Stochastic techniques for global optimization: A survey of recent advances

In this paper stochastic algorithms for global optimization are reviewed. After a brief introduction on random-search techniques, a more detailed analysis is carried out on the application of simulated annealing to continuous global optimization. The aim of such an analysis is mainly that of presenting recent papers on the subject, which have received only scarce attention in the most recent published surveys. Finally a very brief presentation of clustering techniques is given.     

Scale Invariance Properties in the Simulated Annealing Algorithm

The Boltzmann distribution used in the steady-state analysis of the simulated annealing algorithm gives rise to several scale invariant properties. Scale invariance is first presented in the context of parallel independent processors and then extended to an abstract form based on lumping states together to form new aggregate states. These lumped or aggregate states possess all of the mathematical characteristics, forms and relationships of states (solutions) in the original problem in both first and second moments. These scale invariance properties therefore permit new ways of relating objective function values, conditional expectation values, stationary probabilities, rates of change of stationary probabilities and conditional variances. Such properties therefore provide potential applications in analysis, statistical inference and optimization. Directions for future research that take advantage of scale invariance are also discussed.     

现代优化计算方法在蛋白质结构预测中的应用

现代优化计算方法在蛋白质结构预测中占有重要地位.简要地介绍了模拟退火算法,遗传算法,人工神经网络和图论算法在蛋白质结构预测中的应用.对国内外近年来应用这些算法,特别是在蛋白质构象搜索问题中,解决蛋白质结构预测的研究作了回顾,并分析、比较了这几种算法的效果和特点.     

Parallelization of population-based multi-objective meta-heuristics: An empirical study

In single-objective optimization it is possible to find a global optimum, while in the multi-objective case no optimal solution is clearly defined, but several that simultaneously optimize all the objectives. However, the majority of this kind of problems cannot be solved exactly as they have very large and highly complex search spaces. Recently, meta-heuristic approaches have become important tools for solving multi-objective problems encountered in industry as well as in the theoretical field. Most of these meta-heuristics use a population of solutions, and hence the runtime increases when the population size grows. An interesting way to overcome this problem is to apply parallel processing. This paper analyzes the performance of several parallel paradigms in the context of population-based multi-objective meta-heuristics. In particular, we evaluate four alternative parallelizations of the Pareto simulated annealing algorithm, in terms of quality of the solutions, and speedup.     

模拟退火法在吸收薄膜的椭偏反演算法中的应用

将一种广泛用于求解复杂系统优化问题的技术－－模拟退火法－－用来求解椭偏反演方程。首先假设一个薄膜模型，计算出其相应的椭偏参数（Ψ，Δ）的值，在这个计算值的基础上加入不同标准偏差的高斯噪声；然后将加入噪声后的值（Ψm,Δm)作为模拟的测量数据，采用模拟退火算法进行求解，验证得知这种方法求得的薄膜参数很接近于假设的薄膜模型参数的真值，与其他文献的报道结果一致，而且在扩大搜寻范围时，仍然可以得到准确解，从而证明了该方法的可行性以及有效性。     

Parking Capacity and Pricing in Park'n Ride Trips: A Continuous Equilibrium Network Design Problem

In this paper we consider the problem of designing parking facilities for park'n ride trips. We present a new continuous equilibrium network design problem to decide the capacity and fare of these parking lots at a tactical level. We assume that the parking facilities have already been located and other topological decisions have already been taken.The modeling approach proposed is mathematical programming with equilibrium constraints. In the outer optimization problem, a central Authority evaluates the performance of the transport network for each network design decision. In the inner problem a multimodal traffic assignment with combined modes, formulated as a variational inequality problem, generates the share demand for modes of transportation, and for parking facilities as a function of the design variables of the parking lots. The objective is to make optimal parking investment and pricing decisions in order to minimize the total travel cost in a subnetwork of the multimodal transportation system.We present a new development in model formulation based on the use of generalized parking link cost as a design variable.The bilevel model is solved by a simulated annealing algorithm applied to the continuous and non-negative design decision variables. Numerical tests are reported in order to illustrate the use of the model, and the ability of the approach to solve applications of moderate size.     

Analysis of Static Simulated Annealing Algorithms

Generalized hill climbing (GHC) algorithms provide a framework for modeling local search algorithms to address intractable discrete optimization problems. This paper introduces a measure for determining the expected number of iterations to visit a predetermined objective function level, given that an inferior objective function level has been reached in a finite number of iterations. A variation of simulated annealing (SA), termed static simulated annealing (S²A), is analyzed using this measure. S²A uses a fixed cooling schedule during the algorithm execution. Though S²A is probably nonconvergent, its finite-time performance can be assessed using the finite-time performance measure defined in this paper.     

Synthesis and crystal structure of 1,3,5-tris [4-(phenylethynyl)phenyl]benzene

The crystal structure of 1,3,5-tris[4-(phenylethynyl)phenyl]benzene (1) has been investigated. Compound1 represents a model of the repeating unit of the most typical polyphenylene, which contains 1,3,5-trisubstituted benzene rings (chain centers) and acetylenic groups (complex-forming and cross-linking centers) in the main chain. The acetylene groups of neighboring molecules have a tendency to close mutual arrangement, which is favorable for their topochemical interaction. However, the relative conformational rigidity of molecules1 restricts not only the possibility of the optimal adjustment of the reactive sites of neighboring molecules to one another, but also hampers the close packing of molecules in the crystal, which contains channels filled by the solvent molecules (chloroform).Translated fromIzvestiya Akademii Nauk. Seriya Khimicheskaya, No. 11, pp. 1986–1992, November, 1994.The work was carried out with the financial support of the International Science Foundation and the American Crystallographic Association.     

Azine bridged silver coordination polymers: Powder X-ray diffraction route to crystal structure determination of silver benzotriazole

In continuation of our interest in solid-state structures of silver complexes of photographic importance, the structure for silver benzotriazole (AgBZT), has now been obtained. The preferred method for solving crystal structures is via single-crystal X-ray diffraction (XRD). However, for some materials, growing single crystals of appropriate size and quality is often difficult or even impossible. AgBZT is an example of such a silver complex with poor solubility. The usual routes to preparing single crystals using recrystallization from a cooperating solvent resulted in polycrystalline powder samples. We propose a crystal structure for AgBZT, solved from synchrotron X-ray powder diffraction data, using a direct-space Monte Carlo simulated annealing approach. AgBZT crystals are monoclinic, (P2₁/c), with unit cell dimensions, a=14.8052(3) Å, b=3.7498(4) Å, c=12.3495(12) Å, and β=114.200(6)°. The AgBZT complex is constructed from all three of the Benzotriazole (BZT) nitrogens bonding to a separate silver atom. As a consequence of this bonding mode, the structure is a highly cross-linked, coordination polymer.     

A Generalized Markov Sampler

A recent development of the Markov chain Monte Carlo (MCMC) technique is the emergence of MCMC samplers that allow transitions between different models. Such samplers make possible a range of computational tasks involving models, including model selection, model evaluation, model averaging and hypothesis testing. An example of this type of sampler is the reversible jump MCMC sampler, which is a generalization of the Metropolis–Hastings algorithm. Here, we present a new MCMC sampler of this type. The new sampler is a generalization of the Gibbs sampler, but somewhat surprisingly, it also turns out to encompass as particular cases all of the well-known MCMC samplers, including those of Metropolis, Barker, and Hastings. Moreover, the new sampler generalizes the reversible jump MCMC. It therefore appears to be a very general framework for MCMC sampling. This paper describes the new sampler and illustrates its use in three applications in Computational Biology, specifically determination of consensus sequences, phylogenetic inference and delineation of isochores via multiple change-point analysis.