Papers by Keyword: Hybrid Genetic Algorithm

Abstract: This paper uses VB software and accounting information transparency to improve BOT investment mode and establishes computer accounting algorithm using the form of software programming. It also uses the least squares SCF criterion to improve this algorithm and gets the BOT investment accounting information platform after the optimization. The platform mainly focuses on openness of accounting information transparency and account investment efficiency and economic benefit of BOT investment mode using the form of computer array computing. At last, through the calculation, this paper concludes the convergence curve of risk aversion and economic efficiency and analyzes the benefits of BOT investment in risk management and supply chain which provides a theoretical reference for the study of accounting information transparency.

Abstract: In view of the characteristic of goods return in electronic commerce which some returned goods can be redistributed directly to customers after handling simply, a responsive closed-loop supply chain logistics network with integrating forward and reverse flow is researched. The returning feature of online commodity and economy of scale of return goods at initial collection point are based, and the optimization design model of closed-loop supply chain network aiming at determining the number and location of hybrid centers and initial collection points is presented, and a hybrid genetic algorithm is devised to solve the closed-loop supply chain logistics network problem. The usefulness of the proposed model and algorithm is validated by its application to an illustrative example.

Abstract: A chance-constrained vehicle scheduling model for fresh agriculture products pickup with uncertain demands is proposed in this paper. The uncertain measure that vehicle loading will not exceed capacity constraint is presented in the model because of the uncertainty of demands. Based on uncertainty theory, when the demands are some special uncertain variables with uncertainty distribution such as linear, zigzag and normal uncertain distribution etc., the model can be transformed to a deterministic form and solved by genetic algorithm. When the demands are general uncertain variables, a hybrid genetic algorithm with uncertain simulation is presented to obtain the optimal solution. At last, to illustrate the effective of the model and algorithm, and to analyze the impact of parameters on model solution, an experiment is provided.

Abstract: On account of low convergence of the traditional genetic algorithm in the late,a hybrid genetic algorithm based on conjugate gradient method and genetic algorithm is proposed.This hybrid algorithm takes advantage of Conjugate Gradient’s certainty, but also the use of genetic algorithms in order to avoid falling into local optimum, so it can quickly converge to the exact global optimal solution. Using Two test functions for testing, shows that performance of this hybrid genetic algorithm is better than single conjugate gradient method and genetic algorithm and have achieved good results.

Abstract: We studied the logistics vehicle scheduling.We have used Unified Modeling Language (UML) for modeling and java agent development framework (JADE) to realize the Agent server-side interactive collaboration features and set up multi-Agent scheduling platform,and have also used the hybrid genetic algorithm to optimize the scheduling task. Through testing examples as well as the combination use of MATLAB and Visual C+ +, the vehicle Gantt chart has been generated to inspect the performance of optimization algorithm.

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.

Abstract: We introduce basic theory of genetic algorithms in this paper, Build one hybrid genetic algorithms model base on genetic algorithms in solving product optimization problem. And we realize the model by software. The disposal result of real data tells us that the model has good practicability and accuracy to the problem of product optimization problem.

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.

Abstract: Taking the minimum to cylinder thrust force, turntable force and boom force as the objective function then establish the optimization mathematical model of the verifying three nodes, taking BP Neural Network as the main method instead of the cumbersome formula derivation. This article puts forward a Hybrid Genetic Algorithm flow set of solving pareto optimal solution, It is achieved by mixed-using Niche Technology, Groups Sorting Technology. The optimal position of the arm verifying three nodes is conformed by programming using Matlab genetic algorithm toolbox. And the force of the fuel tank , boom and turntable is effectively mitigated. This gives a appropriate reference for the next boom verifying three nodes position to determine and the optimal design of similar structures in other engineering machinery.

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.