基于遗传模拟退火算法的空袭兵力分配及优化

对遗传模拟退火算法中的交叉、变异操作进行了改进，并实施了最优保留策略，形成了改进遗传模拟退火算法．以突击效果最大化和兵力损失最小化为目标函数，以空袭兵力总量的限制、空袭兵器挂载类型的限制等为约束条件，建立了空袭兵力分配及优化模型．在考虑兵力分配模型特点的基础上，利用改进遗传模拟退火算法求解．通过与多目标数学规划和标准遗传算法优化进行的比较表明，该方法能够有效地解决带约束的多目标优化问题．     

基于退火遗传算法的四阶带通箱设计

四阶带通式扬声器系统对扬声器单元参数和箱体参数的配合要求较高，设计复杂。文章中将模拟退火算法和遗传算法相结合，提出了运用具有全局搜索能力的模拟退火遗传算法(SAGA)来设计四阶带通扬声器系统的方法，该算法避免了单一遗传算法存在的早熟收敛和搜索效率较低等问题，提高了算法的收敛速度。文章中考虑了声导管泄漏损耗的影响，并提出了泄漏损耗的QL值的实际测定方法。测量结果表明理论曲线与实测曲线相吻合，说明模拟退火遗传算法能达到设计要求。     

Simulated annealing based parallel genetic algorithm for facility layout problem

The facility layout problem (FLP), a typical combinational optimisation problem, is addressed in this paper by implementing parallel simulated annealing (SA) and genetic algorithms (GAs) based on a coarse-grained model to derive solutions for solving the static FLP with rectangle shape areas. Based on the consideration of minimising the material flow factor cost (MFFC), shape ratio factor (SRF) and area utilisation factor (AUF), a total layout cost (TLC) function is derived by conducting a weighted summation of MFFC, SRF and AUF. The evolution operations (including crossover, mutation, and selection) of GA provide a population-based global search in the space of possible solutions, and the SA algorithm can lead to an efficient local search near the optimal solution. By combing the characteristics of GA and SA, better solutions will be obtained. Moreover, the parallel implementation of simulated annealing based genetic algorithm (SAGA) enables a quick search for the optimal solution. The proposed method is tested by performing a case study simulation and the results confirm its feasibility and superiority to other approaches for solving FLP.     

Solving structural optimization problems with genetic algorithms and simulated annealing

In this paper we study the performance of two stochastic search methods: Genetic Algorithms and Simulated Annealing, applied to the optimization of pin‐jointed steel bar structures. We show that it is possible to embed these two schemes into a single parametric family of algorithms, and that optimal performance (in a parallel machine) is obtained by a hybrid scheme. Examples of applications to the optimization of several real steel bar structures are presented. Copyright © 1999 John Wiley & Sons, Ltd.     

用模拟退火算法解旅行商问题

对解旅行商问题的模拟退火算法作了改进,增加了产生新解的函数,修改了原算法计算旅行回路总长度的代价函数,并用混沌随机序列替代不适宜的随机函数.从而用TurboC实现了改进算法.实验表明,改进算法对于解旅行商问题是实用的.     

A simulated annealing algorithm based on a closed loop layout for facility layout design in flexible manufacturing systems

Facility layout design problems in flexible manufacturing systems (FMS) differ from traditional facility design problems and are more difficult to solve because there are more constraints that must be considered (i.e., cell shape, cell orientation, pick-up and drop-off point positions). The focus of this paper is on the closed loop type layout, which is based on a predetermined layout pattern. This layout pattern is commonly found in manufacturing settings since it requires a simplified material handling system configuration and since it facilitates a modular and expandable layout structure. The open-field type layout problem, where there is no predetermined layout pattern, may potentially have a more efficient configuration, since there are fewer restrictions. However, this problem is more difficult to solve and may result in configurations that are not desirable due to the lack of structure or modularity. The procedure developed in this paper improves the efficiency of the closed loop configuration by changing the rectangular shape of the loop to different sizes. In many cases, the resulting closed loop layout proves to be as efficient as the open field layout. A simulated annealing procedure (SA-CL) is used to search for the configuration that minimizes the total material handling costs. A comparison of the results with existing methods indicates that, based on solution quality and computational time, the SA-CL offers a favourable alternative for efficient layout design.     

基于SA算法的行星齿轮减速器离散变量优化设计

考虑制造工艺要求,将所有设计变量均视为离散变量,包括一般离散变量和伪离散变量,并就这两种情况下状态产生函数的设计原理进行深入研究,解决了将模拟退火算法用于离散变量函数优化的关键技术问题,介绍了一种基于模拟退火算法的离散变量函数优化的新方法。行星齿轮传动中各齿轮的齿数受传动比条件、同轴条件和装配条件的限制而不能任意取值,齿轮的模数也要受国家标准的制约只能取一些离散值,用以数学规划理论为基础的经典约束优化方法求解效果很差,用基于模拟退火算法的离散变量优化设计方法则可以方便快捷地获得满足各方面要求的最优设计方案。     

A large-scale hybrid simulated annealing algorithm for cyclic facility layout problems

The cyclic facility layout problem (CFLP) is a special case of the dynamic facility layout problem (DFLP) in which there are several production periods and the production cycle repeats itself by going to the first period after the last one because of the seasonal nature of products. In this article, a mixed integer programming formulation is developed for the CFLP. In the DFLP literature, department shapes are assumed to be given or fixed. However, this assumption does not hold in the case of the CFLP because the facility size is limited and the area requirements of the departments change significantly throughout the planning horizon. Therefore, department dimensions and sizes are considered as decision variables in the CFLP. A large-scale hybrid simulated annealing algorithm (LS-HSA) is proposed to solve the formulated problem and shown to be effective and versatile as it can be applied to various facility layout problems.     

退火-遗传算法寻优及其实现

分析了遗传算法及退火算法的优缺点,提出用退火算法改进遗传算法局部的最优值搜索效率低问题。退火算法与遗传算法融合后,使算法在寻优结果上更加迅速精确。通过水泥的配比工程实例,与单纯的遗传算法的结果进行对比,说明该方法是有效的。     

一种采用自适应遗传算法的模糊自整定控制器

针对模糊自整定控制器参数寻优能力差的不足,研究了采用自适应交叉概率与变异概率的遗传算法,提出了用这种自适应遗传算法改善模糊自整定控制器性能的方法。对采用自适应遗传算法的模糊自整定控制器与一般的模糊自适应控制器作了仿真对比研究,说明了前者的优越性。     

自适应模拟退火结合共轭梯度法求解薄膜厚度

根据薄膜光学计算理论和最优化理论,提出了用自适应模拟退火法结合共轭梯度法确定薄膜厚度的新方法。该方法建立的数学模型先采用自适应模拟退火算法搜索,再采用共轭梯度算法精确查找。它不但减少了无损伤测量方法对初始计算条件的过多依赖,而且在保证精确度的情况下极大地提高了计算速度,同时有很高的适应性。实验中,应用该方法求解了三层膜系的厚度,计算时间为3s,计算误差小于4nm。     

An efficient hybrid evolutionary heuristic using genetic algorithm and simulated annealing algorithm to solve machine loading problem in FMS

In this paper, a machine loading problem in a flexible manufacturing system (FMS) is discussed, with bi-criterion objectives of minimising system imbalance and maximising system throughput in the occurrence of technological constraints such as available machining time and tool slots. A mathematical model is used to select machines, assign operations and the required tools in order to minimise the system's imbalance while maximising the throughput. An efficient evolutionary algorithm by hybridising the genetic algorithm (GA) and simulated annealing (SA) algorithm called GASA is proposed in this paper. The performance of the GASA is tested by using 10 sample dataset and the results are compared with the heuristics reported in the literature. The influence of genetic operators on the evolutionary search in GASA is studied and reported. Two machine selection heuristics are proposed and their influence on the quality of the solution is also studied. Extensive computational experiments have been carried out to evaluate the performance of the proposed evolutionary heuristics and the results are presented in tables and figures. The results clearly support the better performance of GASA over the algorithms reported in the literature.     

Using simulated annealing to schedule a flowshop manufacturing cell with sequence-dependent family setup times

This paper proposes a simulated annealing-based meta-heuristic to minimise makespan in a flowshop manufacturing cell with sequence-dependent family setup times. To escape from local minima, Cauchy function?–?rather than the Boltzmann function?–?is used during the annealing process. The effectiveness and efficiency of the proposed simulated annealing-based meta-heuristic is compared against the existing heuristics on a benchmark problem dataset used in earlier studies. These computational results show that the proposed simulated annealing-based meta-heuristic is highly effective as compared to the state-of-the-art meta-heuristics for this problem on the same benchmark instances.     

Product portfolio planning: a metaheuristic-based simulated annealing algorithm

Product portfolio diversity is prominent for customers, but critical for manufacturers. From the manufacturers’ perspective, diversity must be maintained at a level where engineering costs do not exceed the acquired advantages of increased market share. Therefore, in this paper we consider the problem of product portfolio planning to simultaneously maximise market share and minimise manufacturing engineering costs. Since this problem belongs to the NP-hard class of problems, exact algorithms are incapable of rendering an optimal solution. Therefore, we used metaheuristic-based simulated annealing to deal with the problem. Our proposed algorithm consists of two parts, i.e. a random search and a predetermined rule to generate the next possible neighbours (product portfolio). In order to have a robust algorithm, we calibrated different levels of our problem's parameters using the Taguchi method. This method picks the best levels of different parameters, conducting the least possible experiments. To evaluate the performance of our proposed algorithm, we compared it with a strong algorithm – the genetic algorithm. We used this comparison as the basis of our research. The obtained computational results clearly demonstrate the efficiency and effectiveness of our proposed algorithm.     

基于模拟退火改进的神经网络算法在颗粒碰撞阻尼研究中的应用

振动问题的普遍存在,使得对振动的研究极为必要。振动分析中建立的系统阻尼的目标函数往往比较复杂,影响因素众多,很难用传统数学建模的方法建立模型,传统的神经网络分析和模拟也很难得到建模问题的全局最优解。为此,将传统的反向传播算法（BP算法）神经网络模型结合模拟退火算法及最佳保留原则,提出一种改进的神经网络模型,得出了颗粒粒度、颗粒填充率和系统阻尼之间的关系。     

Metric-driven mesh optimization using a local simulated annealing algorithm

We report on results obtained with a metric-driven mesh optimization procedure for simplicial meshes based on the simulated annealing (SA) method. The use of SA improves the chances of removing pathological clusters of bad elements, that have the tendency to lock into frozen configurations in difficult regions of the model such as corners and complex face intersections, prejudicing the overall quality of the final grid. A local version of the algorithm is developed that significantly lowers the computational cost. Numerical examples illustrate the effectiveness of the proposed methodology, which is compared to a classical greedy Gauss–Seidel optimization. Substantial improvement in the quality of the worst elements of the grid is observed for the local simulated annealing optimization. Furthermore, the method appears to be robust to the choice of the algorithmic parameters. Copyright © 2006 John Wiley & Sons, Ltd.     

基于改进模拟退火算法的复合材料层合板频率优化

针对复合材料层合板频率优化问题,结合可行规则法和直接搜索模拟退化算法,提出了一种自适应模拟退火(SA)改进算法。层合板优化目标是基频、频率带隙以及给定基频和带隙约束的层合板厚度。设计变量包括铺层角度和铺层数两种离散变量。改进算法的自适应新点产生模块采用依赖温度的动态调整搜索半径,改善了直接搜索模拟退化(DSA)算法易陷入局部极值的缺陷,而可行规则法的引入提高了SA算法求解约束问题的效率和简易性。采用Ritz法进行频率响应分析以考虑弯扭耦合影响。不同铺层数、角度增量和长宽比时的层合板3类算例结果显示:改进算法能有效求解层合板频率优化,可获得更多或更好的铺层顺序全局优化解。     

Using queuing theory and simulated annealing to design the facility layout in an AGV-based modular manufacturing system

An automated guided vehicle-based flow production system is used for manufacturing prefabricated bathroom units. One unit can occupy a space of more than 10?m². Due to large time deviations in sequential processes, queues are formed and greater plant space is needed. Reducing work-in-progress helps to save plant space but renders manufacture less efficient. The research explores better workstation arrangements. An open queuing network (OQN) model was used to approximate the flow production system. Since the problem of workstation arrangement is a combinatorial optimisation problem, simulated annealing (SA) was applied to search for a good solution. The combination of an OQN model and SA provides a powerful tool to solve the facility layout problem for a stochastic flow production system. The experimental results show that the proposed approach has the potential to guide industrial layout design and practice.     

Hybrid simulated annealing with memory: an evolution-based diversification approach

This study presents an efficient metaheuristic approach for combinatorial optimisation and scheduling problems. The hybrid algorithm proposed in this paper integrates different features of several well-known heuristics. The core component of the proposed algorithm is a simulated annealing module. This component utilises three types of memories, one long-term memory and two short-term memories. The main characteristics of the proposed metaheuristic are the use of positive (reinforcement) and negative (inhibitory) memories as well as an evolution-based diversification approach. Job shop scheduling is selected to evaluate the performance of the proposed method. Given the benchmark problem, an extended version of the proposed method is also developed and presented. The extended version has two distinct features, specifically designed for the job shop scheduling problem, that enhance the performance of the search. The first feature is a local search that partially explores alternative solutions on a critical path of any current solution. The second feature is a mechanism to resolve possible deadlocks that may occur during the search as a result of shortage in acceptable solutions. For the case of job shop scheduling, the computational results and comparison with other techniques demonstrate the superior performance of the proposed methods in the majority of cases.     

Solving a stochastic berth allocation problem using a hybrid sequence pair-based simulated annealing algorithm

In this article, a continuous berth allocation problem is studied with stochastic ship arrival and handling times. The objective is to minimize a weighted sum of the expected waiting costs, berthing deviation costs and expected overtime costs. The sequence pair representation is utilized to project the solution space of the problem into two permutations. Then, a scenario-based method is used to capture the uncertainty. To effectively solve the problem over the sequence pair solution space, a simulated annealing is combined with two algorithms. One of the algorithms is used to determine the berthing positions and the other one is used to determine the berthing times. Computational experiments are conducted to evaluate the performance of the solution method and to verify the advantages of the proposed stochastic approach. The results indicate that the proposed methodology is both efficient and effective.