Integration Of Genetic Algorithm Research Articles

Development of hybrid evolutionary algorithms for production scheduling of hot strip mill

A hot strip mill (HSM) produces hot rolled products from steel slabs, and is one of the most important production lines in a steel plant. The aim of HSM scheduling is to construct a rolling sequence that optimizes a set of given criteria under constraints. Due to the complexity in modeling the production process and optimizing the rolling sequence, the HSM scheduling is a challenging task for hot rolling production schedulers. This paper first introduces the HSM production process and requirements, and then reviews previous research on the modeling and optimization of the HSM scheduling problem. According to the practical requirements of hot rolling production, a mathematical model is formulated to describe two important scheduling sub-tasks: (1) selecting a subset of manufacturing orders and (2) generating an optimal rolling sequence from the selected manufacturing orders. Further, hybrid evolutionary algorithms with integration of genetic algorithm (GA) and extremal optimization (EO) are proposed to solve the HSM scheduling problem. Computational results on industrial data show that the proposed HSM scheduling solution can be applied in practice to provide satisfactory performance.

A Graphics Processing Unit Accelerated Genetic Algorithm for Affine Invariant Matching of Broken Contours

Past research works have demonstrated matching of fragmented contours can be effectively accomplished with the integration of genetic algorithms and migrant principle. Despite the success, the computation involved in the evaluation of the fitness function is substantial. To overcome this problem, a new formulation on the fitness evaluation targeted for graphics processing unit (GPU) has been developed and presented in this paper. Experimental results reveal that the proposed solution is capable of reducing the matching time while maintaining high success rates.

A Hybrid Model Using Genetic Algorithm and Neural Network for Optimizing Technology Parameters in Skew Rolling Seamless Pipes

In the production of seamless pipes, there are many factors influencing the quality of the seamless tubes such as diameter of original pipe, feed angle, temperature, minimum roll gap and so on. Unfortunately, the selection of the parameters still relies on manual operation and the experience of engineers. In this paper, a novel hybrid model through integration of genetic algorithm(GA) and neural network is proposed to optimize the technology parameters. Firstly, the neural network model is developed between technology parameters and final pipes dimensions. Then, genetic algorithm is used to optimize the neural network structure. At last, accuracy of the parameters is verified by experiment. The research results indicate that the proposed approach can effectively help engineers determine optimal technology parameters and achieve competitive advantages of product quality and costs

Solving RFID Networks Scheduling Problems Using Hybird Binary Particle Swarm Optimization Algorithm

This paper presents an hybrid binary particle swarm optimization algorithm integration of genetic algorithms (HBPSO) to solve the RFID networks scheduling problems and get the optimal scheduling result in the problem. HBPSO is combined with advantages of binary PSO and GA. We use HBPSO to solve three problems of the RFID reader network and we attempt to minimize the total transaction time. By the results of the three problems, we can conclude that HBPSO is an effective algorithm which can find optimal solutions in the problem of the RFID reader network.

Directed optimization of biocatalytic transglycosylation processes by the integration of genetic algorithms and fermentative approaches into a kinetic model

Many biocatalysts exhibit strict stereospecificity and regioselectivity. However, their thermodynamically controlled equilibria often limit yields in industrial production processes. Herein, we describe the synthesis of fructooligosaccharides from sucrose by various fructansucrases. We previously demonstrated that transfructosylation to diverse acceptors yields d-glucose and the fructose-containing product along with diverse by-products. To streamline this reaction, we developed a procedure that allows the enhanced transfructosylation of diverse acceptors by different fructansucrases. By diverting the released glucose from the reaction via metabolism by living cells we limited the back reaction and forced the consumption of sucrose. The basic conditions for the resulting fermentation process were optimized by a genetic algorithm and integrated into a kinetic model. This strategy allows the prediction of optimal reaction parameters for the production of desired target compounds.

Process Yield Improvement Through Optimum Design of Fixture Layouts in 3D Multistation Assembly Systems

Fixtures control the positions and orientations of parts in an assembly process. Inaccuracies of fixture locators or nonoptimal fixture layouts can result in the deviation of a workpiece from its design nominal and lead to overall product dimensional variability and low process yield. Major challenges involving the design of a set of fixture layouts for multistation assembly system can be enumerated into three categories: (1) high-dimensional design space since a large number of locators are involved in the multistation system, (2) large and complex design space for each locator since the design space represents the area of a particular part or subassembly surfaces on which a locator is placed, (here, the design space varies with a particular part design and is further expanded when parts are assembled into subassemblies), and (3) the nonlinear relations between locator nominal positions and key product characteristics. This paper presents a new approach to improve process yield by determining an optimum set of fixture layouts for a given multistation assembly system, which can satisfy (1) the part and subassembly locating stability in each fixture layout and (2) the fixture system robustness against environmental noises in order to minimize product dimensional variability. The proposed methodology is based on a two-step optimization which involves the integration of genetic algorithm and Hammersley sequence sampling. First, genetic algorithm is used for design space reduction by estimating the areas of optimal fixture locations in initial design spaces. Then, Hammersley sequence sampling uniformly samples the candidate sets of fixture layouts from those predetermined areas for the optimum. The process yield and part instability index are design objectives in evaluating candidate sets of fixture layouts. An industrial case study illustrates and validates the proposed methodology.

A hybrid model using genetic algorithm and neural network for classifying garment defects

The inspection of semi-finished and finished garments is very important for quality control in the clothing industry. Unfortunately, garment inspection still relies on manual operation while studies on garment automatic inspection are limited. In this paper, a novel hybrid model through integration of genetic algorithm (GA) and neural network is proposed to classify the type of garment defects. To process the garment sample images, a morphological filter, a method based on GA to find out an optimal structuring element, was presented. A segmented window technique is developed to segment images into several classes using monochrome single-loop ribwork of knitted garment. Four characteristic variables were collected and input into a back-propagation (BP) neural network to classify the sample images. According to the experimental results, the proposed method achieves very high accuracy rate of recognition and thus provides decision support in defect classification.

Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption

This study presents an integrated algorithm for forecasting monthly electrical energy consumption based on genetic algorithm (GA), computer simulation and design of experiments using stochastic procedures. First, time-series model is developed as a benchmark for GA and simulation. Computer simulation is developed to generate random variables for monthly electricity consumption. This is achieved to foresee the effects of probabilistic distribution on monthly electricity consumption. The GA and simulated-based GA models are then developed by the selected time-series model. Therefore, there are four treatments to be considered in analysis of variance (ANOVA) which are actual data, time series, GA and simulated-based GA. Furthermore, ANOVA is used to test the null hypothesis of the above four alternatives being equal. If the null hypothesis is accepted, then the lowest mean absolute percentage error (MAPE) value is used to select the best model, otherwise the Duncan Multiple Range Test (DMRT) method of paired comparison is used to select the optimum model, which could be time series, GA or simulated-based GA. In case of ties the lowest MAPE value is considered as the benchmark. The integrated algorithm has several unique features. First, it is flexible and identifies the best model based on the results of ANOVA and MAPE, whereas previous studies consider the best-fit GA model based on MAPE or relative error results. Second, the proposed algorithm may identify conventional time series as the best model for future electricity consumption forecasting because of its dynamic structure, whereas previous studies assume that GA always provide the best solutions and estimation. To show the applicability and superiority of the proposed algorithm, the monthly electricity consumption in Iran from March 1994 to February 2005 (131 months) is used and applied to the proposed algorithm.

Integration of genetic algorithm and Gantt chart for job shop scheduling in distributed manufacturing systems

In a distributed manufacturing environment, jobs in a batch could usually be manufactured in several available factories and thus have multiple alternative process plans. This paper presents a new approach to determine good combinations of factories (process plans) to manufacture the jobs and in the meantime generate good operation schedules. A genetic algorithm (GA), integrated with Gantt chart (GC), is proposed to derive the factory combination and schedule. The integration of GA–GC is proved to be efficient in solving small-sized or medium-sized scheduling problems for a distributed manufacturing system. Multiple objectives can be achieved, including minimizing makespan, job tardiness, or manufacturing cost. An illustrative example is given to demonstrate and evaluate the performance of the GA–GC approach.

Integration of genetic algorithm and a coactive neuro-fuzzy inference system for permeability prediction from well logs data

Permeability is one of the reservoir fundamental properties, which relate to the amount of fluid contained in a reservoir and its ability to flow. These properties have a significant impact on petroleum fields operations and reservoir management. The most reliable data of local permeability are taken from laboratory analysis of cores. Extensive coring is very expensive and this expense becomes reasonable in very limited cases. Thus, the proper determination of the permeability is of paramount importance because it affects the economy of the whole venture of development and operation of a field. In this study, we introduce a new hybrid network based on Coactive Neuro-Fuzzy Inference System (CANFIS). CANFIS is a dependable and robust network that developed to identify a non-linear relationship and mapping between petrophysical data and core samples. Then to improve the system performance, genetic algorithm (GA) was integrated in order to search of optimal network parameters and decrease of noisy data in training samples. An Iranian offshore gas field is located in the Persian Gulf, has been selected as the study area in this paper. Well log data are available on substantial number of wells. Core samples are also available from a few wells. It was shown that the new proposed strategy is an effective method in predicting permeability from well logs.

Genetic algorithm based multi-agent system applied to test generation

Automatic test generating system in distributed computing context is one of the most important links in on-line evaluation system. Although the issue has been argued long since, there is not a perfect solution to it so far. This paper proposed an innovative approach to successfully addressing such issue by the seamless integration of genetic algorithm (GA) and multi-agent system. In the design phase, a test ontology was firstly defined for smoothing the communication among agents. For the implementation of GA, The fitness function and the structure of chromosome were identified on the basis of the analysis of constraint conditions associated with a test. To demonstrate the task execution flow and messages passing among agents, the activity diagram and sequence diagram were also shown on the AUML basis. In the phase of implementation, the JADE based agent behavior model was described in detail and the implementation platform was also demonstrated. The final simulation results validated the feasibility of the proposed approach.

Integration of genetic algorithms and GIS for optimal location search

Optimal location search is frequently required in many urban applications for siting one or more facilities. However, the search may become very complex when it involves multiple sites, various constraints and multiple‐objectives. The exhaustive blind (brute‐force) search with high‐dimensional spatial data is infeasible in solving optimization problems because of a huge combinatorial solution space. Intelligent search algorithms can help to improve the performance of spatial search. This study will demonstrate that genetic algorithms can be used with Geographical Information systems (GIS) to effectively solve the spatial decision problems for optimally sitting n sites of a facility. Detailed population and transportation data from GIS are used to facilitate the calculation of fitness functions. Multiple planning objectives are also incorporated in the GA program. Experiments indicate that the proposed method has much better performance than simulated annealing and GIS neighborhood search methods. The GA method is very convenient in finding the solution with the highest utility value.

Optimization of homogeneous charge compression ignition with genetic algorithms

Homogeneous charge compression ignition (HCCI) engines promise high efficiency and low emissions, and thus these engines hold great potential for reducing pollution in electric power generation, trucks, marine vehicles, locomotives, and automobiles. Controlling the time of the HCCI combustion event remains a major technical difficulty yet to be overcome. An integration of genetic algorithms (GAs) with well-mixed reactor simulations is developed for better understanding HCCI combustion and for guiding the development of optimal controls. With GAs, the effects of engine intake charge on engine performance are explored with three fuels: methane, propane, and acetylene. Simulation results are compared to available experimental data showing that model predictions are consistent with known trends. As the first application, GAs are used for searching the optimal intake charge conditions of HCCI combustion for power generation with methane. Results suggest that the use of intake charges with high equivalence ratio and with large amounts of exhaust gas recirculation is optimal. Subsequently, parameter optimization for intake conditions with the stoichiometric mixture at various power demand is explored. The results are analyzed and insights on the optimal managing schemes are revealed.

Voltage and Reactive Power Control by Integration of Genetic Algorithm and Tabu Search

This paper presents on the result of voltage and reactive power control by use of the proposed method. The feature of proposed method is integration of genetic algorithm (GA) and tabu search (TS). This method obtains an excellent fitness at shorter calculation time than GA considering conventional control process. The effectiveness of this method is shown by a practicable 15-bus system.

Demarcation of potential seismic sources on integration of genetic algorithm and BP algorithm

In this paper potential seismic sources in coastal region of South China are identified by integration of genetic algorithm (GA) and back propagation (BP algorithm). GA is used for finding the best parameter combination rapidly in an infinite solution space for artificial neural networks (ANN). The results show that the distribution of potential seismic sources with different upper magnitude demarcated by this classifier is mostly satisfied the intrinsic relationship between seismic environment and earthquake occurrence, with less effect from subjective judgment of human being.

Fusion of qualitative bond graph and genetic algorithms: A fault diagnosis application

Abstract In this paper, the problem of fault diagnosis via integration of genetic algorithms (GA's) and qualitative bond graphs (QBG's) is addressed. We suggest that GA's can be used to search for possible fault components among a system of qualitative equations. The QBG is adopted as the modeling scheme to generate a set of qualitative equations. The qualitative bond graph provides a unified approach for modeling engineering systems, in particular, mechatronic systems. In order to demonstrate the performance of the proposed algorithm, we have tested the proposed algorithm on an in-house designed and built floating disc experimental setup. Results from fault diagnosis in the floating disc system are presented and discussed. Additional measurements will be required to localize the fault when more than one fault candidate is inferred. Fault diagnosis is activated by a fault detection mechanism when a discrepancy between measured abnormal behavior and predicted system behavior is observed. The fault detection mechanism is not presented here.

Development and application of a hybrid genetic algorithm for resource optimization and management

Resource selection/optimization problems are often characterized by two related problems: numerical function and combinatorial optimization. Although techniques ranging from classical mathematical programming to knowledge‐based expert systems (KBESs) have been applied to solve the function optimization problem, there still exists the need for improved solution techniques in solving the combinatorial optimization. This paper reports an exploratory work that investigates the integration of genetic algorithms (GAs) with organizational databases to solve the combinatorial problem in resource optimization and management. The solution strategy involved using two levels of knowledge (declarative and procedural) to address the problems of numerical function, and combinatorial optimization of resources. The research shows that GAs can be effectively integrated into the evolving decision support systems (DSSs) for resource optimization and management, and that integrating a hybrid GA that incorporates resource economic and productivity factors, would facilitate the development of a more robust DSS. This helps to overcome the major limitations of current optimization techniques such as linear programming and monolithic techniques such as the KBES. The results also highlighted that GA exhibits the chaotic characteristics that are often observed in other complex non‐linear dynamic systems. The empirical results are discussed, and some recommendations given on how to achieve improved results in adapting GAs for decision support in the architecture, engineering and construction (AEC) sector.

Qualitative system identification with the use of on-line genetic algorithms

The major problem in building qualitative models via on-line qualitative system identification is how to filter the spurious constraints that are generated from the qualitative reasoning technique. This paper proposes a solution to this problem by an integration of genetic algorithms (GA) and qualitative reasoning. The paper will demonstrate the use of qualitative reasoning to partition the input quantity space into different subsystems, and implementation of GAs to filter and optimize the predicted constraints. The proposed method is verified by simulated examples that suggest the algorithm converges to the optimal point with high speed.