一种改进的禁忌搜索算法及其在连续全局优化中的应用

禁忌搜索算法是一种元启发式的全局优化算法,是局部搜索算法的一种推广,已被成功地应用于许多组合优化问题中。本文针对有界闭区域上的连续函数全局优化问题,提出了一种改进的禁忌搜索算法,并进行了理论分析和数值实验。数值实验表明,对于连续函数全局优化问题的求解该算法是可行有效的,并且结构简单,迭代次数较少,是一种较好的全局启发式优化算法。     

基于偏最小二乘变换与Kriging模型的全局优化方法

针对传统Kriging模型在多变量(高维)输入全局优化中因超参数过多而引发收敛速度慢,精度低,建模效率不高问题,提出了基于偏最小二乘变换技术和Kriging模型的有效全局优化方法.首先,构造偏最小二乘高斯核函数;其次,借助差分进化算法寻找满足期望改进准则最大化条件的新样本点;然后,将不同核函数和期望改进准则组合,构建四种有效全局优化算法并进行比较;最后,数值算例结果表明,基于偏最小二乘变换的Kriging全局优化方法在解决高维全局优化问题方面相比于标准的全局优化算法在收敛精度及收敛速度方面更具优势.     

求解高维复合体函数的智能优化算法

由于粒子群算法在处理高维复杂函数时存在容易陷入局部最优的问题,提出了多种群子空间学习粒子群算法(SLPSO),采用多种群进化模式,在粒子更新公式中加入了全局最优粒子,加快了粒子收敛速度,同时在种群之间采用了交叉学习的方法,大大提高了算法的全局搜索能力.另外,还增加了一种子空间学习方法,充分地利用粒子的历史经验,有效地避免了陷入局部最优的问题.通过在高维基准测试函数的仿真实验表明,SLPSO算法的测试结果都明显优于其他两种算法,随着函数维数增加,SLPSO算法测试结果的下降幅度明显低于其他两种算法.在6个极其复杂的复合函数的测试中,SLPSO算法有2个测试函数结果非常接近理论值,其他4个也明显优于其他三种算法.     

线性二阶锥互补问题的一种非精确光滑算法

在光滑算法的框架下,就线性二阶锥互补问题,给出了一种非精确光滑算法. 在适当的条件下,证明了该算法具有全局收敛性. 数值试验表明该算法对高维线性二阶锥互补问题是有效的.     

含有等式约束非线性规划的全局优化算法

针对含有多个等式约束的非线性规划问题,提出一个全局优化算法.该方法基于可行集策略把改进的模拟退火方法与确定的局部算法方法相结合.对算法的收敛性进行了证明,数值结果表明算法的有效性及正确性.     

基于模拟退火的CRS算法

本文是对非线性规划问题提出的一种算法,该算法把模拟退火算法应用到CRS算法中,根据模拟退火算法每一次迭代都体现集中和扩散两个策略的平衡的特点,使CRS算法更能够搜索到全局最优解,而不会陷入局部最优解。最后把提出的算法应用到两个典型的函数优化问题中,结果表明,算法是可行的、有效的     

模拟退火猫群优化算法

猫群优化算法(Cat Swarm Optimization,CSO)是建立在猫的行为模式和群体智能基础上的一种新型群体智能优化算法。为提高猫群优化算法的性能,把模拟退火算法应用于猫群优化算法,提出模拟退火猫群优化算法(Simulated Annealing Cat Swarm Optimization,SACSO),通过变异算子调整所要优化的种群。其基本过程为先行产生随机初始种群,接着进行搜索,并设置初始温度,继而应用模拟退火算法获取全局最优替代值,再依据位置和速度公式更新新解,然后在个体较优位置再运用变异运算,进行进一步地搜索。然后分别将猫群优化算法、模拟退火粒子群算法(Simulated Annealing Particle Swarm Optimization,SAPSO)、模拟退火猫群优化算法在11个典型的基准测试函数下进行仿真对比,结果表明模拟退火猫群优化算法不仅增加了全局收敛性,而且在收敛速度和精度方面均优于其它两种算法。     

一种具有非线性约束线性规划全局优化算法

本文提出了一种新的适用于处理非线性约束下线性规划问题的全局优化算法。该算法通过构造子问题来寻找优于当前局部最优解的可行解。该子问题可通过模拟退火算法来解决。通过求解一系列的子问题,当前最优解被不断地更新,最终求得全局最优解。最后,本算法应用于几个典型例题,并与罚函数法相比较,数值结果表明该算法是可行的,有效的。     

带有资源柔性约束的max-NPV分布式多项目调度问题

现有的分布式资源约束多项目调度问题研究中,假定全局资源限量在多项目工期内不可突破且多以工期为优化目标。针对此问题,考虑全局资源可从外部获取,以净现值为目标,构建带有全局资源柔性约束的分布式多项目调度问题的整数规划模型并设计有效的求解算法。首先,界定问题并确定项目现金流的计算方法;然后,针对求解问题的NP-hard属性,设计了遗传-模拟退火混合算法(GA_SA)求解此模型。最后,通过多组数值实验,设计不同算法与GA_SA算法进行比较,并分析了关键参数对多项目净现值的影响。结果表明,GA_SA算法具有较好的求解效果;与传统的全局资源刚性约束条件相比,全局资源柔性使用状态可以显著改善分布式多项目的收益绩效。     

解约束优化问题的投影梯度型中心方法

本文提出了一种求解约束优化问题的新算法—投影梯度型中心方法．在连续可微和非退化的假设条件下，证明了其全局收敛性．本文算法计算简单且形式灵活．     

Solving Location-allocation Problems with Rectilinear Distances by Simulated Annealing

The objective of this study is to use the simulated annealing method to solve minisum location-allocation problems with rectilinear distances. The major advantage of the simulated annealing method is that it is a very general and efficient algorithm for solving combinatorial optimization problems with know objective functions. In this study, a simulated annealing algorithm was developed to solve the location-allocation problems, and its performance was compared with two other popular methods for solving location-allocation problems. The results show that simulated annealing is a good alternative to the two methods, as measured by both the solution quality and the computational time.     

A comparison of local search methods for flow shop scheduling

Local search techniques are widely used to obtain approximate solutions to a variety of combinatorial optimization problems. Two important categories of local search methods are neighbourhood search and genetic algorithms. Commonly used neighbourhood search methods include descent, threshold accepting, simulated annealing and tabu search. In this paper, we present a computational study that compares these four neighbourhood search methods, a genetic algorithm, and a hybrid method in which descent is incorporated into the genetic algorithm. The performance of these six local search methods is evaluated on the problem of scheduling jobs in a permutation flow shop to minimize the total weighted completion time. Based on the results of extensive computational tests, simulated annealing is found to generate better quality solutions than the other neighborhood search methods. However, the results also indicate that the hybrid genetic descent algorithm is superior to simulated annealing.     

Molecular conformation on the CM-5 by parallel two-level simulated annealing

In this paper, we propose a new kind of simulated annealing algorithm calledtwo-level simulated annealing for solving certain class of hard combinatorial optimization problems. This two-level simulated annealing algorithm is less likely to get stuck at a non-global minimizer than conventional simulated annealing algorithms. We also propose a parallel version of our two-level simulated annealing algorithm and discuss its efficiency. This new technique is then applied to the Molecular Conformation problem in 3 dimensional Euclidean space. Extensive computational results on Thinking Machines CM-5 are presented. With the full Lennard-Jones potential function, we were able to get satisfactory results for problems for cluster sizes as large as 100,000. A peak rate of over 0.8 giga flop per second in 64-bit operations was sustained on a partition with 512 processing elements. To the best of our knowledge, ground states of Lennard-Jones clusters of size as large as these have never been reported before.Also a researcher at the Army High Performance Computing Research Center, University of Minnesota, Minneapolis, MN 55415     

Stopping and restarting strategy for stochastic sequential search in global optimization

Two common questions when one uses a stochastic global optimization algorithm, e.g., simulated annealing, are when to stop a single run of the algorithm, and whether to restart with a new run or terminate the entire algorithm. In this paper, we develop a stopping and restarting strategy that considers tradeoffs between the computational effort and the probability of obtaining the global optimum. The analysis is based on a stochastic process called Hesitant Adaptive Search with Power-Law Improvement Distribution (HASPLID). HASPLID models the behavior of stochastic optimization algorithms, and motivates an implementable framework, Dynamic Multistart Sequential Search (DMSS). We demonstrate here the practicality of DMSS by using it to govern the application of a simple local search heuristic on three test problems from the global optimization literature.     

Scheduling projects to maximize net present value — the case of time-dependent,contingent cash flows

The problem of scheduling activities in a project to maximize its Net Present Value (NPV) has been solved for the case where net cash flow magnitudes are independent of the time of realization. This paper models a more realistic version of this problem — because of incentive payments and penalties for early and late event occurrences, respectively, and because of changing costs of resources over time, net cash flow magnitudes are dependent on the time of realization. We formulate an optimization program for this more general problem and present a simulated annealing solution approach. We test different implementation strategies for this algorithm and suggest a method for choosing neighborhood moves. We compare the NPVs of the solutions obtained from our formulation with the NPVs of early start schedules and with late start schedules for 168 different problems. These computational results show that the simulated annealing approach consistently produces substantially better solutions than the early start or late start schedules. Even poor simulated annealing neighborhood moves give improved solutions for most problems studied.     

Simulated N-Body: New Particle Physics-Based Heuristics for a Euclidean Location-Allocation Problem

The general facility location problem and its variants, including most location-allocation and P-median problems, are known to be NP-hard combinatorial optimization problems. Consequently, there is now a substantial body of literature on heuristic algorithms for a variety of location problems, among which can be found several versions of the well-known simulated annealing algorithm. This paper presents an optimization paradigm that, like simulated annealing, is based on a particle physics analogy but is markedly different from simulated annealing. Two heuristics based on this paradigm are presented and compared to simulated annealing for a capacitated facility location problem on Euclidean graphs. Experimental results based on randomly generated graphs suggest that one of the heuristics outperforms simulated annealing both in cost minimization as well as execution time. The particular version of location problem considered here, a location-allocation problem, involves determining locations and associated regions for a fixed number of facilities when the region sizes are given. Intended applications of this work include location problems with congestion costs as well as graph and network partitioning problems.     

A hybrid glowworm swarm optimization algorithm to solve constrained multimodal functions optimization

In this paper, a novel hybrid glowworm swarm optimization (HGSO) algorithm is proposed. The HGSO algorithm embeds predatory behaviour of artificial fish swarm algorithm (AFSA) into glowworm swarm optimization (GSO) algorithm and combines the GSO with differential evolution on the basis of a two-population co-evolution mechanism. In addition, to overcome the premature convergence, the local search strategy based on simulated annealing is applied to make the search of GSO approach the true optimum solution gradually. Finally, several benchmark functions show that HGSO has faster convergence efficiency and higher computational precision, and is more effective for solving constrained multi-modal function optimization problems.     

A simulated annealing driven multi-start algorithm for bound constrained global optimization

A derivative-free simulated annealing driven multi-start algorithm for continuous global optimization is presented. We first propose a trial point generation scheme in continuous simulated annealing which eliminates the need for the gradient-based trial point generation. We then suitably embed the multi-start procedure within the simulated annealing algorithm. We modify the derivative-free pattern search method and use it as the local search in the multi-start procedure. We study the convergence properties of the algorithm and test its performance on a set of 50 problems. Numerical results are presented which show the robustness of the algorithm. Numerical comparisons with a gradient-based simulated annealing algorithm and three population-based global optimization algorithms show that the new algorithm could offer a reasonable alternative to many currently available global optimization algorithms, specially for problems requiring ‘direct search’ type algorithm.     

A Controlled Random Search Technique Incorporating the Simulated Annealing Concept for Solving Integer and Mixed Integer Global Optimization Problems

In this paper, a computational algorithm, named RST2ANU algorithm, has been developed for solving integer and mixed integer global optimization problems. This algorithm, which primarily is based on the original controlled random search approach of Price [22i], incorporates a simulated annealing type acceptance criterion in its working so that not only downhill moves but also occasional uphill moves can be accepted. In its working it employs a special truncation procedure which not only ensures that the integer restrictions imposed on the decision variables are satisfied, but also creates greater possibilities for the search leading to a global optimal solution. The reliability and efficiency of the proposed RST2ANU algorithm has been demonstrated on thirty integer and mixed integer optimization problems taken from the literature. The performance of the algorithm has been compared with the performance of the corresponding purely controlled random search based algorithm as well as the standard simulated annealing algorithm. The performance of the method on mathematical models of three realistic problems has also been demonstrated.