Simulated Annealing Technique Research Articles

Gene prioritization-based Active Bio-module identification for Bioinformatics

Massive multi-omics data are being used to research cancer pathogenesis at the molecular level as high-throughput sequencing technology advances. Many present approaches frequently fail to detect strongly coupled modules that are intimately associated with cancer. By combining two forms of omics data, a technique to active bio-module identification known as IdeMod is proposed, which employs gene expression and protein-protein interaction networks. IdeMod is a p-step random walk kernel regression model-based gene activity score algorithm that uses the Pareto optimum consensus (POC) method's dominance connections to generate a prioritised list of genes. IdeMod uses the SA GPROX simulated annealing technique to identify the PPI network's most linked and high-priority bio-modules. The techniques RegMod, LEAN, SigMod, ModFinder, and IdeMod were experimentally tested on real-world cervical and BRCA datasets. These findings show that the IdeMod algorithm may identify a densely linked module containing multiple genes that either promote or hinder tumour growth. The BRCA1 gene increases the likelihood of developing hereditary breast cancer associated with BRCA mutations. As a result, the IdeMod technique can be used in conjunction with other tools to detect bio-modules.

Constructing keyed strong S-box with optimized nonlinearity using nondegenerate 2D hyper chaotic map

Abstract In the realm of information security, the nonlinearity of an S-Box is a pivotal attribute for ensuring its effectiveness. S-Boxes randomly generated with chaotic maps, however, often exhibit insufficient nonlinearity. To construct a robust S-Box with enhanced nonlinearity, we have developed a nondegenerate two-dimensional hyper chaotic map (2D-NDHCM). We rigorously evaluated its dynamic properties through bifurcation and phase diagrams, Lyapunov exponents, Kolmogorov entropy, correlation dimension, and randomness tests to assess the map's resilience. Subsequently, we employed the 2D-NDHCM to devise a novel algorithm for constructing and optimizing a keyed strong S-Box, leveraging simulated annealing technique to achieve superior nonlinearity. Finally, We conducted performance analyses of the optimized S-Box using the classic six criteria, and the results confirmed the algorithm's practicality and effectiveness.

Vehicle-UAV Integrated Routing Optimization Problem for Emergency Delivery of Medical Supplies

In recent years, the delivery of medical supplies has faced significant challenges due to natural disasters and recurrent public health emergencies. Addressing the need for improved logistics operations during such crises, this article presents an innovative approach, namely integrating vehicle and unmanned aerial vehicle (UAV) logistics to enhance the efficiency and resilience of medical supply chains. Our study introduces a dual-mode distribution framework which employs the density-based spatial clustering of applications with noise (DBSCAN) algorithm for efficiently clustering demand zones unreachable by conventional vehicles, thereby identifying areas requiring UAV delivery. Furthermore, we categorize the demand for medical supplies into two distinct sets based on vehicle accessibility, optimizing distribution routes via both UAVs and vehicles. Through comparative analysis, our findings reveal that the artificial bee colony (ABC) algorithm significantly outperforms the genetic algorithm in terms of solving efficiency, iteration counts, and delivery speed. However, the ABC algorithm’s tendency toward early local optimization and rapid convergence leads to potential stagnation in local optima. To mitigate this issue, we incorporate a simulated annealing technique into the ABC framework, culminating in a refined optimization approach which successfully overcomes the limitations of premature local optima convergence. The experimental results validate the efficacy of our enhanced algorithm, demonstrating reduced iteration counts, shorter computation times, and substantially improved solution quality over traditional logistic models. The proposed method holds promise for significantly improving the operational efficiency and service quality of the healthcare system’s logistics during critical situations.

A Simulated Annealing Approach to the Scheduling of Battery-Electric Bus Charging

With an increasing adoption of battery-electric bus (BEB) fleets, developing a reliable charging schedule is vital to a successful migration from their fossil fuel counterparts. In this paper, a simulated annealing (SA) implementation is developed for a charge scheduling framework for a fixed-schedule fleet of BEBs that utilizes a proportional battery dynamics model, accounts for multiple charger types, allows partial charging, and further considers the total energy consumed by the schedule as well as peak power use. Two generation mechanisms are implemented for the SA algorithm, denoted as the “quick” and “heuristic” implementations, respectively. The model validity is demonstrated by utilizing a set of routes sampled from the Utah Transit Authority (UTA) and comparing the results against two other models: the BPAP and the Qin-Modified. The results presented show that both SA techniques offer a means of generating operationally feasible schedules quickly while minimizing the cost of operation and considering battery health.

A flowchart for porosity and acoustic impedance mapping using seismic inversion with semi hybrid optimization combining simulated annealing and pattern search techniques

A flowchart for porosity and acoustic impedance mapping using seismic inversion with semi hybrid optimization combining simulated annealing and pattern search techniques

Generating proton-disordered ice configurations using orientational simulated annealing.

We examine an algorithm for the creation of proton-disordered ice cells based on a simulated-annealing (SA) scheme for molecular orientations. Application to defect-free ice Ih, a clathrate-hydrate structure, and a random polycrystalline ice Ih sample demonstrates the SA technique to be effective, attaining maximum HB connectivity using relatively short cooling simulations, thus serving as an alternative method for those cases in which the application of topology-based methods is inhibited.

New orders of 2k factorial designs generated by simulated annealing adapted to optimality criteria

Excessive changes in factor levels can lead to a high cost in practice and hinder the conduction of experiments, in addition to adding a higher computational cost and loss of the orthogonality property, resulting in numerical problems in estimating the parameters of a model. The sequential specification of experimental points, seen as treatments in a 2k factorial design, results in a high-order bias in some factors, which is caused by the accumulation of −1 or +1 signals. This study aimed to propose new designs generated by the simulated annealing technique, respecting the main A-optimal and D-optimal optimality criteria as random execution orders that minimize the order bias. This approach allowed the generation of 24 and 25 factorials, which were compared to the designs in standard order. The simulated annealing technique is a viable method to generate optimal designs with the same efficiency as the usual designs to obtain A-optimal and D-optimal designs with new execution orders, which minimize the effect of order bias relative to standard order designs. Regarding efficiency, the generated designs were precise in the variance of model parameter estimates, similar to the original designs.

Effective optimization of atomic decoration in giant and superstructurally ordered crystals with machine learning.

Crystals with complicated geometry are often observed with mixed chemical occupancy among Wyckoff sites, presenting a unique challenge for accurate atomic modeling. Similar systems possessing exact occupancy on all the sites can exhibit superstructural ordering, dramatically inflating the unit cell size. In this work, a crystal graph convolutional neural network (CGCNN) is used to predict optimal atomic decorations on fixed crystalline geometries. This is achieved with a site permutation search (SPS) optimization algorithm based on Monte Carlo moves combined with simulated annealing and basin-hopping techniques. Our approach relies on the evidence that, for a given chemical composition, a CGCNN estimates the correct energetic ordering of different atomic decorations, as predicted by electronic structure calculations. This provides a suitable energy landscape that can be optimized according to site occupation, allowing the prediction of chemical decoration in crystals exhibiting mixed or disordered occupancy, or superstructural ordering. Verification of the procedure is carried out on several known compounds, including the superstructurally ordered clathrate compound Rb8Ga27Sb16 and vacancy-ordered perovskite Cs2SnI6, neither of which was previously seen during the neural network training. In addition, the critical temperature of an order-disorder phase transition in solid solution CuZn is probed with our SPS routines by sampling site configuration trajectories in the canonical ensemble. This strategy provides an accurate method for determining favorable decoration in complex crystals and analyzing site occupation at unprecedented speed and scale.

Using fuzzy logic based-modeling and simulated annealing approaches to optimize the hardness distribution of 2024 aluminum alloy during precipitation hardening heat treatment cycles

The present study assesses the impact of age hardening parameters, including aging temperature, aging time, and solution time, on the ultimate hardness of heat-treated 2024 aluminum alloys. Using a numerical approach, fuzzy logic systems were utilized as a robust tool to forecast the mechanical characteristics of high copper aluminum solid solutions throughout the age hardening process. In addition, an attempt was made to use a novel simulated annealing technique to determine the optimum hardness and its corresponding process parameters to achieve the highest mechanical properties. Comparing a fuzzy logic model with experimental results obtained from the Brinell hardness test showed the accuracy and confidence of the fuzzy model in representing such properties. The optimization results indicated that the maximum hardness can be obtained with a solution aging temperature of 173.5 °C, an aging time of 19 hours, and a solution time of 58 minutes. Overall, the variation in the experimental peak hardness obtained using the optimized process parameters was the deciding factor in believing the model.

Optimization of Torrefaction Parameters Using Metaheuristic Approach

The probabilistic technique was used to optimize the torrefaction parameters that indirectly influence the yield of end-products obtained through the pyrolysis of biomass. In the same pursuit, pine cones underwent thermal pre-treatment at 210 °C, 220 °C, 230 °C, 240 °C, and 250 °C in the presence of N2 gas with a flowing rate of 0.7 L∙s−1, whereas the duration of the pre-treatment process was 5 min, 10 min, and 15 min at each. To facilitate the processing of pine waste, a muffle furnace was improvised for pilot-scale testing. The thermal process used to carry out torrefaction was quasi-static. The average dynamic head of volatile gases inside the chamber was 1.04 m. The criteria for determining the optimal solution were based on calorific value, solid yield, energy consumption during the pre-treatment process, and ash handling. In absolute terms, time and temperature did not influence the statistical deviation in cellulose and hemicellulose decomposition after thermal pre-treatment. While considering ash content as a primal factor, thermal processing should be conducted for 5 min at 210 °C for the bounded operating conditions, which are similar to the operating conditions obtained experimentally. The optimal solid yield would be obtained if the thermal pre-treatment is performed at 250 °C for 5 min. The solution derived through a simulated annealing technique provided a better convergence with the experimental dataset.

Optimization of ECAP parameters of ZX30 alloy using feature engineering assisted machine learning and response surface approaches

The precise control of equal channel angular pressing (ECAP) deformation parameters is crucial for producing ultrafine grains in structural ZX30-Mg alloy biodegradable components. To address this challenge, this study aims to conduct a comprehensive analysis of the effects of various ECAP parameters on the microstructure, corrosion, and mechanical properties of ZX30 alloy. ZX30 was utilized through a maximum of 4 passes along routes A, Bc, and C, using die angles of 90ᵒ and 120ᵒ, by utilizing machine learning (ML), artificial neural networks (ANNW), response surface methodology (RSM), and simulated annealing (SA) techniques. Experimental investigations revealed the significance of the number of passes in reducing grain size by up to 1.9 µm, consequently enhancing both mechanical and corrosion resistance properties, notably in route Bc. The corrosion rate was enhanced by 97.6 % while the corrosion resistance was increased by 254 % compared to the as-annealed condition. RSM and simulated annealing optimization results closely aligned with experimental findings. The evaluation of each extrusion parameter revealed that the correlation coefficient, as determined by the optimized Gaussian Process Regression model, ranged from 94 % to 97 % for all evaluated responses, and near to unity via ANNW. The overall distribution of mechanical and corrosion measures showed a significant correlation with the number of passes, with route A, Bc, and die angle following closely behind. Interestingly, route Bc had the main impact on the yield strength, followed by the number of passes and die angle. Thus, the combination of statistically and mathematically selected techniques effectively promotes the attainment of optimal properties. Furthermore, the Simulated Annealing (SA) algorithm demonstrated its capability to provide optimal solutions for ECAP deformation parameters. This study presents a promising avenue for improving the accuracy of ECAP-induced structural optimization in ZX30 alloy.

A variance-based optimization for determining ground and excited N-electron wave functions within the doubly occupied configuration interaction scheme.

This work describes optimizations of N-electron system wave functions by means of the simulated annealing technique within the doubly occupied configuration interaction framework. Using that technique, we minimize the energy variance of a Hamiltonian, providing determinations of wave functions corresponding to ground or excited states in an identical manner. The procedure that allows us to determine electronic spectra can be performed using treatments of restricted or unrestricted types. The results found in selected systems, described in terms of energy, spin, and wave function, are analyzed, showing the performance of each method. We also compare these results with those arising from more traditional approaches that minimize the energy, in both restricted and unrestricted versions, and with those obtained from the full configuration interaction treatment.

Optimizing multimodal timetable synchronization of intercity railway and metro for the first service period during holidays

Metro plays a vital role in managing passenger distribution at intercity railway (IR) stations, particularly during holidays when there is a surge in tourist traffic. To efficiently accommodate the high demand for intercity travel, it becomes imperative for metro agencies to optimize holiday timetables. This paper focuses on designing holiday timetables of the first service period for the metro network that connects to an IR station, aiming to enhance multimodal collaboration with IR timetables while ensuring seamless coordination among various metro lines at the network level. A bi-objective model is proposed to maximize the temporal availability of metro network and minimize transfer waiting times for IR passengers traveling in early morning. To solve the model, an improved Artificial Bee Colony algorithm is designed, incorporating adaptive neighbour search and simulated annealing techniques. The effectiveness of the model and algorithm is verified using the Shanghai Metro network and Hongqiao Railway Station. Results indicate a 9.46% increase in the temporal availability of metro network for IR passengers, coupled with a 9.68% reduction in passenger transfer waiting times. Notably, the study reveals that solely advancing operations of the IR-connected metro lines is inefficient. Instead, optimizing train timetables for the entire metro network proves to be a cost-effective approach to enhancing the overall service level of early-morning operations. Furthermore, the study emphasizes the significance of even-numbered train headways in reducing passenger transfer waiting times.

Team orienteering with possible multiple visits: Mathematical model and solution algorithms

This study addresses a new team orienteering problem in which each point can be visited possibly multiple times with decreasing scores. The problem is to determine a set of routes from a starting to a finishing point within an allowed travel time limit for the objective of maximizing the total collected score. A mixed integer programming model is developed to represent the problem mathematically. Then, due to the complexity of the problem, a basic iterated local search (ILS) algorithm is proposed that incorporates the ordinary perturbation and local search operators. Then, it is improved in two ways: (a) an extended ILS algorithm with multi-visit based local search operators and (b) a hybrid ILS algorithm with a simulated annealing technique to permit non-improving moves in the local search step. To test the performance of the ILS algorithms, computational experiments were done on modified benchmark instances under the cases of linear, convex and concave score decreases, and the results show that the hybrid ILS algorithm improves the others significantly and also gives near optimal solutions for small sized test instances. Finally, a case study was done for a naval warship reconnaissance operation, and the results are reported.

Application of new axial power distribution synthesis method using in-core detector signal in digital core protection system

ABSTRACT A new method for the generation of axial power distributions using artificial neural network (ANN) technique and real-time measured planar radial peaking factor, Fxy (hereinafter ‘Live Fxy’) method for digital core protection system of pressurized water reactors is presented. ANN with Simulated Annealing (SA) technique to find global optimum solution was used to calculate core average axial power distribution. In Live Fxy method, axial node-wise Fxys were calculated by multiplying pin/box factors to the measured assembly powers inferred from in-core detector signals without calculating detailed 3D power distributions. The hot pin power distribution for DNBR and LPD was then obtained by multiplying the core average axial power distribution and Fxys at axial nodes. The validation of the method was performed for various core conditions (e.g. core power levels, control rod positions, etc.) of Korean OPR1000 power plant. The result showed a decrease in RMS errors by 2.2% ~ 7.0% (3.6% on average), and the minimum thermal margins for DNBR and LPD were increased by 6.4% and 15.6%, respectively. The application of the method would improve the accuracy of axial power distribution and thermal margin, contributing to the operability and safety of digital core protection system.

COVERING A MAXIMUM NUMBER OF POINTS BY A FIXED NUMBER OF EQUAL DISKS VIA SIMULATED ANNEALING

The presented paper considers the problem of covering a maximum number of n given points in the plane by m equal disks of radius r. A point is covered if it is inside one or more than one disk. The disks need to be placed in the plane in such a way that a maximum number of points are covered. To solve the problem, an objective function, called energy, is introduced in such a way that the greater the covering is, the lower the energy is. Thus, a configuration of disks with minimum energy is a configuration with maximum covering. To find a configuration of disks that minimizes the energy, a stochastic algorithm based on the Monte Carlo simulated annealing technique is proposed. The algorithm overcomes potential local minima, which, as shown in the paper, are quite likely to occur. The computational complexity of the algorithm is O(mn). The algorithm is tested on several cases demonstrating its efficiency in finding global minima of the energy, i.e. configurations with maximum covering.

Semi-Automatic Layout Adaptation for Responsive Multiple-View Visualization Design.

Multiple-view (MV) visualizations have become ubiquitous for visual communication and exploratory data visualization. However, most existing MV visualizations are designed for the desktop, which can be unsuitable for the continuously evolving displays of varying screen sizes. In this article, we present a two-stage adaptation framework that supports the automated retargeting and semi-automated tailoring of a desktop MV visualization for rendering on devices with displays of varying sizes. First, we cast layout retargeting as an optimization problem and propose a simulated annealing technique that can automatically preserve the layout of multiple views. Second, we enable fine-tuning for the visual appearance of each view, using a rule-based auto configuration method complemented with an interactive interface for chart-oriented encoding adjustment. To demonstrate the feasibility and expressivity of our proposed approach, we present a gallery of MV visualizations that have been adapted from the desktop to small displays. We also report the result of a user study comparing visualizations generated using our approach with those by existing methods. The outcome indicates that the participants generally prefer visualizations generated using our approach and find them to be easier to use.

Deep learning-enabled design for tailored mechanical properties of SLM-manufactured metallic lattice structures

The lattice structures obtained by combining repetitive light cellular forms provide superior high strength to weight capabilities compared to monolithic solid bodies. These properties of lattice structures can be further enhanced by improving the design and manufacturing process. However, the process of creating aluminum alloy lattice structures with tailored mechanical properties is still challenging due to the wide range of possible designs and their complex structure-property relationships. This paper proposes a new methodology that uses the deep learning-based 3D Generative Adversarial Network (3DGAN) model to solve this complex engineering problem. Unlike previous studies on deep learning-based lattice design, the dataset obtained through parametric design and Simulated Annealing techniques enables GAN to create new 3D lattice structures with improved mechanical strength properties. The designs generated with the GAN algorithm were produced using Selective Laser Melting Additive Manufacturing (SLM-AM) technology. The mechanical properties of SLM-fabricated (SLMed) AlSi10Mg unit cell samples were examined by a series of compression and impact tests. The results reveal that the lattice configurations generated using the generative model exhibit improved mechanical properties (e.g., a remarkable increase in normalized energy absorption and extension capacities of up to 57% and 26%, respectively). This study shows that designs generated with the 3DGAN model in lattice structures improve the design space by improving their mechanical properties. Moreover, the successful integration of deep learning and SLM-AM opens up new possibilities for creating custom parts with improved strength-to-weight ratios, dimensional accuracy, and mechanical performance.

Site-Specific Aseismic Design of Multi-Storeyed Buildings Using Optimum Tuned Mass Damper Inerter

ABSTRACT The effectiveness of the tuned mass damper inerter (TMDI) in reducing the vibration of building structures under site-specific earthquake excitation is investigated. The site is Guwahati, Assam, which lies in the most severe seismic intensity zone in India. In this paper, the optimization of the TMDI design parameters is carried out for the site-specific input derived for the building site by a modulated Clough-Penzien (C-P) spectrum fitted to the power spectral density function obtained from a strong motion model of the site. All possible topologies of the TMDI, with one terminal of the inerter being connected to the damper mass attached to the top storey of the example buildings, and the other terminal being connected to any other floor, are investigated. The structure-TMDI system is analyzed both in the frequency-domain and in the time-domain. A numerical study is conducted with two example building structures. Optimization of design parameters of the TMDI is carried out using the technique of simulated annealing in the frequency-domain using the site-specific modulated Clough-Penzien spectrum as input. The performance of the optimal TMDI cases is evaluated by subjecting the example structures to 10 site-specific synthetically generated accelerograms. Results indicate that the TMDI is superior to the TMD for all topologies of the TMDI in case of the 3-storieed structure; and for topologies of the TMDI in which the inerter spans more than 4 floors for the 8-storied structure, with improved reductions in top-floor displacement, acceleration, and stroke length.

Evaluation of the optimum safety performance of the nuclear reactor compact grounding system under lightning strikes and ground fault

Abstract The electrical system of a nuclear reactor facility (NRF) must incorporate a grounding grid in order to ensure it functions safely and reliably. Any disturbance in the electrical system affects the nuclear process, so it is, therefore, crucial to estimate the safety performance of the research nuclear reactor facility grounding system. The paper discusses CYMGrd 6.3, which is based on IEEE Standard 80-2013, and the optimization approaches for finding the best grounding grid design for a nuclear reactor facility connected substation of 500/11 kV in the case of a ground fault and lightning strikes. The result shows that the surface, step and touch potentials along the diagonal coordinates of the proposed grounding grid are below the safety limits in case of a ground fault but also results indicate that 100 kA lightning stroke poses a considerable threat to reactor equipment and personnel safety because the measured grid surface potential exceeds the safe ground potential rise of 3.2 kV standard at the striking point in conjunction with the proposed ground grid, so it must be modified. In this paper, three different optimization algorithms are investigated in order to determine the optimal design of the grounding grid with respect to mesh size: gradient method (GM), the genetic algorithm (GA), and simulated annealing (SA). These methods are used to achieve the purpose of obtaining an effective ground grid design. Comparing these techniques of GM and SA is good for quickly locating local minima, but they may fail to identify global solutions. In contrast, a genetic algorithm is often excellent at achieving a global minimum. Also, the utilization of GA, SA, and GM achieve the reduction of the surface potential of a proposed grounding grid by 16 %, 10 %, and 6 % respectively.