft tissue analysis. Moreover, the brain anatomy features and internal tissue architecture of brain tumor are a complex task in case of 3-D anatomy. The additional spatial relationship in transverse, longitudinal planes and the coronal plane information has been proved to be helpful for clinical applications. This study extends the computation of gray level co- occurrence matrix (GLCM) and Run length matrix (RLM) to a three- dimensional form for feature extraction. The sub-selection of rich optimal bank of features to model a classifler is achieved with custom Genetic Algorithm design. An improved Extreme Learning Machine (ELM) classifler algorithm is explored, for training single hidden layer artiflcial neural network, integrating an enhanced swarm-based method in optimization of the best parameters (input-weights, bias, norm and hidden neurons), enhancing generalization and conditioning of the algorithm. The method is modeled for automatic brain tissue and pathological tumor classiflcation and segmentation of 3D MRI tumor images. The method proposed demonstrates good generalization capability from the best individuals obtained in the learning phase to handle sparse image data on publically available benchmark dataset and real time data sets.

Robust design optimisation of advance hybrid (fiber–metal) composite structures

Hybrid Composite Structures (HCSs) are consisting of alternating layers of Fiber-Reinforced Polymer and metal sheets. Mechanical properties and responses for off-design conditions of HCSs can be improved using an innovative methodology coupling Multi-Objective Genetic Algorithm and robust design method. The concept of robust design approach ensures that a structure will be tolerant to unexpected loading and operating conditions. In this paper, two applications are considered; the first is to maximise the stiffness of the HCS while minimising its total weight through a Multi-Objective Design Optimisation. The second application considers a Robust Multi-Objective Design Optimisation (RMDO) to minimise total weight of HCS and to minimise both, the normalised mean displacement and the standard deviations of displacement, considering critical load cases. For the optimisation process, a distributed/parallel Multi-Objective Genetic Algorithm in robust multi-objective optimisation platform is used and it is coupled to a Finite Element Analysis based composite structure analysis tool to find the optimal combination of laminates sequences for HCSs. Numerical results show the advantages in mechanical properties of HCS over the metal structures, and also the use of RMDO methodology to obtain higher characteristics of HCS in terms of mechanical properties and its stability at the variability of load cases.

Using Design of Experiment and Genetic Algorithm to Obtain the Optimum Gear Shifting Strategy for a Real Driving Cycles

Design of Experiment statistical method and Genetic Algorithms based optimization method are used to obtain the optimum gear shifting strategy that driver can follow to provide best fuel consumption without affecting drivability characteristics of the vehicle according to certain driving cycle. The study is carried on a Mining Dump Truck YT3621 with 9 forward shifts manual transmission. Three loading conditions, no load, 20 ton and 40 ton have been discussed. The truck powertrain is modeled using GT-Drive, and DOE –Post processing tool of the GT-Suit is used for DOE analysis and Genetic Algorithm optimization. Six different real on road driving cycles are used to study the effect of gear shifting strategy on fuel consumption.

Application of genetic algorithms in design and optimisation of multi‐stream plate–fin heat exchangers

AbstractThis paper introduces two new approaches in the thermo‐hydraulic design of multi‐stream heat exchangers (MSHEs). In both approaches, geometrical aspects of the MSHE (e.g. exchanger dimensions, fin type, etc.) are optimised with a genetic algorithm (GA) using the Total Annual Cost (TAC) as an objective function. The first approach is capable of utilising the maximum allowable stream pressure drops and can result in minimal surface area requirements. In the second approach, all of the pressure drop values are considered as design variables and are therefore subject to optimisation. These approaches have been applied to two case studies taken from literature, and the results are compared to those arising from a currently used design method. In the first case study, application of the new approaches resulted in a smaller TAC than the current approach by 5.77% and 31.86%, respectively, and improvement in the second case was estimated to be 5% and 21.46%, correspondingly. The effect of different fins on an MSHE's TAC is discussed through application of GAs to the current approach. It is shown that correct selection of fin types reduces the TAC of the two case studies by 21.75% and 8.7%, respectively. © 2012 Canadian Society for Chemical Engineering

Analysis and optimization of fixture under dynamic machining condition with chip removal effect

Dynamic interactions in the tool---workpiece and workpiece---fixture systems significantly impinge on the quality of finished workpieces. The presented simulation system integrates the effects of workpiece fixture dynamics with the other factors contributing to the machining process dynamics. It provides more accurate prediction of the process output which helps in the design of the optimum fixture configuration prior to the production stage. Modelling of the frictional contact behaviour between the fixture element and the workpiece helps to improve the prediction accuracy of the simulation system which accelerates the convergence to the optimum fixture configuration design and consequently improves the machined part dimensional accuracy and geometric integrity. The developed simulation is capable of modelling complicated part geometries by interfacing with commercial ANSYS.V10® packages. This research work minimizes the deformation of workpiece using integrated optimization tool of Genetic algorithm (GA) and ANSYS Parametric Design Language (APDL) of finite element analysis. The same layouts given by the above optimization tool are used in the experimental setup and it is found that the improved geometric tolerance of squarness and flatness of the given workpiece. The chip removal effect and frictional contact between the workpiece and the fixture elements are taken into account based on element death technique and nonlinear finite-element analysis. A Case study of an open slot milling process illustrates the application of the proposed improved geometric tolerance approach.

Application of genetic algorithms and response surface analysis for the optimization of batch chromatographic systems

A trend to scientifically sound, robust and especially accelerated downstream process development in the biopharmaceutical industry is encouraged by the FDA [1,2]. The underlying methodology – high throughput process development – is an interplay of high throughput experimentation, usage of adequate analytics and model based experimentation or process development. In this study, high-throughput batch chromatographic methods were combined with design of experiments, genetic algorithm and response surface analysis to optimize a cation exchange step. The optimization was successful in batch mode and validated in packed bed column mode. Full automation was achieved by establishing a method to automate pH and salt concentration adjustment on liquid handling stations. Several process optima were identified by the genetic algorithm. It was demonstrated that the initial population design influenced the number of optima found during a genetic algorithm optimization procedure. The mere application of response surface analysis on the experimental results showed that for systems with several optima no distinct statements on parameter dependency are achieved. A combination of genetic algorithm, design of experiments and response surface analysis showed to be the most efficient data usage during process optimization if no information on the process landscape investigated is available.

Genetic Algorithm Design and Simulation for Job-Shop Scheduling Problem

This paper proposes an effective genetic algorithm for the job-shop scheduling problem (JSP) to minimize makespan time. An effective chromosome representation based on real coding is used to conveniently represent a solution of the JSP, and different strategies for selection, crossover and mutation are adopted. Simulation experimental results have shown that the scheduling model using the algorithm can allocate jobs efficiently and effectively.

Intelligent Multi-Objective Optimization for High Strength Sheet Metal Forming Process of Body Part

In high strength sheet metal forming process of body part, crack, wrinkle and severe thinning are the main faults usually. The degree of the faults varies with the change of input process parameters. Optimization for sheet metal forming process of body part is often considered as a multi-objective problem. Design of experiment method and genetic algorithm are often combined together to cope with this multi-objective optimization problem. High strength steel sheet metal forming process is relatively complex and difficult. An intelligent multi-objective optimization strategy for high strength sheet metal forming process was suggested based on genetic algorithm. Latin Hypercube Sampling method was introduced to design the rational experimental samples; the objective function was defined based on crack factor, wrinkle factor and severe thinning factor; the accurate response surface model for sheet metal forming problem was built; Multi-objective genetic algorithm was adopted in optimization and Pareto solution was selected. The strategy was applied to analyze a rocher. The result has proved this strategy suitable for optimization design of sheet metal forming process .

Intelligent Multi-Objective Optimization for High Strength Sheet Metal Forming Process of Body Part

In high strength sheet metal forming process of body part, crack, wrinkle and severe thinning are the main faults usually. The degree of the faults varies with the change of input process parameters. Optimization for sheet metal forming process of body part is often considered as a multi-objective problem. Design of experiment method and genetic algorithm are often combined together to cope with this multi-objective optimization problem. High strength steel sheet metal forming process is relatively complex and difficult. An intelligent multi-objective optimization strategy for high strength sheet metal forming process was suggested based on genetic algorithm. Latin Hypercube Sampling method was introduced to design the rational experimental samples; the objective function was defined based on crack factor, wrinkle factor and severe thinning factor; the accurate response surface model for sheet metal forming problem was built; Multi-objective genetic algorithm was adopted in optimization and Pareto solution was selected. The strategy was applied to analyze a rocher. The result has proved this strategy suitable for optimization design of sheet metal forming process .