利用改进SA算法估计河流水质参数的仿真实验

将改进模拟退火法应用于求解分析河流水团示踪试验数据,确定河流水质参数的函数优化问题。针对标准SA算法收敛速度缓慢的弱点,采取了增加附加约束条件、设置内阈值提前降温和增加记忆功能等措施对算法进行了改进。仿真实验结果表明:1) 改进措施能够明显地提高算法收敛速度,并可得到满意的参数计算结果;2) 内循环次数不会对外循环次数产生明显的影响;3) 内阈值的设置对算法的收敛速度影响非常明显,当其值与外阈值接近或相等时,算法的收敛性最佳;4) 在研究的具体问题情况下,降温指数不宜过大,其在0.4—0.65之间取值为宜。

FPGA PLACEMENT OPTIMIZATION BY TWO-STEP UNIFIED GENETIC ALGORITHM AND SIMULATED ANNEALING ALGORITHM

Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool.

A New Chaotic Parameters Disturbance Annealing Neural Network for Solving Global Optimization Problems

Since there were few chaotic neural networks applicable to the global optimization, in this paper, we propose a new neural network model ? chaotic parameters disturbance annealing (CPDA) network, which is superior to other existing neural networks, genetic algorithms, and simulated annealing algorithms in global optimization. In the present CPDA network, we add some chaotic parameters in the energy function, which make the Hopfield neural network escape from the attraction of a local minimal solution and with the parameter annealing, our model will converge to the global optimal solutions quickly and steadily. The converge ability and other characters are also analyzed in this paper. The benchmark examples show the present CPDA neural network's merits in nonlinear global optimization.

Synthesis of Heat Integrated Complex Distillation Systems via Stochastic Optimization Approaches

This paper addresses the application of stochastic optimization approaches to the synthesis of heatintegrated complex distillation system, which is characterized by large-scale combinatorial feature. Conventionaland complex columns, thermally coupled (linked) side strippers and side rectifiers as well as heat integration betweenthe different columns are simultaneously considered. The problem is formulated as an MINLP (mixed-integernonlinear programming) problem. A simulated annealing algorithm is proposed to deal with the MINLP problemand a shortcut method is applied to evaluate all required design parameters as well as the total cost function. Twoillustrating examples are presented.

A stability study of goaf based on mechanical properties degradation of rock caused by rheological and disturbing loads

Based on the classical static theory and static numerical simulation,the static method could not accurately reflect the stability of goaf where the rocks on the pillar and roof are influenced by Theological and blasting disturbance for a long time.According to the test from the site,an experimental study was made in Theological and dynamic disturbance.After that,on the basis of variable rock mechanics parameters from the experimental data,numerical simulation was used to analyze the vertical stress distribution of goaf,vertical displacement and plastic area of roof in the "deterioration" caused by Theological and blasting,which shows that the mechanics properties of the rock were greatly influenced by Theological,and dynamic disturbance.The results of the experimental study and numerical simulation show that the mechanics properties of rock are greatly influenced by Theological and dynamic disturbance.As a result,the stability of goaf is greatly reduced.Finally,by comparing golf monitoring results with the analysis of theoretical calculation,it was found that the results were approximately the same,which testifies the reliability of the method.This method provides a new way of studying the stability of goaf as well as laying a basic foundation for future safety management.

Advanced Coverage Optimization Techniques for Small Cell Clusters

Coverage challenge for small considered to be a optlmlzation is a main cell clusters which are promising solution to provide seamless cellular coverage for large indoor or outdoor areas. This paper focuses on small cell cluster coverage problems and proposes both centralized and distributed self-optimization methods. Modified Particle swarm optimization （MPSO） is introduced to centralized optimization which employs particle swarm optimization （PSO） and introduces a heuristic power control scheme to accelerate the algorithm to search tbr the global optimum solution. Distributed coverage optimization is modeled as a non-cooperative game, with a utility function considering both throughput and interference. An iterative power control algorithm is then proposed using game theory （DGT） which converges to Nash Equilibrium （NE）. Simulation results show that both MPSO and DGT have excellent performance in coverage optimization and outperform optimization using simulated annealing algorithm （SA）, reaching higher coverage ratio and throughput while with less iterations.

Simulated Annealing Algorithm and Its Application in Irregular Polygons Packing

Two dimensional irregular polygons packing problem is very difficult to be solved in traditional optimal way.Simulated annealing(SA)algorithm is a stochastic optimization technique that can be used to solve packing problems.The whole process of SA is introduced firstly in this paper. An extended neighborhood searching method in SA is mainly analyzed. A general module of SA algorithm is given and used to lay out the irregular polygons. The judgment of intersection and other constrains of irregular polygons are analyzed. Then an example that was used in the paper of Stefan Jakobs is listed.Results show that this SA algorithm shortens the computation time and improves the solution.

Reconstruction of 3-D digital cores using a hybrid method

A 3-D digital core describes the pore space microstructure of rocks. An X-ray micro CT scan is the most accurate and direct but costly method to obtain a 3-D digital core. In this study, we propose a hybrid method which combines sedimentation simulation and simulated annealing （SA） method to generate 3-D digital cores based on 2-D images of rocks. The method starts with the sedimentation simulation to build a 3-D digital core, which is the initial configuration for the SA method. We update the initial digital core using the SA method to match the auto-correlation function of the 2-D rock image and eventually build the final 3-D digital core. Compared with the typical SA method, the hybrid method has significantly reduced the computation time. Local porosity theory is applied to quantitatively compare the reconstructed 3-D digital cores with the X-ray micro CT 3-D images. The results indicate that the 3-D digital cores reconstructed by the hybrid method have homogeneity and geometric connectivity similar to those of the X-ray micro CT image. The formation factors and permeabilities of the reconstructed 3-D digital cores are estimated using the finite element method （FEM） and lattice Boltzmann method （LBM）, respectively. The simulated results are in good agreement with the experimental measurements. Comparison of the simulation results suggests that the digital cores reconstructed by the hybrid method more closely reflect the true transport properties than the typical SA method alone.

Parametric optimization and performance comparison of organic Rankine cycle with simulated annealing algorithm

Taking the ratio of heat transfer area to net power and heat recovery efficiency into account, a multi-objective mathematical model was developed for organic Rankine cycle （ORC）. Working fluids considered were R123, R134a, R141b, R227ea and R245fa. Under the given conditions, the parameters including evaporating and condensing pressures, working fluid and cooling water velocities were optimized by simulated annealing algorithm. The results show that the optimal evaporating pressure increases with the heat source temperature increasing. Compared with other working fluids, R123 is the best choice for the temperature range of 100--180℃ and R141 b shows better performance when the temperature is higher than 180 ℃. Economic characteristic of system decreases rapidly with the decrease of heat source temperature. ORC system is uneconomical for the heat source temperature lower than 100℃.

Novel Adaptive Simulated Annealing Algorithm for Constrained Multi-Objective Optimization

In recent years, sinmlated annealing algo-rithms have been extensively developed and uti-lized to solve nmlti-objective optimization problems. In order to obtain better optimization perfonmnce, this paper proposes a Novel Adaptive Simulated Annealing （NASA） algorithm for constrained multi-objective optimization based on Archived Multi-objective Simulated Annealing （AMOSA）. For han-dling multi-objective, NASA makes improverrents in three aspects： sub-iteration search, sub-archive and adaptive search, which effectively strengthen the stability and efficiency of the algorithnm For handling constraints, NASA introduces corresponding solution acceptance criterion. Furtherrrore, NASA has also been applied to optimize TD-LTE network perform-ance by adjusting antenna paranleters; it can achieve better extension and convergence than AMOSA, NS-GAII and MOPSO. Analytical studies and simulations indicate that the proposed NASA algorithm can play an important role in improving multi-objective optimi-zation performance.

A SAA-based Novel Hybrid Intelligent Evolutionary Algorithm for Job Shop Scheduling Problem

Through systematic analysis and comparison of the common features of SAA, ES and traditional LS (local search) algorithm, a new hybrid strategy of mixing SA, ES with LS, namely HIEA (Hybrid Intelligent Evolutionary Algorithm), is proposed in this paper. Viewed as a whole, the hybrid strategy is also an intelligent heuristic searching procedure. But it has some characteristics such as generality, robustness, etc., because it synthesizes advantages of SA, ES and LS, while the shortages of the three methods are overcome. This paper applies Markov chain theory to describe the hybrid strategy mathematically, and proves that the algorithm possesses the global asymptotical convergence and analyzes the performance of HIEA.

Numerical simulation and combination optimization of aluminum holding furnace linings based on simulated annealing

To reduce heat loss and save cost, a combination decision model of reverb aluminum holding furnace linings in aluminum casting industry was established based on economic thickness method, and was resolved using simulated annealing. Meanwhile, a three-dimensional mathematical model of aluminum holding furnace linings was developed and integrated with user-defined heat load distribution regime model. The optimal combination was as follows: side wall with 80 mm alumino-silicate fiber felts, 232 mm diatomite brick and 116 mm chamotte brick; top wall with 50 mm clay castables, 110 mm alumino-silicate fiber felts and 200 mm refractory concrete;and bottom wall with 232 mm high-alumina brick, 60 mm clay castables and 68 mm diatomite brick. Lining temperature from high to low was successively bottom wall, side wall, and top wall. Lining temperature gradient in increasing order of magnitude was refractory layer and insulation layer. It was indicated that the results of combination optimization of aluminum holding furnace linings were valid and feasible, and its thermo-physical mechanism and cost characteristics were reasonably revealed.

Obtain the optimal of logistics delivery network with annealing algorithm

Simulated annealing(SA) algorithm is a heuristic algorithm,proposed one approximation algorithm of solving optimization combinatorial problems inspired by objects in the annealing process of heating crunch. The algorithm is superior to the traditional greedy algorithm,which avoids falling into local optimum and reaches global optimum. There are often some problems to find the shortest path,etc in the logistics and distribution network, and we need optimization for logistics and distribution path in order to achieve the shortest,best,most economical,and so on. The paper uses an example of SA algorithm validation to verify it,and the method is proved to be feasible.

Enterprise-level business component identification in business architecture integration

The component-based business architecture integration of military information systems is a popu- lar research topic in the field of military operational research. Identifying enterprise-level business components is an important issue in business architecture integration. Currently used methodologies for business component identification tend to focus on software-level business components, and ignore such enterprise concerns in business architectures as organizations and resources. Moreover, approaches to enterprise-level business component identi- fication have proven laborious. In this study, we propose a novel approach to enterprise-level business component identification by considering overall cohesion, coupling, granularity, maintainability, and reusability. We first define and formulate enterprise-level business components based on the component business model and the Department of Defense Architecture Framework （DoDAF） models. To quantify the indices of business components, we formulate a create, read, update, and delete （CRUD） matrix and use six metrics as criteria. We then formulate business com- ponent identification as a multi：objective optimization problem and solve it by a novel meta-heuristic optimization algorithm called the ＇simulated annealing hybrid genetic algorithm （SHGA）＇. Case studies showed that our approach is more practical and efficient for enterprise-level business component identification than prevalent approaches.