Simulated Annealing Procedure Research Articles

Poisson image deblurring with frame-based nonconvex regularization

Poisson image deblurring, which aims to restore the latent image from its blurred and noisy observation, has drawn significant attention in image processing. Due to its ill-posed nature, enhancing image quality often involves incorporating a well-defined prior to effectively regularize the ill-posed inverse problem. Building upon the framelet system, we propose a frame-based nonconvex regularization method for Poisson image deblurring. The method is formulated by combining a data-fitting term with the difference of two norms, namely ℓ1 and ℓ2, on the latent image. We solve the optimization problem by combining the difference of convex functions algorithm (DCA) with the alternating direction method of multipliers (ADMM), establishing its convergence. We further employ a simulated annealing procedure and show that proposed algorithm almost certainly converges to a global minimum. Two different approaches are employed to handle the frame-based ℓ1 norm within the ADMM framework. In particular, the frame-based nonconvex regularization method is also considered for the blind Poisson problem. An effective recovery model and its algorithm are presented. Experimental results demonstrate the effectiveness of our proposed models compared with other models in terms of quantitative metrics and visual quality.

Solving the medical student scheduling problem using simulated annealing

AbstractWe consider the medical student scheduling (MSS) problem, which consists of assigning medical students to internships of different disciplines in various hospitals during the academic year to fulfill their educational and clinical training. The MSS problem takes into account, among other constraints and objectives, precedences between disciplines, student preferences, waiting periods, and hospital changes. We developed a local search technique, based on a combination of two different neighborhood relations and guided by a simulated annealing procedure. Our search method has been able to find the optimal solution for all instances of the dataset proposed by Akbarzadeh and Maenhout (Comput Oper Res 129: 105209, 2021b), in a much shorter runtime than their technique. In addition, we propose a novel dataset in order to test our technique on a more challenging ground. For this new dataset, which is publicly available along with our source code for inspection and future comparisons, we report the experimental results and a sensitivity analysis.

A conditioned Latin hypercube sampling design methodology for ground-truthing transient EM resistivity models

Translating 3D resistivity models of the subsurface to relevant hydrogeological parameters such as sediment texture requires applying a rock physics transform that is ideally calibrated using co-located 1D sediment texture logs. It is difficult to select optimal logging sites in the model that span the range of observed resistivity values using expert opinion alone. Instead, we demonstrate a methodology for selecting logging sites using conditioned Latin hypercube sampling (cLHS), a maximally stratified sampling design methodology. cLHS employs simulated annealing to select sampling sites that minimize an objective function by reproducing the statistics of the resistivity model. We implement numerous preprocessing steps to isolate desirable regions within the model for logging, and we use principal component analysis to reduce the dimensionality of the model to aid the convergence of the simulated annealing procedure while preserving the majority of the model variance. Additionally, we impose an additional objective function condition to penalize the selection of sites in the resistivity model that exhibit a high degree of lateral variance. This condition is used to increase the likelihood that the sediment texture logs acquired at the selected sites correlate well with the resistivity model. We tested our approach on a resistivity model obtained using a towed transient EM system in an almond orchard in California’s Central Valley, comparing our selected sets of logging sites against the five cone penetrometer testing logs originally acquired at the site, as well as randomly selected logging sites. The sample sets selected with cLHS were consistently able to reproduce the statistics of the resistivity model and did so more effectively than the true sampling locations or the random sampling locations.

Estimating age and growth parameters for three commercial NE-Atlantic sea cucumbers, Holothuria mammata, H. forskali and H. arguinensis, in a marine protected area

Sea cucumbers, integral components of benthic ecosystems, have become subjects of scientific scrutiny owing to their intricate morphology and ecological importance. Due to increasing demand, several species of these echinoderms have become overexploited. As a consequence, NE-Atlantic species became new targets for the international markets. There is a vital need for comprehensive biological data to establish and enhance holothurian fisheries management. In the absence of such data, there is a risk of ineffective fisheries regulations, particularly for susceptible commercial species in the NE-Atlantic, which could lead to overexploitation. Establishing effective fisheries regulations requires a foundation of fundamental biological information, such as growth rates from target populations. This work aims to determine the growth parameters for three commercial sea cucumber species from the NE-Atlantic, in a marine protected area, Holothuria mammata, H. forskali and H. arguinensis. This presents a challenge, as sea cucumbers lack significant calcified structures for age determination, and assessing size is complicated because of their body size plasticity. Thus, a von Bertalanffy model was fitted to length-frequency data using the ELEFAN method with the simulated annealing procedure. Underwater length measurements of the three sea cucumber species, in a relaxed state, were systematically recorded over a span of 18 months, with measurements taken every 1.5 months. The results provide the growth parameters of each species. Models considering seasonal growth were better fits to the data. The three species had different growth rates and periods of no growth coincided with the reproductive season. Mortality was lower for the species growing slower. This is crucial information to support decision-making processes regarding stock management, such as setting limits to fisheries considering stock condition associated with environmental variability.

Multi-neighborhood simulated annealing for the capacitated facility location problem with customer incompatibilities

We consider the Capacitated Facility Location Problem with Customer Incompatibilities, which is a recently -proposed variant of the classic facility location problem whose distinctive feature is to take into account incompatibilities between customers.We tackle this problem using local search and we propose a combination of neighborhoods and ad hoc techniques to reduce the size of the search space, in order to effectively deal with large instances. The resulting multi-neighborhood approach is guided by a simulated annealing procedure. Our method, suitably tuned in a statistically-principled way, has been able to outperform all previous techniques on the publicly available dataset, on both short and long running times.

(N-Alkyloxalamido)-Amino Acid Amides as the Superior Thixotropic Phase Selective Gelators of Petrol and Diesel Fuels.

(N-Alkyloxalamido)-amino acid amides 9-12 exhibit excellent gelation capacities toward some lipophilic solvents as well as toward the commercial fuels, petrol and diesel. Gelator 10 exhibits an excellent phase-selective gelation (PSG) ability and also possesses the highest gelation capacity toward petrol and diesel known to date, with minimum gelation concentration (MGC) values (%, w/v) as low as 0.012 and 0.015, respectively. The self-assembly motif of 10 in petrol and toluene gel fibres is determined from xerogel X-ray powder diffraction (XRPD) data via the simulated annealing procedure (SA) implemented in the EXPO2014 program and refined using the Rietveld method. The elucidated motif is strongly supported by the NMR (NOE and variable temperature) study of 10 toluene-d8 gel. It is shown that the triple unidirectional hydrogen bonding between gelator molecules involving oxalamide and carboxamide groups, together with their very low solubility, results in the formation of gel fibres of a very high aspect ratio (d = 10-30 nm, l = 0.6-1.3 μm), resulting in the as-yet unprecedented capacity of gelling commercial fuels. Rheological measurements performed at low concentrations of 10 confirmed the strength of the self-assembled network with the desired thixotropic properties that are advantageous for multiple applications. Instantaneous phase-selective gelation was obtained at room temperature through the addition of the 10 solution to the biphasic mixture of diesel and water in which the carrier solvent was congealed along with the diesel phase. The superior gelling properties and PSG ability of 10 may be used for the development of more efficient marine and surface oil spill recovery and waste water treatment technologies as well as the development of safer fuel storage and transport technologies.

A novel machine learning approach for surface roughness quantification and optimization of cast-on-strap lead-antimony alloy via two-point correlation function

Surface roughness has a negative impact on the materials’ lifetime. It accelerates pitting corrosion, increases effective heat transfer, and increases the rate of effective charge loss. However, controlled surface roughness is desirable in many applications. The automotive lead-acid battery is very sensitive to such effects. In our case study, the cast-on-strap machine has the largest effect on the surface roughness of the lead-antimony alloy. In this regard, statistical correlation functions are commonly used as statistical morphological descriptors for heterogeneous correlation functions. Two-point correlation functions are fruitful tools to quantify the microstructure of two-phase material structures. Herein, we demonstrate the use of the two-point correlation function to quantify surface roughness and optimize lead-antimony poles and straps used in the lead-acid battery as a solution to reduce their electrochemical corrosion when used in highly corrosive media. However, we infer that this method can be used in surface roughness mapping in a wide range of applications, such as pipes submerged in seawater as well as laser cutting. The possibility of using information obtained from the two-point correlation function and applying the simulated annealing procedure to optimize the surface micro-irregularities is investigated. The results showed successful surface representation and optimization that agree with the initially proposed hypothesis.

Solving the Single-Row Facility Layout Problem by K-Medoids Memetic Permutation Group

The single-row facility layout problem (SRFLP) is concerned with arranging facilities along a straight line so as to minimize the sum of the products of the flow costs and distances among all facility pairs. SRFLP has rich practical applications and is however NP-hard. In this article, we first investigate a dedicated symmetry-breaking approach based on the permutation group theory for reducing the solution space of SRFLP. Relevant symmetry properties are identified through the alternating group of the original solution space or the corresponding coordinate rotation space. Then, a memetic algorithm is proposed to explore promising search regions regarding the reduced solution space. The memetic algorithm employs a problem-specific crossover operator guided by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -medoids clustering technique to produce meaningful offspring solutions. The algorithm additionally uses a simulated annealing procedure to intensively exploit a given search region and a distance-and-quality-based population management strategy to ensure a reasonable diversity of the population. Experimental results on commonly used benchmark instances and newly introduced large-scale instances with sizes up to 2000 facilities show that the proposed algorithm competes favorably with state-of-the-art SRFLP algorithms. It attains all but one previous best known upper bounds (BKS) and discovers new upper bounds for 33 instances out of the 93 popular benchmark instances.

A genetic-simulated annealing algorithm for stochastic seru scheduling problem with deterioration and learning effect

ABSTRACT As a flexible and effective production mode, seru production has been adopted successfully in electronic industry. In practice, the processing time may be affected by many stochastic factors, such as worker absence, shortage of resources, and so on. This paper focuses on seru scheduling problems with stochastic processing time, and considers the influence of dynamic resource allocation, job deterioration, learning effecteffect, and setup time simultaneously to minimize the makespan. A genetic-simulated annealing algorithm is proposed, in which a simulated annealing procedure is constructed to re-optimize the optimal individual obtained by geneticthe genetic algorithm. Experiment results validate the effectiveness of proposedthe proposed seru scheduling model and genetic-simulated annealing algorithm for solving large-scale cases, and indicate that the stochastic processing time has a great influence on the makespan whichmakespan that can help production manager to makeproduce more consistent results according to the actual situation.

Spin-orbital liquids and insulator-metal transitions on the pyrochlore lattice

The two orbital Hubbard model, with the electrons additionally coupled to a complex magnetic background, arises in the pyrochlore molybdates. The background involves local moments Hund's coupled to the electrons, driving double exchange ferromagnetism, and antiferromagnetic (AF) tendency arising from competing superexchange. The key scales include the Hubbard repulsion and the superexchange, both of which can be tuned in these materials. They control the phase transition from a ferromagnetic metal to a spin-glass metal and then to a spin-glass (Mott) insulator. We provide a comprehensive description of the ground state of this model using an unrestricted Hartree-Fock scheme implemented via a simulated annealing procedure and establish the metal-insulator transition line for varying Hubbard interaction and superexchange. The electrons see an effective disorder, due to orbital frustration, already in the ferromagnetic phase. The disorder is further enhanced by antiferromagnetic coupling and the resulting magnetic disorder. As a result, increasing AF coupling shifts the metal-insulator transition to lower Hubbard interaction and gives it an additional ``Anderson'' character. We provide detailed results on the magnetic and orbital correlations, the density of states, and the optical conductivity.

A SIMULATED ANNEALING PROCEDURE TO MINIMIZE TOTAL WEIGHTED TARDINESS ON A SINGLE MACHINE WITH UNEQUAL JOB RELEASE TIME, SEQUENCE DEPENDENT SETUP TIME, AND MACHINE DOWN TIME

ABSTRACT In this study, we explored a scheduling problem with machine down time, unequal job release time, and sequence dependent setup time to minimize the total weighted tardiness on a single machine. The ATCS rule (Apparent Tardiness Cost with Setup time Rule, Lee et al. 1997) was modified first and then presented with a simulated annealing procedure. Experimental results revealed that the performance of the new simulated annealing procedure outperformed the modified Lin''s simulated annealing method (Lin and Ying, 2007) in the total weighted tardiness reduction. Keywords single machine, simulated annealing, dependent setup time, release time, machine down time.

Spatiotemporal 3D image registration for mesoscale studies of brain development

Comparison of brain samples representing different developmental stages often necessitates registering the samples to common coordinates. Although the available software tools are successful in registering 3D images of adult brains, registration of perinatal brains remains challenging due to rapid growth-dependent morphological changes and variations in developmental pace between animals. To address these challenges, we introduce CORGI (Customizable Object Registration for Groups of Images), an algorithm for the registration of perinatal brains. First, we optimized image preprocessing to increase the algorithm’s sensitivity to mismatches in registered images. Second, we developed an attention-gated simulated annealing procedure capable of focusing on the differences between perinatal brains. Third, we applied classical multidimensional scaling (CMDS) to align (“synchronize”) brain samples in time, accounting for individual development paces. We tested CORGI on 28 samples of whole-mounted perinatal mouse brains (P0–P9) and compared its accuracy with other registration algorithms. Our algorithm offers a runtime of several minutes per brain on a laptop and automates such brain registration tasks as mapping brain data to atlases, comparing experimental groups, and monitoring brain development dynamics.

Controlling morphology in hybrid isotropic/patchy particle assemblies.

Brownian dynamics is used to study self-assembly in a hybrid system of isotropic particles (IPs), combined with anisotropic building blocks that represent special "designer particles." Those are modeled as spherical patchy particles (PPs) with binding only allowed between their patches and IPs. In this study, two types of PPs are considered: Octahedral PPs (Oh-PPs) and Square PPs (Sq-PPs), with octahedral and square arrangements of patches, respectively. The self-assembly is additionally facilitated by the simulated annealing procedure. The resultant structures are characterized by a combination of local correlations in cubatic ordering and a symmetry-specific variation of bond orientation order parameters (SymBOPs). By varying the PP/IP size ratio, we detected a sharp crossover between two distinct morphologies in both types of systems. High symmetry phases, NaCl crystal for Oh-PP and square lattice for Sq-PP, are observed for larger size ratios. For the smaller ones, the dominant morphologies are significantly different, e.g., Oh-PPs form a compact amorphous structure with predominantly face-to-face orientation of neighboring PPs. Unusually, for a morphology without a long-range order, it is still possible to identify well organized coherent clusters of this structure, thanks to the adoption of our SymBOP-based characterization.

Crystal structure and photochemistry of 5-azacytosine: experimental and theoretical study

The crystal structure of 5-azacytosine was proposed by the X-ray diffraction pattern of the compound and the performed Rietveld analysis. The crystal structure solutions were achieved with the simulated annealing procedure. The model molecule for the procedure was taken from the BLYP/aug-cc-pVDZ optimization of the N1(H) oxo-amino tautomer of the compound. The refined unit cell parameters are: a = 12.82916 Å, b = 9.47282 Å, c = 3.66363 Å, and space group P212121. The studied excited-state reaction paths of the H-detachment processes of two oxo-amino tautomers show a possibility for mutual phototransformation of the two oxo-amino tautomers: N1(H) and N5(H). The driven states of this photoreaction are the 1nσ* and 1πσ* excited states.

SimAnMo—A parallelized runtime model generator

In this article, we present the novel features of the recent version of SimAnMo, the Simulated Annealing Modeler. The tool creates models that correlate the size of one input parameter of an application to the corresponding runtime and thus SimAnMo allows predictions for larger input sizes. A focus lies on applications whose runtime grows exponentially in the input parameter size. Such programs are, for example, of high interest for cryptanalysis to analyze practical security of traditional and post-quantum secure schemes. However, SimAnMo also generates reliable models for the widespread case of polynomial runtime behavior and also for the important case of factorial runtime increase. SimAnMo's model generation is based on a parallelized simulated annealing procedure and heuristically minimizes the costs of a model. Those may rely on different quality metrics. Insights into SimAnMo's software design and its usage are provided. We demonstrate the quality of SimAnMo's models for different algorithms from various application fields. We show that our approach also works well on ARM architectures.

Heuristic optimization techniques in abrasive water jet hole making – A case study

Evolutionary algorithms are commonly used for the purpose of modelling manufacturing processes as they provide results with a great amount of efficiency and accuracy. This article presents a comparison between the two of the most popular optimization techniques namely genetic algorithm and simulated annealing. These techniques have been compared step by step by applying to an abrasive water jet drilling process. The process is represented with the help of both the models and the methods/results are compared. Inputs considered for developing the model are water pressure, abrasive flow volume and standoff distance. The responses selected are overcut, circularity and drill rate. In both the optimization models four stages are reported. Each model is characterized by the change in weights of the factors and the results for the individual cases have been analyzed. The results show that the results obtained from both the techniques is very close but the time required for genetic algorithm is considerably less when compared to the simulated annealing procedure.

A Global-Local Approach for Detecting Hotspots in Multiple-Response Regression.

We tackle modelling and inference for variable selection in regression problems with many predictors and many responses. We focus on detecting hotspots, that is, predictors associated with several responses. Such a task is critical in statistical genetics, as hotspot genetic variants shape the architecture of the genome by controlling the expression of many genes and may initiate decisive functional mechanisms underlying disease endpoints. Existing hierarchical regression approaches designed to model hotspots suffer from two limitations: their discrimination of hotspots is sensitive to the choice of top-level scale parameters for the propensity of predictors to be hotspots, and they do not scale to large predictor and response vectors, for example, of dimensions 103-105 in genetic applications. We address these shortcomings by introducing a flexible hierarchical regression framework that is tailored to the detection of hotspots and scalable to the above dimensions. Our proposal implements a fully Bayesian model for hotspots based on the horseshoe shrinkage prior. Its global-local formulation shrinks noise globally and, hence, accommodates the highly sparse nature of genetic analyses while being robust to individual signals, thus leaving the effects of hotspots unshrunk. Inference is carried out using a fast variational algorithm coupled with a novel simulated annealing procedure that allows efficient exploration of multimodal distributions.

Improved energy minimization of iron–carbon systems: on the influence of positioning interstitial atoms

We compare different optimization schemes for the energy minimization in iron–carbon single crystals, where either choosing an initial distribution of interstitial atoms is followed by a conjugate gradient algorithm, or a simulated annealing (SA) procedure is employed. As an alternative to random or so-called single-atom starting configurations, globally optimal interstitial sites with respect to a long-range potential for the carbon–carbon interaction are computed by exact methods of discrete optimization. A comparison of the mechanical properties of the corresponding systems reveals that the positioning scheme can have a large influence. The elastic properties of the crystals for initially randomly distributed carbon atoms show large fluctuations for different initial C interstitial positions. The solution by SA is superior but still causes significant deviations, while using the optimized configurations leads to an increased stiffness of the Fe–C system.

Column generation-based stochastic school bell time and bus scheduling optimization

Given a trip plan (a sequence of bus stops), the integrated school bell time and bus scheduling problem simultaneously optimizes the school bell time and bus routes (the assignment of buses to serve trips at a specific time) while maintaining the minimum level-of-service requirements. The objective is to minimize the total number of buses and the total vehicle time. The previous efforts focused on the deterministic problem. However, uncertainty is unavoidable in a real-world problem. Thus, we consider this integrated problem with stochastic travel times. Two Mixed Integer Programming (MIP) models and a Column Generation-based algorithm are proposed. The algorithm incorporates the merits of the Column Generation, Simulated Annealing, Insertion Algorithm, and Greedy Randomized Adaptive Search Procedure and gains the computational power that the existing methods do not have. The experiments on a real-world problem show that, after the bell time optimization, up to 41% of current buses can be saved with even better service in terms of the higher punctuality and shorter student ride time.

The exponential multi-insertion neighborhood for the vehicle routing problem with unit demands

In this paper, we extend, analyze and evaluate the exponential multi-insertion neighborhood for the vehicle routing problem with unit demands, originally proposed by Angel et al. (2008). In this neighborhood, a neighbor solution is obtained by the removal of a set of mobile nodes from a solution and a subsequent best reinsertion. At first, we examine theoretical properties of the neighborhood, such as its connectivity and the question how many nodes may be chosen as mobile. Furthermore, we prove that finding a best set of mobile nodes is NP-hard. Then, we present a two-stage approach in which first mobile nodes are selected heuristically and reinserted in an optimal way afterwards. Finally, this approach is embedded into a simulated annealing procedure and compared to other heuristics known for the more general vehicle routing problem with arbitrary demands.