Research on Non-Linear Power Coal Blending Model and its Algorithm

2013 ◽  
Vol 787 ◽  
pp. 71-74
Author(s):  
Ai Chun Ma ◽  
Jie Yang ◽  
Dong Nan Chen ◽  
Jian Ping Ou
Keyword(s):  
Simulated Annealing Algorithm ◽  
Computing Time ◽  
General Regression Neural Network ◽  
Blended Coal ◽  
Ga Algorithm ◽  
Coal Blending ◽  
Non Linear ◽  
Annealing Algorithm ◽  
The Cost ◽  
General Regression

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.

scholarly journals Optimization of Ordering and Allocation Scheme for Distributed Material Warehouse Based on IGA-SA Algorithm

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1746
Author(s):  
Han Jiang ◽  
Yunlong Wu ◽  
Qing Zhang
Keyword(s):  
Simulated Annealing Algorithm ◽  
Cost Model ◽  
Allocation Scheme ◽  
Improved Genetic Algorithm ◽  
Inventory Cost ◽  
Safety Stock ◽  
Modern Enterprise ◽  
Annealing Algorithm ◽  
The Cost ◽  
Optimal Ordering

The distributed material warehouse is the crucial link in the process of modern enterprise construction, and the goal of the enterprise is to save the cost of material distribution and reduce the time of distribution. In order to obtain the optimal ordering and allocation scheme, firstly, a distributed inventory system consisting of an ordering centre, a material coordination centre and n material warehouses are considered, and the cost model of ordering and allocation of the distributed material warehouse is established. Next, the safety stock and the ordering point of the distributed material warehouse are solved. Then the improved genetic algorithm-simulated annealing algorithm (IGA-SA) is used to solve the optimization of the distributed material warehouse. Finally, the application example is given. The results show that the IGA-SA algorithm can effectively reduce inventory cost and improve inventory utilization.

Research on Optimal Deployment of HWSN Node Cost Based on Simulated Annealing Algorithm

2014 ◽  
Vol 651-653 ◽  
pp. 1921-1924
Author(s):  
Ji Tao Shen ◽  
Jun Yang Zhang
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Fault Tolerant ◽  
Monitoring Network ◽  
Optimization Method ◽  
Perception Model ◽  
Annealing Algorithm ◽  
Target Monitoring ◽  
The Cost ◽  
Network Fault Tolerance

An optimal heterogeneous sensor differentiated deployment schemes based on simulated annealing algorithm is proposed to solve the problems of the high density of distributing heterogeneity nodes in WSN and geographical irregularity of the sensed event. This method can not only apply to Boolean perception model of the node, but also apply to perception model. The algorithm uses the cost of sensors deployment as objective function in the context of assuring the coverage and fault tolerant of networks. The simulation results show that, the optimization method proposed in this paper can effectively convergence, under the premise to ensure network fault tolerance and robustness, reduces the cost of network deployment, improve the quality of target monitoring network.

scholarly journals An Effective Heuristic-Based Approach for Partitioning

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xibin Zhao ◽  
Hehua Zhang ◽  
Yu Jiang ◽  
Songzheng Song ◽  
Xun Jiao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Embedded System ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Local Optimal Solution ◽  
Annealing Algorithm ◽  
The Cost ◽  
Combined Algorithm ◽  
Search Capability

As being one of the most crucial steps in the design of embedded systems, hardware/software partitioning has received more concern than ever. The performance of a system design will strongly depend on the efficiency of the partitioning. In this paper, we construct a communication graph for embedded system and describe the delay-related constraints and the cost-related objective based on the graph structure. Then, we propose a heuristic based on genetic algorithm and simulated annealing to solve the problem near optimally. We note that the genetic algorithm has a strong global search capability, while the simulated annealing algorithm will fail in a local optimal solution easily. Hence, we can incorporate simulated annealing algorithm in genetic algorithm. The combined algorithm will provide more accurate near-optimal solution with faster speed. Experiment results show that the proposed algorithm produce more accurate partitions than the original genetic algorithm.

An evaluation of the simulated annealing algorithm for solving the area-restricted harvest-scheduling model against optimal benchmarks

2005 ◽  
Vol 35 (10) ◽  
pp. 2500-2509 ◽  
Author(s):  
Kevin A Crowe ◽  
J D Nelson
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Computing Time ◽  
Multiple Time ◽  
Harvest Scheduling ◽  
Problem Size ◽  
Solution Quality ◽  
Annealing Algorithm ◽  
Objective Function Values ◽  
Problem Instances

A common approach for incorporating opening constraints into harvest scheduling is through the area-restricted model. This model is used to select which stands to include in each opening while simultaneously determining an optimal harvest schedule over multiple time periods. In this paper we use optimal benchmarks from a range of harvest scheduling problem instances to test a metaheuristic algorithm, simulated annealing, that is commonly used to solve these problems. Performance of the simulated annealing algorithm was assessed over a range of problem attributes such as the number of forest polygons, age-class distribution, and opening size. In total, 29 problem instances were used, ranging in size from 1269 to 36 270 binary decision variables. Overall, the mean objective function values found with simulated annealing ranged from approximately 87% to 99% of the optima after 30 min of computing time, and a moderate downward trend of the relationship between problem size and solution quality was observed.

scholarly journals Prediksi Harga Rumah Menggunakan General Regression Neural Network

2021 ◽  
Vol 8 (1) ◽  
pp. 59-66
Author(s):  
Evi Febrion Rahayuningtyas ◽  
Feny Novia Rahayu ◽  
Yufis Azhar
Keyword(s):  
Neural Network ◽  
General Regression Neural Network ◽  
Output Layer ◽  
Input Layer ◽  
Non Linear ◽  
Pattern Layer ◽  
General Regression

Rumah merupakan salah satu kebutuhan manusia selain sandang dan pangan. Hirarki Kebutuhan Maslow menyebutkan bahwa rumah adalah salah satu Physiological Needs. Dewasa ini, kebutuhan akan rumah sering dijadikan sebagai sumber investasi bagi pengusaha properti yang tentunya menghasilkan investasi yang tidak dapat diremehkan. Keuntungan dalam jual beli rumah dijadikan alasan bagi seseorang dalam melakukan transaksi jual beli rumah itu sendiri. Sehingga munculah harga yang tidak terprediksi pada harga rumah yang ada. Penelitian ini menggunakan General Regression Neural Network sebagai pemodelannya. GRNN menggunakan dasar regresi non linear dimana strukturnya terdiri dari 4 layer, yaitu Input Layer, Pattern Layer, Summation Layer dan Output Layer.  Variabel dependen yang digunakan berjumlah 6. Terdiri dari tanggal transaksi, jarak rumah dari stasiun MRT, jumlah toko atau minimarket yang dekat dengan rumah, umur rumah, dan letak geografis berupa longitude dan latitude. Variabel independen berupa harga rumah dalam satuan luas Ping yang selanjutnya dikonversi menjadi 10.000 New Dollar Taiwan/Ping.  Hasil yang didapatkan berupa data actual dan data predicted yang divisualisasikan dengan line plot. Dilakukan juga uji akurasi dan uji kinerja pemodelan menggunakan 3 jenis evaluasi dengan hasil dari ketiga jenis evaluasi tersebut adalah 58,72 untuk skor MSE, skor 7,66 untuk RMSE dan 5,99 untuk skor MAE.

scholarly journals A Simulated Annealing Algorithm for Solving Two-Echelon Vehicle Routing Problem with Locker Facilities

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 218
Author(s):  
A. A. N. Perwira Redi ◽  
Parida Jewpanya ◽  
Adji Candra Kurniawan ◽  
Satria Fadil Persada ◽  
Reny Nadlifatin ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Simulated Annealing Algorithm ◽  
Two Phase ◽  
Routing Problem ◽  
Intermediate Facilities ◽  
Annealing Algorithm ◽  
Phase Optimization ◽  
The Cost

We consider the problem of utilizing the parcel locker network for the logistics solution in the metropolitan area. Two-echelon distribution systems are attractive from an economic standpoint, whereas the product from the depot can be distributed from or to intermediate facilities. In this case, the intermediate facilities are considered as locker facilities present in an accessible location in the vicinity of the final customers. In addition, the utilization of locker facilities can reduce the cost caused by the unattended deliveries. The problem is addressed as an optimization model that formulated into an integer linear programming model denoted as the two-echelon vehicle routing problem with locker facilities (2EVRP-LF). The objective is to minimize the cost of transportation with regards to the vehicle travelling cost, the intermediate facilities renting cost, and the additional cost to compensate the customer that needs to travel to access the intermediate facilities. Because of its complexity, a simulated annealing algorithm is proposed to solve the problem. On the other hand, the modelling approach can be conducted by generating two-phase optimization model approaches, which are the p-median problem and the capacitated vehicle routing problem. The results from both methods are compared in numerical experiments. The results show the effectiveness of 2EVRP-LF compared to the two-phase optimization. Furthermore, the simulated annealing algorithm showed an effective performance in solving 2EVRP-LF.

An Improved Simulated Annealing Algorithm Based on Genetic Algorithm

2012 ◽  
Vol 490-495 ◽  
pp. 267-271 ◽  
Author(s):  
Shu Fei Li
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Reservoir Operation ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Optimization Method ◽  
Operation Speed ◽  
Ga Algorithm ◽  
Hybrid Simulated Annealing ◽  
Annealing Algorithm

An effective hybrid Simulated Annealing Algorithm based on Genetic Algorithm is proposed to apply to reservoir operation. Compared with other optimal methods, it is proved that SA-GA algorithm is a quite effective optimization method to solve reservoir operation problem. The simulated annealing algorithm is introduced to Genetic Algorithm, which is feasibility and validity. As a result of stronger ability of global search and better convergence property of SA-GA, and compared with other algorithms, the approximate global optimal solution would be obtained in little time. The operation speed is more quickness and the results are more stabilization by SA-GA, than Genetic Algorithm and the traditional Dynamic Programming and POA.

scholarly journals A security technology of power relay using edge computing

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0253428
Author(s):  
Zhongqing Sang ◽  
Kai Yang ◽  
Rencheng Zhang
Keyword(s):  
Simulated Annealing Algorithm ◽  
Computing Time ◽  
Relay Protection ◽  
Active Role ◽  
Edge Computing ◽  
Optimal Time ◽  
Security Technology ◽  
Proposed Model ◽  
Annealing Algorithm ◽  
Fault State

The purposes are to find the techniques suitable for the safety relay protection of intelligent substations and discuss the applicability of edge computing in relay protection. Regarding relay protection in intelligent substations, edge computing and optimized simulated annealing algorithm (OSAA) are combined innovatively to form an edge computing strategy. On this basis, an edge computing model is proposed based on relay fault traveling waves. Under different computing shunt tasks, OSAA can converge after about 1,100 iterations, and its computing time is relatively short. As the global optimal time delay reaches 0.5295, the corresponding computing time is 456.27s, apparently better than the linear search method. The proposed model can reduce the computing time significantly, playing an active role in the safe shunting of power relays. The simulation also finds that the voltage and current waveforms corresponding to the fault state of Phase A are consistent with the actual situations. To sum up, this model provides a reference for improving and optimizing intelligent substation relay protection.

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