优化组合法进行发电机组负荷最优分配的仿真研究

提出了一种用于发电机组负荷最优分配的优化组合法,该方法把优先顺序法和动态规划法结合使用,既克服了优先顺序法不能计及启停耗量的不足的缺陷,又解决了动态规划法的“维数灾”及机组功率响应速度约束问题。在某发电公司四台机组的负荷经济调度决策系统中进行了动态仿真研究与实现。

生长曲线参数估计的一种新方法—优化回归组合法

在现有文献研究的基础上，对生长曲线参数估计问题又作了进一步研究，给出了生长曲线参数估计的一种新方法优化回归组合法，该方法创造性地将最优化方法与回归方法结合在一起，利用最优化理论中的区间搜索和一维搜索，可以得到一系列c^＊值，利用回归方法可求得与其相对应的一系列a和b的值．当c取最优值c时，a和b便得到最优值a^＊和b^＊经示例计算表明，这种参数估计法具有较高的精度，

非对称养分效应模型参数估计的一种优化回归组合法

在现有文献研究的基础上,对非对称养分效应模型参数估计问题又作了进一步探讨,指出了现有文献给出的参数估计方法存在的局限性和不足。在此基础上,研究提出了一种新的参数估计方法——优化回归组合法,该方法不仅克服了现有文献给出的参数估计方法之不足,而且经示例计算表明,该方法具有较高的拟合精度。

谈我馆以优化组合为中心的管理改革

为了深化教育改革,增强图书馆的活力,调动工作人员积极性,提高工作效益,1989年我校在各系及图书馆实行工资总额包干制度,结合我馆实际情况,进行了以优化组合为中心的管理改革,在此谈谈我们的作法和体会。 一、为什么要进行优化组合 河北师大图书馆是有80多年历史的老馆。十一届三中全会以来发展比较快,现拥有1万平方米的独立馆舍,藏书110多万册。

基于粒子群算法的OFDM峰平比抑制问题研究

提出了一种基于粒子群的相位优化组合算法来抑制OFDM信号的高峰平比,结合相位优化组合算法,通过寻找使峰平比最小的相位来抑制信号的高峰值信号的原理,利用粒子群算法无需过多原始信息、收敛迅速快、寻优准确等优点解决了寻找抑制OFDM信号峰平比的最优相位向量问题.实验仿真结果表明,提出的改进算法在不过多增加系统复杂度的同时,更为有效地抑制了OFDM信号的高峰平比现象.

提高微机实验室装机效率的方法研究

根据本实验室现有的设备条件 ,借鉴传统方法 ,在局域网环境中提出并使用优化组合法 。

低温低电耗低浓度多稀土镀铬添加剂

低温低电耗低浓度多稀土镀铬添加剂该添加剂系针对普通高铬镀铬工艺以及无稀土型、单一稀土型、混合稀土型镀铬添加剂所存在的问题，采用“多稀土纯品优化组合法”开发的一种宽温、宽浓度（尤其适用低温、低浓度）、低电耗镀铬的新一代镀铬添加剂。该添加剂采用吸氢剂、导...

Hopfield neural network based on ant system

Hopfield neural network is a single layer feedforward neural network. Hopfield network requires some control parameters to be carefully selected, else the network is apt to converge to local minimum. An ant system is a nature inspired meta heuristic algorithm. It has been applied to several combinatorial optimization problems such as Traveling Salesman Problem, Scheduling Problems, etc. This paper will show an ant system may be used in tuning the network control parameters by a group of cooperated ants. The major advantage of this network is to adjust the network parameters automatically, avoiding a blind search for the set of control parameters. This network was tested on two TSP problems, 5 cities and 10 cities. The results have shown an obvious improvement.

Damage evolution analysis of cast steel GS-20Mn5V based on modified GTN model

A modified Gurson-Tvergaard-Needleman （GTN） model that accounts for the mixed （isotropic and kinematic） hardening of cast steel GS-20Mn5V was developed and implemented in the finite dement program ABAQUS/Standard via a user-defined material subroutine UMAT. This model couples the stress state and damage evolution （pore volume fraction increase） by a classic method that assumes that the total void volume fraction is divided into a nucleation and a growth part. A parametric study was conducted to assess the effect of modified GTN model parameters on mechanical properties such as the nucleation, growth and coalescence of voids and to obtain the optimal parameter combination by the orthogonal test method. The predicted load-displacement curves of notched specimens with the optimal parameters are favorably compared to the experimental curves. Therefore, the modified GTN model can be used to predict the damage evaluation and fracture behavior of GS-20Mn5V.

A novel hybrid algorithm based on a harmony search and artificial bee colony for solving a portfolio optimization problem using a mean-semi variance approach

Portfolio selection is one of the major capital allocation and budgeting issues in financial management, and a variety of models have been presented for optimal selection. Semi-variance is usually considered as a risk factor in drawing up an efficient frontier and the optimal portfolio. Since semi-variance offers a better estimation of the actual risk portfolio, it was used as a measure to approximate the risk of investment in this work. The optimal portfolio selection is one of the non-deterministic polynomial(NP)-hard problems that have not been presented in an exact algorithm, which can solve this problem in a polynomial time. Meta-heuristic algorithms are usually used to solve such problems. A novel hybrid harmony search and artificial bee colony algorithm and its application were introduced in order to draw efficient frontier portfolios. Computational results show that this algorithm is more successful than the harmony search method and genetic algorithm. In addition, it is more accurate in finding optimal solutions at all levels of risk and return.

Joint Resource Allocation Using Evolutionary Algorithms in Heterogeneous Mobile Cloud Computing Networks

The problem of joint radio and cloud resources allocation is studied for heterogeneous mobile cloud computing networks. The objective of the proposed joint resource allocation schemes is to maximize the total utility of users as well as satisfy the required quality of service(QoS) such as the end-to-end response latency experienced by each user. We formulate the problem of joint resource allocation as a combinatorial optimization problem. Three evolutionary approaches are considered to solve the problem: genetic algorithm(GA), ant colony optimization with genetic algorithm(ACO-GA), and quantum genetic algorithm(QGA). To decrease the time complexity, we propose a mapping process between the resource allocation matrix and the chromosome of GA, ACO-GA, and QGA, search the available radio and cloud resource pairs based on the resource availability matrixes for ACOGA, and encode the difference value between the allocated resources and the minimum resource requirement for QGA. Extensive simulation results show that our proposed methods greatly outperform the existing algorithms in terms of running time, the accuracy of final results, the total utility, resource utilization and the end-to-end response latency guaranteeing.

Solving material distribution routing problem in mixed manufacturing systems with a hybrid multi-objective evolutionary algorithm

The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II.

Prediction and Optimization Performance Models for Poor Information Sample Prediction Problems

The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on experimental data analysis.Through a large number of prediction and optimization experiments,the accuracy and stability of the prediction method and the correction ability of the optimization method are studied.First,five traditional single-item prediction methods are used to process small samples with under-sufficient information,and the standard deviation method is used to assign weights on the five methods for combined forecasting.The accuracy of the prediction results is ranked.The mean and variance of the rankings reflect the accuracy and stability of the prediction method.Second,the error elimination prediction optimization method is proposed.To make,the prediction results are corrected by error elimination optimization method(EEOM),Markov optimization and two-layer optimization separately to obtain more accurate prediction results.The degree improvement and decline are used to reflect the correction ability of the optimization method.The results show that the accuracy and stability of combined prediction are the best in the prediction methods,and the correction ability of error elimination optimization is the best in the optimization methods.The combination of the two methods can well solve the problem of prediction with small samples and under-sufficient information.Finally,the accuracy of the combination of the combined prediction and the error elimination optimization is verified by predicting the number of unsafe events in civil aviation in a certain year.

An Optimization Approach for Unit Commitment of a Power System Integrated with Renewable Energy Sources： A Case Study of Afghanistan

This paper focused on generation scheduling problem with consideration of wind, solar and PHES （pumped hydro energy storage） system. Wind, solar and PHES are being considered in the NEPS （northeast power system） of Afghanistan to schedule all units power output so as to minimize the total operation cost of thermal units plus aggregate imported power tariffs during the scheduling horizon, subject to the system and unit operation constraints. Apart from determining the optimal output power of each unit, this research also involves in deciding the on/off status of thermal units. In order to find the optimal values of the variables, GA （genetic algorithm） is proposed. The algorithm performs efficiently in various sized thermal power system with equivalent wind, solar and PHES and can produce a high-quality solution. Simulation results reveal that with wind, solar and PHES the system is the most-cost effective than the other combinations.

Genetic approach for Cell-by-Cell dynamic spectrum allocation in the heterogeneous scenario

In this paper,a genetic algorithm (GA) is investigated to deal with cell-by-cell dynamic spectrumallocation (DSA) in the heterogeneous scenario with temporal and spatial traffic demand changes,whichis also known as a difficult combinatorial optimization problem.A new two-dimensional chromosome encodingscheme is defined according to characteristics of the heterogeneous scenario,which prevents forminginvalid solutions during the genetic operation and enables much faster convergence.A novel randomcoloring gene generation function is presented which is the basic operation for initialization and mutationin the genetic algorithm.Simulative comparison demonstrates that the proposed GA-based cell-by-cellDSA outperforms the conventional contiguous DSA scheme both in terms of spectral efficiency gain andquality of service (QoS) satisfaction.

Research on Optimization wharf Structure Type of Triangle frame pier

Based on optimization wharf structure type of triangle flame pier proposed, namely＂ Spatial triangular flame pier ＋ Big span bent which taking Slant supports＂ Simulating three-dimensional pier model of a variety of conditions in the actual loading by structural finite element software. Under the guidance of the three-dimensional structure of the most unfavorable load combination algorithm, three-dimensional combination algorithm is applied to the new structure by the MATLAB software programming. Search and calculate the most unfavorable combination of action effects and the corresponding intemal force of the main member, checking the feasibility of the three-dimensional algorithms, Calculating the new wharf structure structural features and stability, Providing numerical reference for the design of this sort of wharf.

Approach for earth observation satellite real-time and playback data transmission scheduling

The scheduling of earth observation satellites(EOSs)data transmission is a complex combinatorial optimization problem. Current researches mainly deal with this problem on the assumption that the data transmission mode is fixed, either playback or real-time transmission. Considering the characteristic of the problem, a multi-satellite real-time and playback data transmission scheduling model is established and a novel algorithm based on quantum discrete particle swarm optimization(QDPSO)is proposed. Furthermore, we design the longest compatible transmission chain mutation operator to enhance the performance of the algorithm. Finally, some experiments are implemented to validate correctness and practicability of the proposed algorithm.

Algorithms for seismic topology optimization of water distribution network

As an essential lifeline engineering system,water distribution network should provide enough water to maintain people's life after earthquake in addition to working under daily operation.However,the design of water distribution network usually ignores the influence of earthquake,resulting in water stoppage in large area during many recent strong earthquakes.This study introduced a seismic design approach of water distribution network,i.e.,topology optimization design.With network topology as the optimization goal and seismic reliability as the constraint,a topology optimization model for designing water distribution network under earthquake is established.Meanwhile,two element investment importance indexes,a pipeline investment importance index and a diameter investment importance index,are introduced to evaluate the importance of pipelines in water distribution network.Then,four combinational optimization algorithms,a genetic algorithm,a simulated annealing genetic algorithm,an ant colony algorithm and a particle swarm algorithm,are introduced to solve this optimization model.Moreover,these optimization algorithms are used to optimize a network with 19 nodes and 27 pipelines.The optimization results of these algorithms are compared with each other.

SEMI-DEFINITE RELAXATION ALGORITHM FOR SINGLE MACHINE SCHEDULING WITH CONTROLLABLE PROCESSING TIMES

The authors present a semi-definite relaxation algorithm for the scheduling problem with controllable times on a single machine. Their approach shows how to relate this problem with the maximum vertex-cover problem with kernel constraints (MKVC).The established relationship enables to transfer the approximate solutions of MKVCinto the approximate solutions for the scheduling problem. Then, they show how to obtain an integer approximate solution for MKVC based on the semi-definite relaxation and randomized rounding technique.