Global Optimization Performance Measures for Generalized Hill Climbing Algorithms

Generalized hill climbing algorithms provide a framework for modeling several local search algorithms for hard discrete optimization problems. This paper introduces and analyzes generalized hill climbing algorithm performance measures that reflect how effectively an algorithm has performed to date in visiting a global optimum and how effectively an algorithm may pes]rform in the future in visiting such a solution. These measures are also used to obtain a necessary asymptotic convergence (in probability) condition to a global optimum, which is then used to show that a common formulation of threshold accepting does not converge. These measures assume particularly simple forms when applied to specific search strategies such as Monte Carlo search and threshold accepting.     

Tabu Guided Generalized Hill Climbing Algorithms

This paper formulates tabu search strategies that guide generalized hill climbing (GHC) algorithms for addressing NP-hard discrete optimization problems. The resulting framework, termed tabu guided generalized hill climbing (TG²HC) algorithms, uses a tabu release parameter that probabilistically accepts solutions currently on the tabu list. TG²HC algorithms are modeled as a set of stationary Markov chains, where the tabu list is fixed for each outer loop iteration. This framework provides practitioners with guidelines for developing tabu search strategies to use in conjunction with GHC algorithms that preserve some of the algorithms known performance properties. In particular, sufficient conditions are obtained that indicate how to design iterations of problem-specific tabu search strategies, where the stationary distributions associated with each of these iterations converge to the distribution with zero weight on all non-optimal solutions.     

Finite-Time Performance Analysis of Static Simulated Annealing Algorithms

Generalized hill climbing (GHC) algorithms provide a framework for modeling local search algorithms for addressing intractable discrete optimization problems. Current theoretical results are based on the assumption that the goal when addressing such problems is to find a globally optimal solution. However, from a practical point of view, solutions that are close enough to a globally optimal solution (where close enough is measured in terms of the objective function value) for a discrete optimization problem may be acceptable. This paper introduces -acceptable solutions, where is a value greater than or equal to the globally optimal objective function value. Moreover, measures for assessing the finite-time performance of GHC algorithms, in terms of identifying -acceptable solutions, are defined. A variation of simulated annealing (SA), termed static simulated annealing (S²A), is analyzed using these measures. S²A uses a fixed cooling schedule during the algorithm's execution. Though S²A is provably nonconvergent, its finite-time performance can be assessed using the finite-time performance measures defined in terms of identifying -acceptable solutions. Computational results with a randomly generated instance of the traveling salesman problem are reported to illustrate the results presented. These results show that upper and lower estimates for the number of iterations to reach a -acceptable solution within a specified number of iterations can be obtained, and that these estimates are most accurate for moderate and high fixed temperature values for the S²A algorithm.     

Simulated Annealing Algorithms for Continuous Global Optimization: Convergence Conditions

In this paper, simulated annealing algorithms for continuous global optimization are considered. After a review of recent convergence results from the literature, a class of algorithms is presented for which strong convergence results can be proved without introducing assumptions which are too restrictive. The main idea of the paper is that of relating both the temperature value and the support dimension of the next candidate point, so that they are small at points with function value close to the current record and bounded away from zero otherwise.     

Performance Analysis of Cyclical Simulated Annealing Algorithms

Generalized hill climbing (GHC) algorithms provide a framework for modeling local search algorithms for addressing intractable discrete optimization problems. Measures for assessing the finite-time performance of GHC algorithms have been developed using this framework, including the expected number of iterations to visit a predetermined objective function value level. This paper analyzes how the expected number of iterations to visit a predetermined objective function value level can be estimated for cyclical simulated annealing. Cyclical simulated annealing uses a cooling schedule that cycles through a set of temperature values. Computational results with traveling salesman problem instances taken from TSPLIB show how the expected number of iterations to visit solutions with predetermined objective function levels can be estimated for cyclical simulated annealing.AMS 2000 Subject Classification 90-08 Computational Methods: Local Search, 90C59 Heuristics: Simulated Annealing     

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.     

A Hybrid Meta-Heuristic for Multi-Objective Optimization: MOSATS

Real optimization problems often involve not one, but multiple objectives, usually in conflict. In single-objective optimization there exists a global optimum, while in the multi-objective case no optimal solution is clearly defined but rather a set of solutions, called the Pareto-optimal front. Thus, the goal of multi-objective strategies is to generate a set of non-dominated solutions as an approximation to this front. However, the majority of problems of this kind cannot be solved exactly because they have very large and highly complex search spaces. In recent years, meta-heuristics have become important tools for solving multi-objective problems encountered in industry as well as in the theoretical field. This paper presents a novel approach based on hybridizing Simulated Annealing and Tabu Search. Experiments on the Graph Partitioning Problem show that this new method is a better tool for approximating the efficient set than other strategies also based on these meta-heuristics.     

Parallel Simulated Annealing Algorithms in Global Optimization

Global optimization involves the difficult task of the identification of global extremities of mathematical functions. Such problems are often encountered in practice in various fields, e.g., molecular biology, physics, industrial chemistry. In this work, we develop five different parallel Simulated Annealing (SA) algorithms and compare them on an extensive test bed used previously for the assessment of various solution approaches in global optimization. The parallel SA algorithms consist of various categories: the asynchronous approach where no information is exchanged among parallel runs and the synchronous approaches where solutions are exchanged using genetic operators, or where solutions are transmitted only occasionally, or where highly coupled synchronization is achieved at every iteration. One of these approaches, which occasionally applies partial information exchanges (controlled in terms of solution quality), provides particularly notable results for functions with vast search spaces of up to 400 dimensions. Previous attempts with other approaches, such as sequential SA, adaptive partitioning algorithms and clustering algorithms, to identify the global optima of these functions have failed without exception.     

Rescaled Simulated Annealing—Accelerating Convergence of Simulated Annealing by Rescaling the States Energies

This paper presents a new metaheuristic, called rescaled simulated annealing (RSA) which is particularly adapted to combinatorial problems where the available computational effort to solve it is limited. Asymptotic convergence on optimal solutions is established and the results are favorably compared to the famous ones due to Mitra, Romeo, and Sangiovanni-Vincentelli (Mitra, Romeo, and Sangiovanni-Vincentelli. (1986). Adv. Appl. Prob. 18, 747–771.) for simulated annealing (SA). It is based on a generalization of the Metropolis procedure used by the SA algorithm. This generalization consists in rescaling the energies of the states candidate for a transition, before applying the Metropolis criterion. The direct consequence is an acceleration of convergence, by avoiding dives and escapes from high energy local minima. Thus, practically speaking, less transitions need to be tested with RSA to obtain a good quality solution. As a corollary, within a limited computational effort, RSA provides better quality solutions than SA and the gain of performance of RSA versus SA is all the more important since the available computational effort is reduced. An illustrative example is detailed on an instance of the Traveling Salesman Problem.     

Annealing-tabu PAES: a multi-objective hybrid meta-heuristic

Most real-life optimization problems require taking into account not one, but multiple objectives simultaneously. In most cases these objectives are in conflict, i.e. the improvement of some objectives implies the deterioration of others. In single-objective optimization there exists a global optimum, while in the multi-objective case no optimal solution is clearly defined, but rather a set of solutions. In the last decade most papers dealing with multi-objective optimization use the concept of Pareto-optimality. The goal of Pareto-based multi-objective strategies is to generate a front (set) of non-dominated solutions as an approximation to the true Pareto-optimal front. However, this front is unknown for problems with large and highly complex search spaces, which is why meta-heuristic methods have become important tools for solving this kind of problem. Hybridization in the multi-objective context is nowadays an open research area. This article presents a novel extension of the well-known Pareto archived evolution strategy (PAES) which combines simulated annealing and tabu search. Experiments on several mathematical problems show that this hybridization allows an improvement in the quality of the non-dominated solutions in comparison with PAES, and also with its extension M-PAES.     

利用模拟退火算法进行油井流入动态研究

油井流入动态是油井举升优化设计的基础 ,目前国内外研究油井流入动态的方法有多种 ,但是各有其不足和局限性 .为此利用模拟退火方法进行油井流入动态方法研究 .与以往的近似算法相比 ,该方法不需要将单相流和多相流分开讨论 ,且无任何假设条件 ,适用范围广 ;通过实例 ,把模拟退火的预测的产量结果与利用 Vogel方程所预测的产量结果进行比较和误差分析 ,结果表明 ,该算法的误差是非常小的 .因此 ,利用该算法研究油井流入动态非常可行 .     

Crossing Over Genetic Algorithms: The Sugal Generalised GA

Sugal is a major new public-domain software package designed to support experimentation with, and implementation of, Genetic Algorithms. Sugal includes a generalised Genetic Algorithm, which supports the major popular versions of the GA as special cases. Sugal also has integrated support for various datatypes, including real numbers, and features to make hybridisation simple. This paper discusses the Sugal GA, showing how recombining the features of the popular algorithms results in the creation of a number of useful hybrid algorithms.     

Annealing Algorithms for Multisource Absolute Location Problems on Graph

A methodology is presented for applying annealing techniques tomultisource absolute location problems on graph. Two kinds ofobjective functions are considered: barycenters and centers. Aclass of new algorithms is described: its development startsfrom the iterative cluster-and-locate algorithm and reliesupon the relaxation of the integrality constraints onallocation variables. Experimental results are reported.     

经济业绩评价的广义DEA模型

经济业绩是衡量地区宏观经济发展状况的重要指标.从广义数据包络分析理论出发,建立了一种用于评价地区经济业绩的广义DEA模型,并运用模型,与中国其它直辖市相对比,对天津市1996—2012年的地区经济业绩进行了实证分析.     

Performance of the MOSA Method for the Bicriteria Assignment Problem

The classical linear Assignment problem is considered with two objectives. The aim is to generate the set of efficient solutions. An exact method is first developed based on the two-phase approach. In the second phase a new upper bound is proposed so that larger instances can be solved exactly. The so-called MOSA (Multi-Objective Simulated Annealing) is then recalled; its efficiency is improved by initialization with a greedy approach. Its results are compared to those obtained with the exact method. Extensive numerical experiments have been realized to measure the performance of the MOSA method.     

Convergence of the Simulated Annealing Algorithm for Continuous Global Optimization

A class of simulated annealing algorithms for continuous global optimization is considered in this paper. The global convergence property is analyzed with respect to the objective value sequence and the minimum objective value sequence induced by simulated annealing algorithms. The convergence analysis provides the appropriate conditions on both the generation probability density function and the temperature updating function. Different forms of temperature updating functions are obtained with respect to different kinds of generation probability density functions, leading to different types of simulated annealing algorithms which all guarantee the convergence to the global optimum.     

风险敏感度激励学习的广义平均算法

提出了一种新的算法．这个算法通过潜在地牺牲控制策略的最优性来获取其鲁棒性．这是因为,如果在理论模型与实际的物理系统之间存在不匹配,或者实际系统是非静态的,或者控制动作的可使用性随时间的变化而变化时,那么鲁棒性就可能成为一个十分重要的问题．主要工作是给出了一组逼近算法和它们的收敛结果．利用广义平均算子来替代最优算子max(或min),对激励学习中的一类最重要的算法——动态规划算法——进行了研究,并讨论了它们的收敛性,目的就是为了提高激励学习算法的鲁棒性．同时使用了更具一般性的风险敏感度性能评价体系,发现基于动态规划的学习算法中的一般结论在这种体系之下并不完全成立．     

First-Order Algorithms for Generalized Semi-Infinite Min-Max Problems

We present a first-order algorithm for solving semi-infinite generalized min-max problems which consist of minimizing a function f⁰(x) = F(¹(x), .... , ^m (x)), where F is a smooth function and each ⁱ is the maximum of an infinite number of smooth functions.In Section 3.3 of [17] Polak finds a methodology for solving infinite dimensional problems by expanding them into an infinite sequence of consistent finite dimensional approximating problems, and then using a master algorithm that selects an appropriate subsequence of these problems and applies a number of iterations of a finite dimensional optimization algorithm to each of these problems, sequentially. Our algorithm was constructed within this framework; it calls an algorithm by Kiwiel as a subroutine. The number of iterations of the Kiwiel algorithm to be applied to the approximating problems is determined by a test that ensures that the overall scheme retains the rate of convergence of the Kiwiel algorithm.Under reasonable assumptions we show that all the accumulation points of sequences constructed by our algorithm are stationary, and, under an additional strong convexity assumption, that the Kiwiel algorithm converges at least linearly, and that our algorithm also converges at least linearly, with the same rate constant bounds as Kiwiel's.     

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.