快速模拟退火组合优化法在区域水环境质量评价中的应用

以地表水环境质量准标为基础,设定水体污染物的基准值,构建用于水体的一种环境质量评价的模型,利用快速模拟退火组合优化法对相关参数进行优化,而得到一个对多种水污染物都适用的综合水体环境质量评价指数公式。该公式形式简单,计算容易,评价结果具有可比性。

模拟退火法及其在抗差估计中的应用

抗差估计结果的好坏与初值的选取密切相关。当迭代初值偏离较大时,ρ函数不是严凸时,采取的选权迭代法往往不能获得全局最优值,而只能得到局部的最优解。本文提出一种新的抗差估计求解过程,即把它看作是关于未知参数的全局最优化问题,应用模拟退火法进行求解。计算结果表明:相对于传统的选权迭代法,模拟退火法对初值的选取不敏感,能给出全局最优值。

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.

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.

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.

DESIGN OF MAXIMALLY FLAT FIR FILTERS BASED ON EXPLICIT FORMULAS COMBINED WITH OPTIMIZATION

A maximally flat FIR filter design method based on explicit formulas combined with simulated annealing and random search was presented. Utilizing the explicit formulas to calculate the ini- tial values, the firate-word-length FIR filter design problem was converted into optimization of the filter coefficients, An optimization method combined with local discrete random search and simulated annealing was proposed, with the result of optimum solution in the sense of Chebyshev approximation. The proposed method can simplify the design process of FIR filter and reduce the calculation burden. The simulation result indicates that the proposed method is superior to the traditional round off method and can reduce the value of the objective function to 41%～74%.

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.

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.

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.

新型低损耗塑料光纤结构优化的数值分析

分析了具有反共振反射的蜘蛛网包层塑料空芯光纤在1.3μm波段的损耗特性及其与结构参数的关系。将光纤包层等效为周期性多层膜,用渐近转移矩阵法进行数值模拟。为优化光纤结构参数,分析了包层层数N,高折射率层厚度d2和高、低折射率比值n2∶n1对损耗的影响。计算结果表明,随着N的增加,损耗首先快速下降最后达到一个固定值;模式损耗随d2的增加呈线形增大趋势,n2∶n1越大,反共振波长处的损耗越低。在此基础上,用模拟退火优化算法对空气层厚d1和纤芯半径rco进行了最优化求解。最后,用以上求得结构参数的优化组合即N=3,d2=2.648μm,n2∶n1=1.49∶1.0,d1=3μm和rco=100μm,计算得到最低损耗是0.449dB/km。

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.