Papers by Keyword: Simulated Annealing Algorithm

Abstract: In this study, the simulated annealing (SA) algorithm was adopted to optimize the geometry of horizontal plate fin heat sink by the extreme entransy dissipation principle. The alculation of the entransy dissipation rate was presented in detail. Using the entransy dissipation rate as the objective condition, the geometry optimization of the fin heat sink was conducted. To verify the results, the heat source temperature and the entropy generation rate were also calculated in the procedure. It is found that the entrasy dissipation rate, entropy generation and heat source temperature have the similar trend. The extreme entransy dissipation principle and minimization of entropy generation play similar roles in the geometry optimization of plate fin heat sink.

Abstract: With the popularity of e-commerce recently, how to control the operation cost has become an urgent issue need to be solved for all logistics company. This paper proposes an improved bin packing approach under the consideration of fuel consumption for delivery vehicle, to provide logistic company bin packing scheme with the least vehicle numbers and fuel consumption. First, with the consideration of controlling logistics costs, a multi-objective function is built to minimize the number of vehicles and the fuel consumption during delivery. Then a simulated annealing algorithm with improved neighborhood operations is proposed to solve the joint optimization problem. Finally, the effectiveness of the method is evaluated. The results show the proposed approach can effectively reduce logistics costs compared with the conventional bin packing approach and is helpful for reducing fuel consumption by balancing the weight among different vehicles.

Abstract: Logistics distribution involves preparing goods in the distribution center or logistics node for most reasonable delivery according to the requirements of customers. Genetic algorithm is a random global search algorithm based on the principle of natural evolution. It can be a good solution to optimize the distribution routes. This paper combines genetic algorithm and the simulated annealing algorithm, to which memory device is added, in order to avoid best result losing in the crossover operator of the genetic algorithm. The experimental results show that a memory function with this genetic simulated annealing algorithm in solving the logistics distribution routing problem, can not only get a higher qualified solution, but can also significantly reduce the evolutionary generation that algorithm requires, and obtain solution to the problem in less time.

Abstract: BP learning algorithm has advantage of simple structure, easy to implement and so on, it has gained wide application in the malfunction diagnosis and pattern recognition etc.. For BP algorithm is easy to fall into local minima shortcoming cites simulated annealing algorithm. Firstly, study the basic idea of BP learning algorithm and its simple mathematical representation; Then, research simulated annealing algorithm theory and annealing processes; Finally, the study makes BP algorithm combine with simulated annealing algorithm to form a hybrid optimization algorithm of simulated annealing algorithm based on genetic and improved BP algorithm, and gives specific calculation steps. The results show that the content of this study give full play to their respective advantages of two algorithms, make best use of the advantages and bypass the disadvantages, whether in academic or in the application it has a very important significance.

Abstract: This paper presents a search strategy for single mobile robots to realize the active olfaction (also called odor/gas source localization or plume tracing). The odor source localization is regarded as a kind of dynamic function optimization problem in this article, using the simulated annealing algorithm to calculate the optimal solution of density distribution function, namely the odor source location. The simulation experiments results in indoor ventilated environment show that the robot can track in plume and locate the odor source under the area of the 10m*10m, and it can effectively jump out of local maximum values in the process of search.

Abstract: Crew planning with complicated constraints is decomposed into two sequential phases: crew scheduling phase, crew rostering phase. Setting a dynamic model based on set covering model, Genetic Algorithm is adopted based on feasible solution range in search of optimal scheduling set with minimum time. Constructing a node-arc TSP network, it adopts Genetic Algorithm and Simulated Annealing Algorithm to create a work roster. Based on Wuhan-Guangzhou High-Speed Railway in China, the balance degree of crew planning is measured by crew working time entropy. The proposed model proves strong practical application.

Abstract: Genetic algorithms often cause premature convergence and can not give out global optimality in solving UC problems,while the simulated annealing algorithm shows better performance in accepting new solutions.By introducing annealing algorithm to the evaluation functions and the selection manipulation,the selection manipulation and the global convergency can be improved.Using decimal coding with no decoding,both the calculation error and time for calculation can be reduced.The example shows that this hybrid algorithm can improve the economic performance of UC scheme while various restrictions for security and reliability are still satisfied.

Abstract: Traveling salesman problem (TSP) is not only a combinatorial optimization problem but also a classical NP problem, which has has high application value. Simulated annealing algorithm is especially effective for solving TSP problems. Based on the deficiency of simulated annealing algorithm on avoiding local minima, this paper has improved the traditional simulated annealing algorithm, proposed simulated annealing algorithm of multiple populations to solve the classical TSP problem. This algorithm has introduced collateral mechanism of multiple populations and increased the initial populations so that it can include more solution set, avoid local minima, thus it has improved the optimization efficiency.This algorithm has very high use value in solving the TSP problem. Keywords: Traveling salesman problem, NP (Non-deterministic Polynomial) problem, simulated annealing algorithm, multiple populations

Abstract: Accurate identification of model parameters is to improve the giant magnetostrictive precision displacement control key, For single algorithm is difficult to achieve for giant magnetostrictive hysteresis nonlinear model parameters accurately identify problems, in this paper, the genetic algorithm and simulated annealing algorithm fusion, First, quick search ability of genetic algorithm are used to get a better community, recycle kick ability of simulated annealing algorithm to to adjust and optimize the whole group, Presented an improved genetic simulated annealing algorithm, And its application to the giant magnetostrictive actuator displacement hysteresis nonlinear model parameter identification. The algorithm combines the advantages of genetic algorithm and simulated annealing algorithm, both the faster convergence speed, and improves the precision and quality of the optimal solution.

Abstract: To master the variation regularity of finance, obtain greater benefits in stock investment. study of the support vector machine and application in prediction of stock market. The simulated annealing algorithm to optimize the least squares support vector machine prediction model, and the least square support vector machine and simulated annealing algorithm is described, given the optimal prediction model. Through the research on the simulation of the Hang Seng Index, shows that this method is simple, fast convergence, the algorithm with high accuracy. Has the actual guiding sense for investors, the stock market of the financial firm to operate.