Papers by Keyword: Simulated Annealing Algorithm

Abstract: The model of the electric vehicle charging station location is built in this paper. The objective function is charging station utility maximization, the constraint condition is traffic flow, and simulated annealing algorithm is used to solve this model. The result shows that this model is reasonable to provide the electric vehicle project managers with an optimal or approximate optimal location solution.

Abstract: Site selection for the professional teams of civil air defense is an important part of layout of works for professional team of civil air defense. In this paper firstly analyze the main influencing factors of site selection. Then construct the model by the way of entropy method and multi-objective linear programming, which is optimized and solved by the Simulated annealing algorithm programming with Matlab. Finally we have an instance of civil air defense in a second category city to illuminate.

Abstract: The location routing problem (LRP), which simultaneously tackles both facility location and the vehicle routing decisions to minimize the total system cost, is of great importance in designing an integrated logistic distribution network. In this paper a simulated annealing algorithm (SA) based hybrid genetic algorithm was developed to solve the LRP with capacity constraints (CLRP) on depots and routes. The proposed hybrid genetic algorithm modified the population generation method, genetic operators and recombination strategy and realized the combination of the local searching ability of SA and global searching ability of GA. To evaluate the performance of the proposed approach, we conducted an experimental study and compared its results with other heuristics on a set of well-known Barreto Benchmark instances. The experimental results verified the feasibility and effectiveness of our approach.

Abstract: This paper transfer the area target scheduling problem into maximal coverage problem based on summaring the traditional sovling problem. A MIP model is build based on problem characters; simulated anneanling problem is used to solve the problem. Four neighborhood and tow differentiation mechanisms are designed to fit the study problem, such as offset neighborhood. The relationship between coverage and overlap and division angle is analysised by test data, and algorithm validation and effective is test based on example data.

Abstract: Based on the common used coals and designed coal in a power plant in Hunan, a non-linear coal blending model was built. Exhaust algorithm, genetic algorithm and simulated annealing algorithm were used to solve the model respectively using the lowest cost of the blended coal as the objective function. Three kinds of single coals were blended together according to certain proportions based on 15 kinds of common used single coals. The coal characteristics of blended coals were predicted by the model of General Regression Neural Network. The cost, fitness and predicting time obtained by three different algorithms were compared and analyzed. The results show that the solution is ideal in cost and fitness with exhaust algorithm while the predicting time is very long. GA algorithm is good in cost, computing time and reliability.

Abstract: Simulated Annealing Algorithm is one of the top ten classical optimization algorithm, and it has been successfully applied to various fields. Simulated annealing is a optimization algorithm which can find the global optimal solution, compares to neural network algorithm, it is so easily to implement that has higher probability to be adopted, but it has own shortcomings like other optimization algorithms, its result largely depends on initial value, The initial value of the traditional simulated annealing algorithm began with a random number, its convergence speed is often slow very much and the effect is bad. In this paper, a new simulated annealing algorithm that based on genetic algorithm and hill-climbing method was brought up, because of hill-climbing algorithm was easy to fall into local optimum, and simulated annealing can just solve the problem, it not only escaped from local optimum, but also got good convergence speed and results.

Abstract: The Independent Component Analysis (ICA) is a classical algorithm for exploring statistically independent non-Gaussian signals from multi-dimensional data, which has a wide range of applications in engineering, for instance, the blind source separation. The classical ICA measures the Gaussian characteristic by kurtosis, which has the following two disadvantages. Firstly, the kurtosis relies on the value of samples, and is not robust to outliers. Secondly, the algorithm often falls into local optima. To address these drawbacks, we replace the kurtosis by negative entropy, utilize the simulated annealing algorithm for optimization, and finally propose an improved ICA algorithm. Experimental results demonstrate that the proposed algorithm outperforms the classical ICA in its robustness to outliers and convergent rate.

Abstract: Because there are some differences in operation management body and investment body between city railway/suburban railway and other modes of transportation of urban rail transit, reasonable coordination and effective connection cant achieve in various modes of transportation of urban rail transit, resulting in not only wasting national limited traffic resource, but also bringing so much inconvenience to the traveling passengers. Under this background, the author proposes the alternative use mode of building integrative urban passenger rail transit system and their definitions. Then, the bi-level programming model of the integrative passenger rail transit line station layout based on the alternative use mode is established. Whats more, the simulated annealing algorithm is used to solve it. Finally, a numerical example shows that the pattern of multi-point stopping under alternative use mode is beneficial to reduce total consumption of passenger agglomeration and shorten the passenger traveling time, at the same time it is more beneficial to ease the urban traffic pressure (especially the city ground traffic).

Abstract: The core technology of apparel layout is the intelligent algorithm of apparel marker software, this paper puts forward an optimized particle swarm algorithm under the condition of comparing and analyzing the merits and drawbacks of genetic algorithm, simulated annealing algorithm and particle swarm optimization algorithm, in addition, a demonstration test is done by C++ under the environment of VS2007. The result concluded in this paper shows that the optimized particle swarm algorithm can achieve ideal material utilization.

Abstract: The simulated annealing algorithm is applied on traveling salesman problem (TSP), which the genetic algorithm solving in while the earliness phenomena appear. Modeling and Simulation about TSP Based on Simulated Annealing Algorithm have been done. The simulation results have proved that the simulated annealing algorithm is better in searching in the global searching than the genetic algorithm.