Papers by Keyword: GA

Paper TitlePage

Authors: Bang Zhang, Xing Wei Wang, Min Huang
Abstract: In this paper, a multiple data replica placement scheme for cloud storage under health-care environment is proposed. It is based on p-center model with minimizing user access cost as its optimization objective. It uses GA (Genetic Algorithm) to find the optimal data replica placement solution. Simulation results have shown that it can not only improve user access time and promote load balance but also enhance system scalability and reliability.
Authors: Bashra Kadhim Oleiwi, Hubert Roth, Bahaa I. Kazem
Abstract: In this study, we developed an Ant Colony Optimization (ACO) - Genetic Algorithm (GA) hybrid approach for solving the Multi objectives Optimization global path planning (MOPP) problem of mobile robot. The ACO optimization algorithm is used to find the sub-optimal collision free path which then used as initial population for GA. In the proposed modified genetic algorithms, specific genetic operator such as deletion operator is proposed, which is based on domain heuristic knowledge, to fit the optimum path planning for mobile robots. The objective of this study is improving GA performance for efficient and fast selection in generating the Multi objective optimal path for mobile robot navigation in static environment. First we used the proposed approach to evaluate its ability to solve single objective problem in length term as well as we compared it with traditional ACO and simple GA then we extended to solve Pareto optimality ideas based on three criteria: length, smoothness and security, and making it Multi objective Hybrid approach. The proposed approach is tested to generate the single and multi objective optimal collision free path. The simulation results show that the mobile robot travels successfully from one location to another and reaches its goal after avoiding all obstacles that are located in its way in all tested environment and indicate that the proposed approach is accurate and can find a set Pareto optimal solution efficiently in a single run.
Authors: Meng Lan Wang
Abstract: Genetic algorithm is the most widely used and successful bionic optimization algorithm. In this paper we will discuss the tasks scheduling problem on equipments, establish a general mathematical model and put forward a hybrid genetic algorithm to solve this problem. The simulation results show the effectiveness of the hybrid genetic algorithm.
Authors: Xiao Quan Li, Mao Lin, Shuai Liu
Abstract: This paper presents a hybrid algorithm combining immune genetic algorithm (IGA) with simulated annealing (SA) to overcome the shortages of both the two algorithms respectively. SA is introduced to solve the problem of IGA in fault diagnosis, unable to reach whole convergence and etc. by designing a new kind of self-adaption strategy of genetic parameters. Finally, the Schaffer function is introduced to show the optimization ability of this proposed IGA-SA algorithm.
Authors: Tao Yan, Xian Min Lin, You Ping Zhong
Abstract: With regard to the slow-convergence disadvantage in the latter generations of GA, ACO is combined with GA in this paper to solve the optimal load allocation problem among thermal power plant units. By comparison with GA method, results show that GA-ACO has faster convergence than the GA method.
Authors: Dian Ting Liu, Hao Ping Hu
Abstract: The principle and main steps of module partition in green product configuration design under uncertainty, and how to determine the correlation between the basic elements of a product, and how to calculate the green degree of module are discussed in this paper. Let the maximum degree of cluster inner a module and the minimum coupling degree among modules and the maximum green degree of modules as the objective function, the mathematic model of uncertainly optimizing for green module partition is set up. And then It is transformed to an ascertainable combinatorial optimization model by de-fuzzy operator, and it is solved by GA (Genetic Algorithm,). The methods of chromosome encoding and the methods of selection and crossover and mutation operator are presented in this paper. A computational example is studied; its result verifies the effectiveness and practical value of the method proposed in this paper.
Authors: Wen Chin Chen, Yen Fu Lin, Pen Hsi Liou
Abstract: This study proposes an optimization system to find out the optimal process parameters of plastic injection molding (PIM). The system is divided into two phases. In the first phase, the Taguchi method and analysis of variance (ANOVA) are employed to perform the experimental work, calculate the signal-to-noise (S/N) ratio, and determine the initial process parameters. In the second phase, the back-propagation neural network (BPNN) is employed to construct an S/N ratio predictor. The S/N ratio predictor and genetic algorithms (GA) are integrated to search for the optimal parameter combination. The purpose of this stage is to reduce the process variance and promote product quality. Experimental results show that the proposed optimization system can not only satisfy the quality specification, but also improve stability of the PIM process.
Authors: Dong Yan Zhang, Chun Yan Zhang, Liang Kuan Zhu, Zhi Duo Diao
Abstract: This paper investigates the development and intelligent modeling problem for a wood drying kiln process via optimized support vector machine (SVM). Based on parameters optimization and model selection idea, the swarm intelligence algorithms of Particle Swarm Optimization (PSO)-SVM and Genetic Algorithm (GA)-SVM were proposed for wood drying process with strong coupling and nonlinear characteristics. The simulation results showed that both of these two kinds of swarm intelligence optimization algorithm could get the appropriate parameters of SVM effectively, and by contrast, PSO showed a better learning ability and generalization in wood drying process modeling, and could establish predictive model with better accessibility.
Authors: Li Dong Zhang, Lei Jia, Wen Xing Zhu
Abstract: This paper attempts to summarize the findings of a large number of research papers concerning the application of intelligent optimization algorithms to ITS. A brief introduction to intelligence is included, for the benefit of readers unfamiliar with the techniques. Then it put emphasis on three kinds of intelligent optimization application in ITS, including ANN, GA and PSO. It should be noted first that each of the three subjects can prolong to a long paper, and second that there are also some other intelligent optimization method, such as fuzzy logic, ant colony, shuffle frog-leaping algorithm On the constraint of time and paper volume, we only analyzed those three algorithms, their state-and-the-art use in ITS, and their future development trend.
Authors: Yu Mei Liu, Z. L. Jiang, Z. Li
Abstract: The residual stress is one important factor causing deformation and distortion. A mathematical model is presented. It predicts the surface residual-stress caused by end-milling. Response Surface Methodology (RSM) with the Takushi method is used to design experiment. The variance analysis (ANOVA) is conducted to determine the adequacy of the model. It is shown that the model offering good correlation between the experimental and predicted results, is useful in selecting suitable cutting parameters for milling aluminium alloy 6061.
Showing 1 to 10 of 79 Paper Titles