scholarly journals Combining Pixel Swapping and Simulated Annealing for Land Cover Mapping

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1503 ◽  
Author(s):  
Lijuan Su ◽  
Yue Xu ◽  
Yan Yuan ◽  
Jingyi Yang
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Spatial Information ◽  
Local Optimum ◽  
Mapping Accuracy ◽  
Mixed Pixels ◽  
Annealing Algorithm ◽  
Land Covers ◽  
Subpixel Mapping ◽  
Optimum Solution

Mixed pixels commonly exist in low-resolution remote sensing images, and they are the key factors hindering the classification of land covers and high-precision mapping. To obtain the spatial information at the subpixel level, subpixel mapping (SPM) technologies, including the pixel-swapping algorithm (PSA), that use the unmixed proportions of various land covers and allocate subpixel land covers have been proposed. However, the PSA often falls into a local optimum solution. In this paper, we propose a SPM method, the PSA_MSA algorithm, that combines the PSA and the modified simulated annealing algorithm to find the global optimum solution. The modified simulated annealing algorithm swaps subpixels within a certain range to escape the local optimum solution. The method also optimizes all the mixed pixels in a randomized sequence to further improve the mapping accuracy. The experimental results demonstrate that the proposed PSA_MSA algorithm outperforms the existing PSA-based algorithms for SPM. The images with different spatial dependences are tested and the results show that the proposed algorithm is more suitable for images with high spatial autocorrelation. In addition, the effect of proportion error is analyzed by adding it in the experiments. The result shows that a higher proportion error rate leads to larger degradation of the subpixel mapping accuracy. Finally, the performance of PSA_MSA algorithm with different ranges of selection on subpixel-swapping is analyzed.

scholarly journals The Parameters Optimization of MCR-WPT System Based on the Improved Genetic Simulated Annealing Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Sheng Lu ◽  
Chenyang Zuo ◽  
Changhao Piao
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Global Optimum ◽  
Parameters Optimization ◽  
Selection Strategy ◽  
Local Optimum ◽  
Penalty Function Method ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm ◽  
Optimum Solution

To solve the problem of parameter selection during the design of magnetically coupled resonant wireless power transmission system (MCR-WPT), this paper proposed an improved genetic simulated annealing algorithm. Firstly, the equivalent circuit of the system is analysis in this study and a nonlinear programming mathematical model is built. Secondly, in place of the penalty function method in the genetic algorithm, the selection strategy based on the distance between individuals is adopted to select individual. In this way, it reduces the excess empirical parameters. Meanwhile, it can improve the convergence rate and the searching ability by calculating crossover probability and mutation probability according to the variance of population’s fitness. At last, the simulated annealing operator is added to increase local search ability of the method. The simulation shows that the improved method can break the limit of the local optimum solution and get the global optimum solution faster. The optimized system can achieve the practical requirements.

Research on Image Technology with Application of K-Means Based on Genetic Simulated Annealing Algorithm in CT Image Segmentation

2014 ◽  
Vol 1022 ◽  
pp. 269-272
Author(s):  
Ling Li Zhu ◽  
Lan Wang
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Simulated Annealing ◽  
Clustering Analysis ◽  
Simulated Annealing Algorithm ◽  
Local Optimum ◽  
Initial Condition ◽  
Ct Image ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

Aiming at the characteristic of medical images, this paper presents the improved genetic simulated annealing algorithm with K-means clustering analysis and applies in medical CT image segmentation. This improved genetic simulated annealing algorithm can be used to globally optimize k-means image segmentation functions to solve the locality and the sensitiveness of the initial condition. It can automatically adjust the parameters of genetic algorithm according to the fitness values of individuals and the decentralizing degree of individuals of the population and keep the variety of population for rapidly converging, and it can effectively avoid appearing precocity and plunging into local optimum. The example shows that the method is feasible, and better segmentation results have got to satisfy the request for 3D reconstruction, compared with k-means image segmentation and genetic algorithm based image segmentation.

A hybrid differential evolution and simulated annealing algorithm for global optimization

2021 ◽  
pp. 1-17
Author(s):  
Xiaobing Yu ◽  
Zhenjie Liu ◽  
XueJing Wu ◽  
Xuming Wang
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Differential Evolution ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Local Optimum ◽  
Search Efficiency ◽  
Optimal State ◽  
Mutation Operation ◽  
Annealing Algorithm

Differential evolution (DE) is one of the most effective ways to solve global optimization problems. However, considering the traditional DE has lower search efficiency and easily traps into local optimum, a novel DE variant named hybrid DE and simulated annealing (SA) algorithm for global optimization (HDESA) is proposed in this paper. This algorithm introduces the concept of “ranking” into the mutation operation of DE and adds the idea of SA to the selection operation. The former is to improve the exploitation ability and increase the search efficiency, and the latter is to enhance the exploration ability and prevent the algorithm from trapping into the local optimal state. Therefore, a better balance can be achieved. The experimental results and analysis have shown its better or at least equivalent performance on the exploitation and exploration capability for a set of 24 benchmark functions. It is simple but efficient.

Artificial Intelligence Application in Modern Management

2010 ◽  
Vol 37-38 ◽  
pp. 203-206
Author(s):  
Rong Jiang
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Local Optimum ◽  
Efficient Operation ◽  
Modern Management ◽  
Global Optimal ◽  
Annealing Algorithm

Modern management is a science of technology that adopts analysis, test and quantification methods to make a comprehensive arrangement of the limited resources to realize an efficient operation of a practical system. Simulated annealing algorithm has become one of the important tools for solving complex optimization problems, because of its intelligence, widely used and global search ability. Genetic algorithm may prevent effectively searching process from restraining in local optimum, thus it is more possible to obtains the global optimal solution.This paper solves unconstrained programming by simulated annealing algorithm and calculates constrained nonlinear programming by genetic algorithm in modern management. So that optimization process was simplified and the global optimal solution is ensured reliably.

scholarly journals Optimization of Healthy Diet Menu Variation using PSO-SA

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Imam Cholissodin ◽  
Ratih Kartika Dewi
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Optimal Parameter ◽  
Pso Algorithm ◽  
Healthy Diet ◽  
Local Optimum ◽  
The Family ◽  
Lower Complexity ◽  
Annealing Algorithm

Abstract. Optimal healthy diet in accordance with the allocation of cost needed so that the level of nutritional adequacy of the family is maintained. The problem of optimal healthy diet (based on family budget) can be solved with genetic algorithm. The algorithm particle swarm optimization (PSO) has the same effectiveness with genetic algorithm but PSO is superior in terms of efficiency, PSO algorithm has a lower complexity than genetic algorithm. However, genetic algorithms and PSO have a problem of local optimum because these algorithm associated with random numbers. To overcome this problem, PSO algorithm will be improved by combining it with simulated annealing algorithm (SA). Simulated annealing algorithm is a numerical optimization algorithms that can avoid local optimal. From our results, optimal parameter for PSO-SA are popsize 280, crossover rate 0.6, mutation rate 0.4, first temperature 1, last temperature 0.2, alpha 0.9, and generation size 100.Keywords: PSO, SA, optimization, variation, healthy diet menu.

Research on the Initial Value of the Simulated Annealing

2013 ◽  
Vol 774-776 ◽  
pp. 1770-1773
Author(s):  
Xiao Wen Liang ◽  
Wei Gong ◽  
Wen Long Fu ◽  
Jing Qi
Keyword(s):  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Hill Climbing ◽  
Local Optimum ◽  
Initial Value ◽  
Good Convergence ◽  
Annealing Algorithm

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.

A Hybrid Heuristic Algorithm for the Intelligent Transportation Scheduling Problem of the BRT System

2015 ◽  
Vol 24 (4) ◽  
pp. 437-448 ◽  
Author(s):  
Zheng Ning ◽  
Chen Tao ◽  
Lin Fei ◽  
Xu Haitao
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Heuristic Algorithm ◽  
Simulated Annealing Algorithm ◽  
Local Optimum ◽  
Scheduling Problem ◽  
Selection Probability ◽  
Hybrid Heuristic ◽  
Hybrid Heuristic Algorithm ◽  
Annealing Algorithm

AbstractThis work proposes a hybrid heuristic algorithm to solve the bus rapid transit (BRT) intelligent scheduling problem, which is a combination of the genetic algorithm, simulated annealing algorithm, and fitness scaling method. The simulated annealing algorithm can increase the local search ability of the genetic algorithm, so as to accelerate its convergence speed. Fitness scaling can reduce the differences between individuals in the early stage of the algorithm, to prevent the genetic algorithm from falling into a local optimum through increasing the diversity of the population. It can also increase the selection probability of outstanding individuals, and speed up the convergence at the late stage of the algorithm, by increasing the differences between individuals. Using real operational data of BRT Line 1 in a city of Zhejiang province, the new scheduling scheme can be obtained through algorithm simulation. The passengers’ total waiting time in a single way will be reduced by 40 h on average under the same operating cost compared with the original schedule scheme in a day.

scholarly journals Applying Pareto Frontier to Determine Control Strategy of Technical Objects Operation Process

2017 ◽  
Vol 44 (1) ◽  
pp. 37-57
Author(s):  
Klaudiusz Migawa ◽  
Leszek Knopik ◽  
Andrzej Neubauer ◽  
Agnieszka Sołtysiak
Keyword(s):  
Simulated Annealing ◽  
Process Control ◽  
Markov Processes ◽  
Control Strategy ◽  
Simulated Annealing Algorithm ◽  
Pareto Frontier ◽  
Operation Process ◽  
Annealing Algorithm ◽  
Optimum Solution ◽  
Semi Markov Processes

Abstract The paper deals with the multi-criteria model of technical objects operation process control in which the choice of control strategy is made with the use of nondeterministic methods. The decisive model was created with the use of decisive theories of semi-Markov processes. The choice of the optimum (quasi-optimum) solution is made with the use of simulated annealing algorithm. As a result of numeric calculations for used criteria functions (availability, risk), a set of quasi-optimum solutions in the form of Pareto frontier is obtained.

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