Simulated Annealing

Particle Swarm Optimization is a popular heuristic search algorithm which is inspired by the social learning of birds or fishes. It is a swarm intelligence technique for optimization developed by Eberhart and Kennedy [1] in 1995. Inertia weight is an important parameter in PSO, which significantly affects the convergence and exploration-exploitation trade-off in PSO process. Since inception of Inertia Weight in PSO, a large number of variations of Inertia Weight strategy have been proposed. In order to propose one or more ...

In this paper, we investigate camera calibration in soccer field using a field model defined according to FIFA standards. The proposed algorithm searches for homographic transform matrix for best correspondence between world coordinate and image coordinate systems. To find this matrix, some static markers are required in image and field. A good proposal for these markers is the intersections of lines in field and image. These points are transferred to Simulated Annealing algorithm then it searches for the best correspondence between image and field lines. In this work, we have tested several frames of soccer games and have obtained fairly good results.

The Quadratic Assignment Problem (QAP) is one of the classical combinatorial optimization problems and is known for its diverse applications. In this paper, we suggest a genetic algorithm for the QAP and report its computational behavior. The genetic algorithm incorporates many greedy principles in its design and, hence, we refer to it as a greedy genetic algorithm. The ideas we incorporate in the greedy genetic algorithm include (i) generating the initial population using a randomized construction heuristic; (ii) new crossover schemes; (iii) a special purpose immigration scheme that promotes diversity; (iv) periodic local optimization of a subset of the population; (v) tournamenting among different populations; and (vi) an overall design that attempts to strike a balance between diversity and a bias towards fitter individuals. We test our algorithm on all the benchmark instances of QAPLIB, a well-known library of QAP instances. Out of the 132 total instances in QAPLIB of varied sizes, the greedy genetic algorithm obtained the best known solution for 103 instances, and for the remaining instances (except one) found solutions within 1 % of the best known solutions.

The V2 vasopressin renal receptor (V2R), which controls antidiuresis in mammals, is a member of the large family of heptahelical transmembrane (7TM) G protein-coupled receptors (GPCRs). Using the automated GPCR modeling facility available via Internet (http://expasy.hcuge.ch/swissmod/SWISS-MODEL.html) for construction of the 7TM domain in accord with the bovine rhodopsin (RD) footprint, and the SYBYL software for addition of the intra-and extracellular domains, the human V2R was modeled. The structure was further refined and its conformational variability tested by the use of a version of the Constrained Simulated Annealing (CSA) protocol developed in this laboratory. An inspection of the resulting structure reveals that the V2R (likewise any GPCR modeled this way) is much thicker and accordingly forms a more spacious TM cavity than most of the hitherto modeled GPCR constructs do, typically based on the structure of bacteriorhodopsin (BRD). Moreover, in this model the 7TM helices are arranged differently than they are in any BRD-based model. Thus, the topology and geometry of the TM cavity, potentially capable of receiving ligands, is in this model quite different than it is in the earlier models. In the subsequent step, two ligands, the native [arginine 8 ]vasopressin (AVP) and the selective agonist [Darginine 8 ]vasopressin (DAVP) were inserted, each in two topologically non-equivalent ways, into the TM cavity and the resulting structures were equilibrated and their conformational variabilities tested using CSA as above. The best docking was selected and justified upon consideration of ligand-receptor interactions and structure-activity data. Finally, the amino acid residues were indicated, mainly in TM helices 3-7, as potentially important in both AVP and DAVP docking.

This paper examines several heuristic algorithms for the maximum allowable workload (MAW) problem for real-time systems with tasks having variable workloads. Briefly, the problem concerns the allocation of n tasks to m processors, where each task t is characterized by a function t.r(w) that gives the running time of the task in terms of its workload (or input size) w. The objective of the maximum allowable workload problem is to find an allocation of tasks to processors so that the allocation is feasible (no task misses its deadline) when each task is given a workload of w or smaller and w is maximized. This optimization problem uses a robustness measure that is closely related to the MAIL (maximum allowable increase in load) metric recently proposed by Gertphol et. al. The main contribution of this paper is the the comparison of several heuristic algorithms for the MAW-RMS problem. Hillclimbing, random search, simulated annealing, and first-fit heuristics are presented and evaluated via simulation. As we show here, the first-fit greedy heuristic produces solutions of a reasonable quality compared to the other algorithms. In addition, we demonstrate the applicability of our model in air defense systems.

This paper explores the use of simulated annealing (SA) for solving arbitrary combinatorialoptimisation problems. It reviews an existing code called GPSIMAN for solving0-1 problems, and evaluates it against a commercial branch-and-bound code, OSL ...

School timetabling is a classical combinatorial optimization problem, which consists in assigning lessons to time slots, satisfying a set of constraints of various kinds. Due mostly to the constraints this problem falls in the category of NP-Complete problems. In this paper we try to show an implementation of a decision support system that solves real timetabling problems from various schools in Portugal. This implementation is based on the Simulated Annealing meta-heuristic. The constraints we use were obtained after inquiries made to several schools in Portugal. We show the results on three schools from different levels of teaching.

Discovery of the optimal best possibility of location for facilities is the central problem associated in logistics management. The optimal places for the distribution centres (DCs) can be based on the selected attributes that are crucial to locate the best possible locations to increase the speed of the facility service and thus reduce the overall transport cost and time and to provide best service. The major task is to identifying and locating the required number of DCs and its optimum locations are considered as the important goals for the design of any logistics network. The number of DCs will clearly depends upon many factors like population, capacity of the facility, type of facility etc. but locating the optimum locations of DCs will reduce the overall cost. But, for solving such a wide problem space, the powerful tools are the soft computing based approaches and that are well suited and find a meaningful solution in finite time. In this work, we are going to find the optimum locations of DCs for logistics using various soft computing methods. .

Over the past two decades there has been an increasing interest in using what has come to be called Evolutionary Computation (EC) in the analysis and optimization of structural systems. These methods include Genetic Algorithms (GA), Evolution Strategies (ES), Simulated Annealing and other stochastic based numerical methods. Each of these methods shares the drawback that they are very computationally intensive compared to deterministic methods. Furthermore, the computational burden can rapidly increase as the size of the analyzed structure increases. This paper suggests that the level of computation can be significantly reduced by avoiding the common practice of using ground structures in coding the topology. Additionally, comparative examples show that a broader range of good solutions can be reached when the use of ground structures is avoided.

This paper presents ESSR (Enhanced Simulated annealing for Single-row Routing) model for solving the single-row routing problem. The main objective in this problem is to produce a realization that minimizes both the street congestion and the number of doglegs. Simulated annealing (SA) is a stochastic, hill-climbing and gradient-descent technique based on the statistical properties of particles undergoing thermal annealing. By performing slow cooling, the nets in the single-row routing problem align themselves according to a configuration with the lowest energy. The model has been known to produce reasonably good solutions for many NP-complete optimization problems, such as the single-row routing problem. In ESSR, our strategy is to minimize both the street congestion and the number of interstreet crossings (doglegs) by expressing a single energy function as their collective properties. This objective is achieved by representing the energy as the absolute sum of the heights of the net segments. To speed up convergence, we pivot the street congestion value while having the energy drops directly proportional to the number of doglegs. This action has the effect of minimizing the number of doglegs as the energy stabilizes. Our simulation work on ESSR produces optimal results in most cases for both the street congestion and the number of doglegs. Our experimental results compare well against results obtained from our earlier model (SRR-7) and two other methods reported in the literature.

Owing to the wide utilization of heat exchangers in industrial processes, their cost minimization is an important target for both designers and users. Traditional design approaches are based on iterative procedures which gradually change the design and geometric parameters to satisfy a given heat duty and constraints. Although well proven, this kind of approach is time consuming and may not lead to cost effective design as no cost criteria are explicitly accounted for. The present study explores the use of nontraditional optimization technique: called simulated annealing (SA), for design optimization of shell and tube heat exchangers from economic point of view. The optimization procedure involves the selection of the major geometric parameters such as tube diameters, tube length, baffle spacing, number of tube passes, tube layout, type of head, baffle cut etc and minimization of total annual cost is considered as design target. The presented simulated annealing technique is simple ...

In order to construct a 3D model from a collection of 2D images of an object, an energy function is defined between the object's images and corresponding images of an articulated mesh in three dimensions. Repeated adjustment of the mesh to minimize the energy function results in a mesh that produces images which closely approximate the input images, that is to say that under the appropriate conditions it realizes a preconceived object. It has implications for model building, reverse engineering and computer vision. Minimization of the energy function is a multivariate problem of large scale with many local minima. We give an approach for solving this problem. For certain restricted, but useful applications, intuitive solutions to the minimization are consistently obtained. $

Assigning the resources of a virtual network to the components of a physical network, called Virtual Network Mapping, plays a central role in network virtualization. Existing approaches use classical heuristics like simulated annealing or attempt a two stage solution by solving the node mapping in a first stage and doing the link mapping in a second stage.

Markov chain methods for Boltzmann sampling work in phases with decreasing temperatures. The number of transitions in each phase crucially affects terminal state distribution. We employ dynamic programming to allocate iterations to phases to improve guarantees on sample quality. Numerical experiments on the Ising model are presented.

The MutT enzyme (129 residues) catalyzes the hydrolysis of normal and mutagenic nucleoside triphosphates, such as 8-oxo-dGTP, by substitution at the rarely attacked P-P, to yield NMP and pyrophosphate. Previous heteronuclear NMR studies of MutT have shown the secondary structure to consist of a five-stranded mixed P-sheet connected by the loop I-a-helix I-loop I1 motif, by two tight tums, and by loop 111, and terminated by loop IV-a-helix I1 [Abeygunawardana et al. (1993) Biochemistry 32, 1307 1 -13080; Weber et al. (1 993) Biochemistry 32, 1308 1 -13087). Complete side-chain assignments of IH and I3C resonances have now been made by 3D C(C0)NH and HCCH-TOCSY experiments. A total of 1461 interproton proximities (1 1 per residue), obtained by 3D 15N-resolved NOESY-HSQC and 3D I3C-resolved NOESY-HSQC spectra, including 372 long-range NOES, as well as 65 dihedral angle

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In this paper, we present an improved hybrid optimization algorithm, which was applied to the hard combinatorial optimization problem, the quadratic assignment problem (QAP). This is an extended version of the earlier hybrid heuristic approach proposed by the author. The new algorithm is distinguished for the further exploitation of the idea of hybridization of the well‐known efficient heuristic algorithms, namely, simulated annealing (SA) and tabu search (TS). The important feature of our algorithm is the so‐called “cold restart mechanism”, which is used in order to avoid a possible “stagnation” of the search. This strategy resulted in very good solutions obtained during simulations with a number of the QAP instances (test data). These solutions show that the proposed algorithm outperforms both the “pure” SA/TS algorithms and the earlier author's combined SA and TS algorithm. Key words: hybrid optimization, simulated annealing, tabu search, quadratic assignment problem, simulat...

The irregular shape packing problem is a combinatorial optimization problem that consists of arranging items on a container in such way that no item overlaps. In this paper we adopt a solution that places the items sequentially, touching the already placed items or the container. To place a new item without overlaps, the collision free region for the new item is robustly computed using non manifold Boolean operations. A simulated annealing algorithm controls the items sequence of placement, the item's placement and orientation. In this work, the placement occurs at collision free region's vertices. Several results with benchmark datasets obtained from the literature are reported. Some of them are the best already reported in the literature. To improve the computational cost performance of the algorithm, a parallelization method to determine the collision free region is proposed. We demonstrated two possible algorithms to compute the collision free region, and only one of them can be parallelized. The results showed that the parallelized version is better than the sequential approach only for datasets with very large number of items. The computational cost of the non manifold Boolean operation algorithm is strongly dependent on the number of vertices of the original polygons.

In this paper we study a model to estimate the probability that a target group of an advertising campaign is reached by a commercial message a given number of times. This contact frequency distribution is known to be computationally difficult to calculate because of dependence between the viewing probabilities of advertisements. Our model calculates good estimates of contact frequencies in a very short time based on data that is often available. A media planning model that optimizes effective reach as a function of contact frequencies demonstrates the usefulness of the model. Several local search procedures such as taboo search, simulated annealing and genetic algorithms are applied to find a good media schedule. The results show that local search methods are flexible, fast and accurate in finding media schedules for media planning models based on contact frequencies. The contact frequency model is a potentially useful new tool for media planners.

The most important issue in sequential program parallelisation is the efficient assignment of computations into different processing elements. In the past, too many approaches were devoted in efficient program parallelization considering various models for the parallel programs and the target architectures. The most widely used parallelism description model is the task graph model with precedence constraints. Nevertheless, as far as physical mapping of tasks onto parallel architectures is concerned, little research has given practical results. It is well known that the physical mapping problem is NP-hard in the strong sense, thus allowing only for heuristic approaches. Most researchers or tool programmers use exhaustive algorithms, or the classical method of simulated annealing. This paper presents an alternative approach onto the mapping problem. Given the graph of clustered tasks, and the graph of the target distributed architecture, our heuristic finds a mapping by first placing the highly communicative tasks on adjacent nodes of the processor network. Once these «backbone» tasks are mapped, there is no backtracking, thus achieving low complexity. Therefore, the remaining tasks are placed beginning from those close to the «backbone» tasks. The paper concludes with performance and comparison results which reveal the method's efficiency.

In this paper, we present a simulated annealing algorithm for solving multi-objective simulation optimization problems. The algorithm is based on the idea of simulated annealing with constant temperature, and uses a rule for accepting a candidate solution that depends on the individual estimated objective function values. The algorithm is shown to converge almost surely to an optimal solution. It is applied to a multi-objective inventory problem; the numerical results show that the algorithm converges rapidly.

266 Abstract—The estimation accuracy has been focused in various formal estimation models in recent research initiatives. The formal estimation models were developed to measure lines of code and function points in the software projects but most of them failed to improve accuracy in estimation. The concept of reusability in software development in estimating effort using artificial neural network is focused in this paper. Incorporation of reusability metrics in COCOMO II may yield better results. In COCOMO II it is very difficult to find the values of size parameters. A new model called COREAN has been proposed in this paper for better effort estimation accuracy and reliability. The proposed model has focused on two components of COCOMO II. First, instead of using RUSE cost driver, three new reuse cost drivers are introduced. Second, In order to reduce the project cost, three cost drivers such as PEXE, AEXE, LTEX are combined into single cost driver Personnel Experience (PLEX). Final...

This paper aims to model a consumer goods cross-docking problem, which is solved using metaheuristics to minimize makespan and determine the capacity in terms of inbound and outbound docks. The consumer-goods cross-docking problem is represented through inbound and outbound docks, customer orders (products to be delivered to customers), and metaheuristics as a solution method. Simulated annealing (SA) and particle swarm optimization (PSO) are implemented to solve the cross-docking problem. Based on the results of statistical analysis, it was identified that the two-way interaction effect between inbound and outbound docks, outbound docks and items, and items and metaheuristics are the most statistically significant on the response variable. The best solution provides the minimum makespan of 973.42 minutes considering nine inbound docks and twelve outbound docks. However, this study detected that the combination of six inbound docks and nine outbound docks represents the most efficie...

Chaotic simulated annealing (CSA) is a relatively new heuristic optimization technique and has been widely applied to optimization problems because of its simplicity and capability of finding fairly good solutions rapidly. However, currently only experimental results are used for verifying its superiority. In this paper, a new of chaotic simulated annealing method (CSA) is introduced and then a mathematic proof is given. It shows that the Markov Chain associated with the algorithm is weakly ergodic, which guarantees that the asymptotic behavior of the algorithm is independent of initial states. Furthermore, the theoretical analysis of the proposed CSA is very important to understand the essential features which make the algorithm work well.

The authors introduce maximum likelihood techniques for optimised discrimination between agricultural and wooded regions, based on a multitemporal sequencc of ERS images. The inherent resolution of the system is inadequate to make such a classification on an individual image. However, the different temporal change patterns of the two classes can be exploited. One approach uses joint annealed segmentation of the image sequence, providing optimised exploitation of the speckle model in determining the common set of region boundaries in the underlying radar cross-section. This is followed by maximum likelihood change detection using a normalised log temporal texture measure. This is shown to be superior to constructing the normalised log measure directly, including speckle fluctuations, followed by a single annealed segmentation process. Finally, it is demonstrated how simple filtering of this normalised log measure can provide reasonable classification with greatly reduced computation load. 0 IEE, 2001 /EE Proceedings online no. 20010114

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. ML algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. 

To achieve high productivity in a flexible manufacturing system (FMS), an efficient layout arrangement and material flow path design are important due to the large percentage of product cost that is related to material handling. The layout design problem addressed in this paper has departments with fixed shapes and pick-up/drop-off points. It is an open-field type layout with single-loop directed flow path. A two-step heuristic is proposed to solve the problem. It first solves a traditional block layout with directed-loop flow path to minimize material handling costs by using a combined spacefilling curve and simulated annealing algorithm. The second step of the proposed methodology uses the resulting flow sequence and relative positioning information from the first step as input to solve the detailed FMS layout, which includes the spatial coordinates and orientation of each FMS cell. This detailed FMS layout problem is formulated and solved as a mixed integer program. Empirical illustrations show promising results for the proposed methodology in solving real-world type problems.

In this paper, an enhanced ant colony optimization (EACO) is proposed for capacitated vehicle routing problem. The capacitated vehicle routing problem is to service customers with known demands by a homogeneous fleet of fixed capacity vehicles starting from a depot. It plays a major role in the field of logistics and belongs to NP-hard problems. Therefore, it is difficult to solve the capacitated vehicle routing problem directly when solutions increase exponentially with the number of serviced customers. The framework of this paper is to develop an enhanced ant colony optimization for the capacitated vehicle routing problem. It takes the advantages of simulated annealing and ant colony optimization for solving the capacitated vehicle routing problem. In the proposed algorithm, simulated annealing provides a good initial solution for ant colony optimization. Furthermore, an information gain based ant colony optimization is used to ameliorate the search performance. Computational results show that the proposed algorithm is superior to original ant colony optimization and simulated annealing separately reported on fourteen small-scale instances and twenty large-scale instances.

The Capacitated Lot Sizing Problem (CLSP) consists of planning the lot sizes of multiple items over a planning horizon with the objective of minimizing setup and inventory holding costs. In each period that an item is produced a setup cost is incurred. Capacity is limited and ...

A simple, stochastic model is developed of an asexual biological population that is undergoing natural selection. It is then observed that the size of the population, like the temperature parameter in the simulated annealing algorithm, is a measure of the amount of randomness to be allowed in the system. Exploiting the formal analogy between the two processes, it is shown that the distribution of different types of organisms in the population model converges to a stationary distribution if the population is growing more slowly than O(ln t) ("annealing"), but can fail to converge at all if the population is growing faster than O(ln t) ("quenching"). The results may be related to the "historical accidents" that permeate biological structures.

We apply the simulated annealing as a combinatorial optimization heuristic in some multidimensional scaling (MDS) contexts for the minimization of Stress: metric MDS, MDS with restrictions in the configuration and INDSCAL parameter estimation. The application of this technique is based on a discretization of the representation space by a grid. Results obtained are compared to those of usual well-known algorithms and are shown to be better in most of the cases.

In this paper, we propose a new study of a stochastic lognormal diffusion process (SLDP), with three parameters, which can be considered as an extension of the bi-parametric lognormal process with the addition of a threshold parameter. From the Kolmogorov equation, we obtain the probability density function and the moments of this process. The statistical inference of the parameter is studied by considering discrete sampling of the sample paths of the model and then using the maximum likelihood (ML) method. The estimation of the threshold parameter requires the solution of a nonlinear equation. To do so, we propose two methods: the classical Newton-Raphson (NR) method and one based on simulated annealing (SA). This methodology is applied to an example with simulated data corresponding to the process with known parameters. From this, we obtain the estimators of the parameters by both methods (NR and SA). Finally, the methodology studied is applied to a real case concerning the mean age of males in Spain at the date of their first wedding.

The goal of this study is to obtain globally optimum shapes for two-dimensional structures subject to quasi-static loads and restraints. For this purpose a technique is proposed, using which the volume (or weight) of a structure can be minimized. The emphasis is on how one can define the shape precisely, and find a shape that accurately reflects the globally optimum shape.

As design constraints, stresses developed in the structure should not exceed the maximum allowable stress, and connectivity of the structure should not be lost during shape changes. Optimization is achieved by a stochastic search algorithm called direct simulated annealing (DSA), which seeks the global minimum through randomly generated configurations. In order to obtain random configurations, a boundary variation technique is used. In this technique, a set of key points is chosen and connected by cubic splines to describe the boundary of the structure. Whenever the positions of the key points are changed in random directions, a new shape is obtained. Thus, coordinates of the key points serve as design variables. In order to apply the optimization procedure, a general computer code was developed using ANSYS Parametric Design Language. A number of cases were examined to test its effectiveness. The results show that this technique can be applied to two-dimensional shape optimization problems with high reliability even for cases where the entire free boundary is allowed to vary.

In the past, many methods have been proposed for the inference of probabilistic and non-probabilistic finite state automata from positive examples of their behaviour. In this paper, we introduce a search method guided by the reformation-theoretic Minimum Message Length principle to infer Probabilistic Finite State Automata (PFSA). ~ The method is a beam search technique that searches for the best PFSA that accounts {or a given dataset. Results of testing this method against some earlier algorithms are presented. A simulated annealing version of the beam search algorithm is also described as ongoing research in the area.

In this paper we apply a heuristic method based on artificial neural networks in order to trace out the efficient frontier associated to the portfolio selection problem. We consider a generalization of the standard Markowitz mean-variance model which includes cardinality and bounding constraints. These constraints ensure the investment in a given number of different assets and limit the amount of capital to be invested in each asset. We present some experimental results obtained with the neural network heuristic and we compare them to those obtained with three previous heuristic methods.

I n this paper, we propose and operationalize a new method for optimizing shelf arrangements. We show that there are important dependencies between the layout of the shelf and stock-keeping unit (SKU) sales and marketing effectiveness. The importance of these dependencies is further shown by the substantive profit gains we obtain with our proposed shelf optimization approach. The basis of our model is a standard sales equation that explains sales using item-specific marketing effect parameters and intercepts. In a Hierarchical Bayes (HB) fashion, we augment this model with a second layer that relates the effect parameters to shelf and SKU descriptors. We also take into account potential endogeneity of facings. After estimating the parameters of the two-level model using Bayesian methodology, we carefully investigate the dependencies of SKU sales and SKU marketing effectiveness on the shelf layout. Next, we search for the shelf arrangement that maximizes the expected total profit using simulated annealing (SA). We appear to be able to increase profits for all the stores analyzed, and our approach appears to outperform well-known rules of thumb. , and was with the authors 10 months for 2 revisions; processed by Eric Bradlow.Published online in Articles in Advance.

This paper proposes a new method to solve certain classes of systems of multivariate equations over the binary field and its cryptanalytical applications. We show how heuristic optimization methods such as hill climbing algorithms can be relevant to solving systems of multivariate equations. A characteristic of equation systems that may be efficiently solvable by the means of such algorithms is provided. As an example, we investigate equation systems induced by the problem of recovering the internal state of the stream cipher Trivium. We propose an improved variant of the simulated annealing method that seems to be well-suited for this type of system and provide some experimental results.

Specification tests using stochastic bottleneck models of airport congestion investigate whether dominant airlines internalize or ignore self-imposed delays at twenty-seven major US airports. Data on flight times determine the airport's landing and takeoff delays for every minute of operation during peak travel days. Dynamic congestion functions based on stochastic-queuing theory separately identify delays that aircraft experience directly, impose internally on their airline's other aircraft, or impose externally on other airlines. Specification tests largely reject internalization and fail to reject non-internalization by dominant airlines. Optimal pricing should value all time using non-dominant aircraft time values and treat all delays as external.