Papers by Keyword: Hybrid Genetic Algorithm

Paper TitlePage

Authors: Juan Li
Abstract: To effectively predict cascading failure in power system, a cascading failure prediction method in power system based on multi-agent and hybrid genetic algorithm is constructed. A cascading failure prediction procedure in power system was established by multi-agent and hybrid genetic algorithm to investigate the emergent behaviors of cascading failures and to further study the prediction and defense of cascading failures. Finally, the cascading failure prediction simulation system of power system based on this method was demonstrated and validated by Flexsim software. The result showed that the proposed method was available, and can provided guidance for avoiding and predict cascading failure in power system, and support for stable performance in power system.
Authors: Dong Jing Miao, Liao Wu, Ken Chen, Guo Lei Wang
Abstract: Obstacle avoidance is an important content in the research of redundant manipulator. To automate spraying for inner surface in a complex curved pipe, its a challenge to plan collision-free trajectory for the manipulator. A hybrid planning algorithm based on genetic algorithm and pseudo-inverse method is presented in this paper. Firstly, the collision problem between the manipulator and curved pipe is simplified to collision detection problem between polyhedron. The manipulator joints are represented by bounding volumes, and the curved pipe is deemed to polyhedron. Secondly, in the hybrid genetic algorithm, the initial population is obtained by using the pseudo-inverse method, by establishing the distance relationship between the vertices of the bounding volume and its cross-sectional plane polygon. The fitness function is constructed to evaluate the collision situation between the manipulator and pipe. Via the continuous evolution of the population, the collision-free trajectory is obtained finally.
Authors: Yong Zhan, Chang Hua Qiu, Kai Xue
Abstract: This paper considers the practical manufacturing environment of the hybrid flow shop (HFS) with non-identical machines in parallel. In order to significantly enhance the performance level of manufacturing, maintaining load balancing among parallel machines is very important. The aim of this paper is to minimize makespan with load balancing in a non-identical parallel machine environment by using hybrid genetic algorithm (HGA). In the HGA, the neighborhood search-based method is used together with genetic algorithm as local optimization method to balance the exploration and exploitation abilities. The representation of chromosome used in this paper is composed of two layers: allocation layer and sequencing layer, which can be encode and decoded easily. In generating initial population, a special constraint of load balancing between parallel machines is used to reduce the number of individuals. And particular crossover operation is used, which generates multiple offspring at a time, so that the efficiency of the algorithm can be well improved. At last, the proposed algorithm is tested on a benchmark, and numerical example shows good result.
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: Xian Zhou Cao, Zhen He Yang
Abstract: In this paper, a dual-resource constrained job shop scheduling problem was studied by designing a hybrid genetic algorithm based on Genetic Algorithm (GA) and Simulated Annealing (SA). GA is used to search for a group of better solutions to the problem of minimizing production cost and then SA is applied to searching them for the best one. The combination of GA and SA utilizes the advantages of the two algorithms and overcomes their disadvantages. The operation-based encoding and an active schedule decoding method were employed. This hybrid genetic algorithm reasonably assigns the resources of machines and workers to jobs and achieves optimum on some performance. The results of numerical simulations, which are compared with those of other well-known algorithms, show better performance of the proposed algorithm.
Authors: Shu Zhi Nie, Bang Yan Ye
Abstract: In this paper, built mathematical model on Flow Shop scheduling, put forward a RNA genetic algorithm based on DNA computing to solve the Flow Shop scheduling problems. Adopt RNA four digit system encoding method based on DNA computing and RNA computing operator in genetic algorithm. It resolved the encoding scheme and convergence problem which exists in the conventional genetic algorithm. Under some constraint conditions, this genetic algorithm got simulated. Simulation results showed that this algorithm has a better optimum searching and seeking abilities, made the scheduling results comparatively reasonable and expanded the application of DNA computing.
Authors: Shun Fa Hwang, Cyuan Kuan Yeh, Rong Song He
Abstract: Combining vibration testing and numerical method is a potential inverse technique for determining elastic constants of materials because of its nondestructive characteristic, single test, and producing average properties. In order to simplify the modeling processes and to reduce complicated derivation in the numerical method, the combination of finite element analysis and optimum design is adopted in this work. A finite element package, ANSYS, is used to do the modal analysis of the composite plate. A hybrid genetic algorithm, in which a simulated annealing mutation process and adaptive mechanisms are added to the real-parameter genetic algorithm, is used to search the possible elastic constants. After obtaining the natural frequencies of the composite plates from vibration testing, this inverse technique could predict the elastic constants of the composite plate. The inverse technique is verified by comparing with other methods and by determining the elastic constants of aluminum plates, and the excellence of including the hybrid genetic algorithm is proved. The results also indicate that the present technique could obtain very accurate elastic constants of composite plates.
Authors: J. C. Wang, H. Qiu, J. M. Chen, G. D. Ji
Abstract: Reliability allocation optimization problem of a complex mechatronic system is a highly nonlinear constrained optimization problem, and hence solution to this kind of problem is of NP-hardness even with moderate scale. Due to the nonlinearity combined with multiple local extreme values, traditional optimization techniques fail to arrive at the global or nearly global optimal solution to the problem. Genetic algorithm incorporated with neighboring domain traversal searching technique is utilized in this paper to solve the complex mechatronic system reliability optimization allocation problem. Reliability allocation optimization of the life-support system in a space capsule, being a typical non serial-parallel system, is specifically demonstrated to show the satisfactory convergence performance as well as the important practical value of hybrid genetic algorithm. The simulation results show that the proposed method may gain better precision in solving the complex mechatronic system reliability optimization problem.
Authors: Bo Liu, Bo Li, Yan Li
Abstract: A bilevel programming model is established to determine the emergency storage centers location and the resource supply plan of the provincial and municipal levels by the collaborative mode of the vertical supply and lateral transfer for the emergency logistics system in the unusual emergencies. And the optimal solution is obtained by the hybrid genetic algorithm. Finally, the case shows the effectiveness of the proposed model and its algorithm.
Authors: Ali Rahimi Fard, Babak Yousefi Yegane, Narges Khanlarzade
Abstract: Flexible job shop scheduling problem )FJSP) is an extension of the classical job shop scheduling problem which allows an operation to be processed by any machine from a given set. FJSP is NP-hard and mainly presents two difficulties. The first one is to assign each operation to a machine out of a set of capable machines, and the second one deals with sequencing the assigned operations on the machines. However, it is quite difficult to achieve an optimal solution to this problem in medium and large size problems with traditional optimization approaches. In this paper a memetic algorithm )MA) for flexible job shop scheduling with overlapping in operation is proposed that solves the FJSP to minimize makespan time. The experimental results show that the proposed algorithm is capable to achieve the optimal solution for small size problems and near optimal solutions for medium and large size problems
Showing 1 to 10 of 33 Paper Titles