Simulated Annealing Optimization Technique Research Articles

Coverage Optimization of Eureka Digital Sound Broadcasting Single Frequency Network Using Simulated Annealing and Particle Swarm Optimization

Due to scarcity of bandwidth available for sound broadcasting, Digital sound broadcasting Technology is emerging to be the Technology of resort in sound broadcasting industry towards replacing the analogue sound broadcasting currently dominated by FM Radio. There are many digital sound broadcasting systems being proposed with different performance and bandwidth efficiency. Static delays are artificial delays intentionally introduced at each Transmitter in order to minimize interference in a Single Frequency Network (SFN). In this paper, we have looked at the Terrestrial digital audio broadcasting (T-DAB) system specifically to optimize its final SFN coverage by finding an optimal set of static delays for transmitters. For the sake of simulation, hexagonal model of transmitters operating under Single Frequency Network (SFN) was used. The aim of this study is to maximize SFN coverage by using optimal set of artificial static delays, Particle Swarm Optimization (PSO) have strong ability in finding the global optimistic result while Stimulated Annealing (SA) algorithm has a strong ability to find the local Optimistic result and therefore based on their unique strength, these methods were selected so that our study can have a good comparison in terms of coverage by using both global and local optimistic results. We report the increase of coverage by 1.12% and 2.38% using Simulated Annealing and Particle Swarm Optimization technique respectively.

Chance-Constrained Automated Test Assembly

Classical automated test assembly (ATA) methods assume fixed and known coefficients for the constraints and the objective function. This hypothesis is not true for the estimates of item response theory parameters, which are crucial elements in test assembly classical models. To account for uncertainty in ATA, we propose a chance-constrained version of the maximin ATA model, which allows maximizing the α-quantile of the sampling distribution of the test information function obtained by applying the bootstrap on the item parameter estimation. A heuristic inspired by the simulated annealing optimization technique is implemented to solve the ATA model. The validity of the proposed approach is empirically demonstrated by a simulation study. The applicability is proven by using the real responses to the Trends in International Mathematics and Science Study (TIMSS) 2015 science test.

A Simulated Annealing optimization technique to obtain uniform dose distribution in gamma irradiators

It is important to achieve dose uniformity in product box irradiated by gamma irradiators. To minimize lateral non-uniformity of absorbed dose, quantified as Dose Uniformity Ratio in a particular plane (DURp), activities of source pencils need to be optimized. In present study, Simulated Annealing technique is used for this purpose and the same is described for a Low-Dose 60Co Blood Irradiator. An in-house computer program is developed that calculates DURp and optimizes the activities of source pencils. The study demonstrates that only the primary component of dose is sufficient to determine DURp. In the Simulated Annealing technique, several cooling parameters are tested and the most effective cooling schedule is selected to obtain optimized activity distribution. An improvement of 22% in DURp value is observed after optimization as compared to each source pencil having the same activity. The methodology described in the paper is efficient as optimization of source activities using a trial-and-error approach will be tedious and time consuming. The optimization methodology described in the study is applicable to any irradiator geometry. However, geometry-specific dose calculation approach should be adapted.

Controlling an automatic voltage regulator using a novel Harris hawks and simulated annealing optimization technique

AbstractMaintaining the terminal voltage of a power system is a crucial process and this can be achieved via a system named automatic voltage regulator (AVR). However, an AVR needs an appropriate control method. In this context, this article proposes a novel Harris hawks optimization (HHO) and simulated annealing (SA) technique which can be used for AVR. The proposed optimization technique (HHO‐SA) combines the good exploration feature of HHO with the exceptional local search feature of SA. The HHO‐SA algorithm is introduced as a novel design method to obtain the optimum parameters for proportional + integral + derivative plus second order derivative (PID + DD) controller adopted in the AVR. Time domain objective function of the system is effectively minimized and the best PID + DD parameters are obtained. The analysis of statistical tests, convergence, transient and frequency responses, root locus, and disturbance rejection along with robustness are conducted for verifying the efficiency of the HHO‐SA algorithm. Also, the performance of the HHO‐SA tuned PID + DD controller on AVR is compared with the original HHO tuned PID + DD along with PID, FOPID, and PID + DD controllers that are adjusted by state‐of‐the‐art metaheuristic methods. The practical implementation of the proposed controller is also demonstrated in this work. The extensive simulation results and comparisons with the existing controllers adopting the same set of data demonstrate the superior control performance and good robustness of the HHO‐SA tuned PID + DD controller.

Energy harvesting optimization using 2D car suspension system actuated by a sawtooth speed bump

In this work, an optimal suspension system is proposed to reduce the oscillations/vibrations of the car's body and harvest some of the induced vibration energy. The usual shock absorbers are replaced with energy harvesters capable of not only absorbing vibrations for ride comfort but also regenerating electrical energy for onboard use. To investigate the efficiency of the proposed harvesters, the input to the vehicle wheels is assumed to come from a sawtooth-shaped speed bump or rumble speed strip. Also, given the coupling between the various degrees of freedom of the car (heave, pitch, etc.), a half-car model is adopted in the derivation of the equations of motion. To maximize the amount of energy harvested, the design parameters of the harvesters are obtained using the simulated annealing optimization technique with four objective functions. Many of the design parameters, including magnet size, coil turns, and coil layers, are adjusted during the optimization process. Constant and accelerated motions are considered in this study to maximize the generated electricity index and ride comfort efficiency. The simulation results showed that the optimized harvesters were able to regenerate a significant amount of energy while maintaining an acceptable ride comfort level.

Efficient and trusted autonomous vehicle routing protocol for 6G networks with computational intelligence

The Internet of Things (IoT) and wireless sensors have collaborated with many real-time environments for the collection and processing of physical data. Mobile networks with sixth-generation (6G) technologies provide support for emerging applications using Connected and Autonomous Vehicles (CAV) and observe critical conditions. Although, autonomous vehicle-based routing solutions have presented significant development toward reliable and inter-vehicle communications. However, there are numerous research obstacles in terms of data delivery and transmission latency due to the unpredictable environment and changing states of IoT sensors. Therefore, this work presents an efficient and trusted autonomous vehicle routing protocol using 6G networks, which aims to guarantee high quality of service and data coverage. Firstly, the proposed protocol establishes a routing process using a simulated annealing optimization technique and improves energy optimization between IoT-based vehicles, and under difficult circumstances, it statistically guarantees the optimal solution. Secondly, it provides a risk-aware security system due to reliable session-oriented communication with network edges among connected vehicles and avoids uncertainties in the autonomous system. The proposed protocol is verified using simulations for varying vehicles and varying iterations that indicates a green communication system for the autonomous system with authenticity and system intelligence.

Evaluating sample sizes and design for monitoring and characterizing the spatial variations of potentially toxic elements in the soil

Cost-effective, representative and spatial coverage sampling designs are required to monitor the effects of potentially toxic elements (PTEs) in the soil. This study aims to evaluate the minimum sample sizes and placement of soil sampling designs to monitor and characterize the spatial variation of the PTEs (Cu, Zn, Cd, Cr, Pb, and Ni) in the soils. However, there is no standardized approach for evaluating the optimum soil sample size and monitoring location because of the spatial heterogeneity of PTEs in the soil. As a result, three broad techniques were applied. The first step was to use Global Moran's I and q-statistic values to describe the variability of soil PTEs and select appropriate evaluation methods. Second, using simple random sampling (SRS), ordinary kriging (OK), and Mean of Surface with Non-homogeneity (MSN), we estimated and evaluated soil PTEs in the current soil sampling schemes. Finally, MSN and spatial simulated annealing (SSA) optimization techniques were used to assess the required sample sizes and placements in the existing designs. Method performance was evaluated using a standard error (SE) and a relative standard error of the mean (RSE). Except for Zn and Cd, all PTEs tested showed heterogeneous distributions over the area. The MSN lowered the predicted SE by 79–86 % compared with SRS. The OK approach also outperformed the SRS method regarding mean estimated values of soil PTEs by 42–57 %. After SSA refined the initial design, the predicted SE by MSN of Cr and Zn was lowered by 13 % and 39 %, respectively. The MSN was effective with small sample sizes, reducing sample sizes and surveying costs by 39 % after SSA optimized the existing sample numbers. Thus, integrating various sampling strategies may be efficient for building optimal sample designs to monitor PTEs in the soil.

Coverage area maximization with parallel simulated annealing

This study provides a system that determines where to locate k disks-like services of radius r so that they globally cover as much as possible a region of demand. It is an NP-hard problem with notorious applications in the facility location field when locating multiple warning sirens, cellular towers, radio stations, or pollution sensors covering as much area as possible of a city or geographical region. The region of demand is assumed to be delimited by a general polygonal domain, and the resolution strategy relies on a parallel simulated annealing optimization technique based on a suitable perturbation strategy and a probabilistic estimation of the area of the polygonal region covered by the k disks in O(k2) time. The system provides a good enough location for the disks starting from an arbitrary initial solution with very reasonable running times. The proposal is experimentally tested by visualizing the solutions, analyzing and contrasting their quality, and studying the computational efficiency of the entire strategy.

Sequence recommendations for groups: A dynamic approach to balance preferences

We are living in the age of recommendations: it has been estimated that two-thirds of the films viewed on Netflix come from recommendations while the 35% of Amazon sales regard goods suggested to users. There are many factors to consider when providing a new suggestion: in addition to being useful, it should also be relevant and serendipitous, starting from historical data previously collected. In particular, the notion of context has to be considered since it induces some dynamic aspects in the definition of user preferences. The role of context becomes particularly important when we shift from single (myopic) suggestions to be provided to an individual user, to sequences of recommendations for groups of users. When the preferences of individual users are combined to define the preference of a new ephemeral group, dynamic contextual concerns have to be considered in order to provide the best possible experience and extend the group life, preventing the defection of some members because their preferences are not balanced. In this paper we introduce our proposal for producing sequences of recommendations for groups of users which is based on the Multi-Objective Simulated Annealing optimization technique and takes into account dynamic aspects. Moreover, we propose some strategies for extracting the required dynamic information from log data typically available and present the experimental results of the application of our approach in some real-world case studies.

A multicountry comparison of cryptocurrency vs gold: Portfolio optimization through generalized simulated annealing

The last few years have seen a paradigm shift in the financial sector with the development of cryptocurrencies as an alternative mode of payment as well as an investment scheme. The aim of this study is two-fold. The first is to quantify the volatility of cryptocurrencies in terms of the dynamics of tail-end behavior using different approaches and choose the one with the lowest value-at-risk. The second is to investigate the effect of its inclusion in a portfolio with and without gold, to see if Bitcoin is indeed the “digital gold”. This paper uses the generalized simulated annealing optimization technique to compare portfolios for ten countries across the world. The data provide convincing evidence in favor of the inclusion of Bitcoin in the optimized portfolios. Rolling-window analyses (three-year and five-year) confirm the same. However, for some countries, the empirical pattern suggests that instead of replacing gold from the portfolio, both should be comprised. Our results are robust in terms of the inclusion of non-linear constraints.

Swaption Pricing under Libor Market Model Using Monte-Carlo Method with Simulated Annealing Optimization

The thesis seeks to use simulated annealing optimization to minimize the difference between the value of the libor model volatility and the ones quoted in the market for congruent pricing of a Swaption contract. The simulated annealing optimization technique, being a global minimisation method, would provide accurate parameters that will simulate libor rates that are harmonious with the observed yield curve. This latter feature employed in a Monte-Carlo pricing method would price the Swaption contract fundamentally closer to its market value than other local optimization methods. The SA method starts from an initial point, often random, then searches the neighbourhood of the current solution for the next point. The neighbourhood function search is in accordance with the set probabilistic distribution that will determine the distance between the two solutions. Each solution has a cost value associated with it. The cost function determines the eligibility of the solution by measuring its discrepancy with the set limit. If the discrepancy is larger than the set limit, a new solution is sought. If the discrepancy is still large, the old and new cost value is compared, and the latter is accepted if it’s less than the former or otherwise rejected with a certain probability that is largely dependent on the control mechanism. The method terminates if the cost value attained is equal to the set tolerance level. Different from other heuristic methods that solely base their solution on the iterative improvement of the solution’s cost value, simulated annealing accepts some inferior solutions so as to have a wider search in the design space. The main advantage of the method is the ability to escape local minimum entrapment through the aforementioned acceptance/rejection criteria. The results indicate that the advantageous aspects of the Simulated annealing enable it to outperform the least square non-linear optimization method commonly used in the simulation.

Boron removal from aqueous solutions by chitosan/functionalized-SWCNT-COOH: Development of optimization study using response surface methodology and simulated annealing

Boron contamination in water resources (especially drinking waters and agricultural land) is a major problem for the ecosystem. In this study, a novel synthesized chitosan/functionalized-SWCNT-COOH was prepared to separate boron (as boric acid) from aqueous solutions. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) analysis revealed that SWCNT was dispersed in chitosan homogenously. Moreover, this study has related to the constrained optimization problem with an engineering approach. Response surface method (RSM) with face-centered central composite design (FCCCD) was chosen for maximizing the adsorption capacity as well as determining optimal independent factors such as pH, adsorbent dose, and concentration of boric acid. The optimized response (adsorption capacity) was reached 62.16 mg g−1 under the optimal conditions (98.77 mg L−1 of boric acid concentration, pH of 5.46 and 76 min). The present study has indicated that the synthesized material can be used as an adsorbent for eliminating boric acid from aqueous solutions depending on its high adsorbent capacity to remove boron and has better performance than existing adsorbents. Furthermore, simulated annealing (SA) optimization technique was used to compare the findings of RSM. Moreover, the selected optimization techniques were compared with error functions. The optimal conditions derived from SA were 91.17 mg L−1 of boric acid concentration, pH of 5.86, and 76.17 min. The optimal adsorption capacity of SA was found to be 62.06 mg g−1. These results revealed that the predictions of the two models are very close to each other.

Low Latency Aware Fog Nodes Placement in Internet of Things Service Infrastructure

Fog Computing extends storage and computation resources closer to end-devices. In several cases, the Internet of Things (IoT) applications that are time-sensitive require low response time. Thus, reducing the latency in IoT networks is one of the essential tasks. To this end, fog computing is developed with a motive for the data production and consumption to always be within proximity; therefore, the fog nodes must be placed at the edge of the network, which is near the end devices, such that the latency is minimized. The optimal location selection for fog node placement within a network out of a very large number of possibilities, such as minimize latency, is a challenging problem. So, it is a combinatorial optimization problem. Hard combinatorial optimization problems (NP-hard) involve huge discrete search spaces. The fog node placement problem is an NP-hard problem. NP-hard problems are often addressed by using heuristic methods and approximation algorithms. Combinatorial optimization problems can be viewed as searching for the best element of some set of discrete items; therefore in principle, any metaheuristic can be used to solve them. To resolve this, meta-heuristic-based methods is proposed. We apply the Simulated Annealing (SA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) technique to design fog node placement algorithms. Genetic Algorithm is observed to give better solutions. Since Genetic Algorithm may get stuck in local optima, Hybrid Genetic Algorithm, and Simulated Annealing (GA-SA), Hybrid Genetic Algorithm and Particle Swarm Optimization (GA-PSO) were compared with GA. By extensive simulations, it is observed that hybrid GA-SA-based for node placement algorithm outperforms other baseline algorithms in terms of response time for the IoT applications.

Scheduling Optimization of a Cabinet Refrigerator Incorporating a Phase Change Material to Reduce Its Indirect Environmental Impact

Phase Change Materials (PCMs) incorporated in refrigerators can be used to shift their energy consumption from peak periods, when the electric network energy demand is the highest, to off-peak periods. While PCMs can flatten the energy demand curve, they can achieve economic savings if Time-of-Use (TOU) electricity tariffs are applied. However, the hourly carbon emission factor is not commonly linked to the hourly tariff, and the final CO2 emitted due to the operations of the refrigerator would not be fully optimized. In this work, a method based on the Simulated Annealing optimization technique was proposed to identify the optimal working schedule of a cabinet refrigerator incorporating a PCM to reduce its indirect carbon emissions. Data from countries with different representative carbon intensity profiles were used. The normalized standard deviation and normalized range are the best statistical indexes to predict carbon emission reduction in the proposed solution. These parameters proved that countries with a higher hourly carbon intensity variation (Uruguay, France, Denmark, and Germany) benefit from the application of the algorithm. Cost and carbon emission reduction cannot be maximized simultaneously, and a trade-off is required.

Dosimetric impact of dwell time deviation constraint on inverse brachytherapy treatment planning and comparison with conventional optimization method for interstitial brachytherapy implants.

High-dose rate remote afterloading brachytherapy machine and advanced treatment planning system help in getting optimum dose to tumor and low dose to normal structures. Inverse planning simulated annealing (IPSA) optimization technique has a unique feature of dwell time deviation constraint (DTDC). In this study, six IPSA-based plans having different DTDC values with routinely practiced geometric plus graphical optimization (GO + GrO) have been compared using various dosimetric parameters. For this retrospective study, we have generated IPSA-optimized interstitial brachytherapy plans for ten cancer cervix patients. Routinely practiced GO + GrO-based plans were compared with six different IPSA plans having varying DTDC values from 0.0 to 1.0 using different dosimetric indices. Conformity index and homogeneity index (HI) were higher in GO + GrO plans, compared to IPSA-optimized plans. However, HI of IPSA plans was increasing with increasing DTDC values. High-dose volumes were well controllable using DTDC parameter in IPSA-optimized plans. Dose to the rectum and bladder was smaller for IPSA-optimized plans than GO + GrO plans. One of the benefits of applying DTDC in IPSA-optimized plan is that it reduces high-dose volumes. Another advantage is the reduction in rectum and bladder dose.

Numerical solution of Bagley–Torvik equations using Legendre artificial neural network method

In this article, we have used the Legendre artificial neural network to find the solution of the Bagley–Torvik equation, which is a fractional-order ordinary differential equation. Caputo fractional derivative has been considered throughout the presented work to handle the fractional order differential equation. The training of optimal weights of the network has been carried out using a simulated annealing optimization technique. Here we have presented three examples to exhibit the precision and relevance of the proposed technique with comparison to the other numerical methods with error analysis. The proposed technique is an easy, highly efficient, and robust technique for finding the approximate solution of fractional-order ordinary differential equations.

A time-lapse study using self-potential and electrical resistivity tomography methods for mapping of old mine working across railway-tracks in a part of Raniganj coalfield, India

The first coal mining in India was started in the seventeenth century with an unplanned way in Raniganj coalfield, the coal capital of India. The coalfield was possessed by several small private companies which were nationalized in 1973. A part plan of Chanch/Victoria Area, Victoria West Colliery, BCCL, Raniganj Coalfield, India, indicates an underground coal mine gallery/goaf in the Begunia Coal-seam that connects to a Light Casting Factory. It was established as “Barakar Iron Works” in the year 1881 by the government at that time. It is understood that the approaching gallery was used for coal supply to the “Light Casting Factory” by the mining of “Begunia Coal-Seam”. Mostly, these are uncharted and very poorly documented with scarce mine plans. So, the present study attempts to explore the location, depth, extension, and condition of the old working gallery through time-lapse monitoring using a combined study comprising of Self-Potential (SP) and Electrical Resistivity Tomography (ERT) techniques. Four SP profiles data along a line and thirteen ERT profiles data in four different lines were collected across the expected site with different station/electrode spacing covering different profile length in four seasons, viz., Summer (May 2016), Monsoon (August 2016), Post-Monsoon (October 2016) and Summer (April 2017). SP data were analysed using simulated annealing (SA) optimization technique for the evaluation of model parameters. The ERT data were acquired using Wenner, Dipole–Dipole and Schlumberger arrays and inversion of the combined data set was performed using the 2.5D ZZRESINV inversion software. Prominent negative SP signature with equivalent low resistivity anomaly have been delineated that possibly indicate the presence of the old mine working/mine gallery. However, the overall results of time-lapse study inferred that the ground is stable. All results are corroborated with available lithology and field photographs.

Machine Learning Approach to Model Rock Strength: Prediction and Variable Selection with Aid of Log Data

Comprehensive knowledge and analysis of in situ rock strength and geo-mechanical characteristics of rocks are crucial in hydrocarbon and mineral exploration stage to maximize wellbore performance, maintain wellbore stability, and optimize hydraulic fracturing process. Due to the high cost of laboratory-based measurements of rock mechanics properties, the log-based prediction is a viable option. Nowadays, the machine learning tools are being used for estimation of the in situ rock properties using wireline log data. This paper proposes a machine learning approach for rock strength (uniaxial compressive strength) prediction. The main objectives are to investigate the performance of data-driven predictive model in determining this vital parameter and to select features of predictor log variables in the model. The backpropagation multilayer perception (MLP) artificial neural network (ANN) with Levenberg–Marquardt training algorithm as well as the least squares support vector machine (LS-SVM) with coupled simulated annealing (CSA) optimization technique is employed to develop the dynamic data-driven models. Capturing nonlinear, high dimensional, and complex nature of real field log data, the rock strength models’ performances are evaluated using statistical criteria to ensure concerning the model reliability and accuracy. The model predictions are compared and validated against the measured values as well as the results obtained from existing log-based correlations. Both the MLP-ANN and the CSA-based LS-SVM connectionist strategies are able to predict the rock strength so that there is a very good match between the model results and corresponding measured values. The input log parameters are ranked based on their contributions in prediction performance. The acoustic travel time and gamma ray are found to have the highest relative significance in estimating rock strength. New correlations are also developed to obtain the in situ rock strength of the siliciclastic sedimentary rocks using the most important log parameters such as dynamic sonic slowness, formation electron density, and shalyness effect. The developed correlations can be used to obtain quick estimation of dynamic uniaxial compressive strength profile using wireline logging data, instead of static data from the surface measurements or laboratory data. It is expected that the proposed models and tools will enable oil and gas engineers to better predict rock strength and thus enhance wellbore performance.

Log data-driven model and feature ranking for water saturation prediction using machine learning approach

Log-based reservoir characterization is one of the widely used techniques to estimate the reservoir properties and make decisions about future plans for hydrocarbon production. Use of the machine learning tools is becoming a more accessible approach for predictive model development in oil and gas engineering. The objective of this research is to identify and rank the most contributing log variables for estimation of water saturation using the machine learning tools. The multilayer perception artificial neural network (MLP-ANN) and kernel function-based least-squares support vector machine (LS-SVM) techniques are employed to develop predictive models for water saturation. The model can capture the non-linear behaviors and high-dimensional complex relationships among field log data variables. Based on the prediction performance of the models, the Levenberg-Marquardt algorithm-based MLP-ANN and the radial kernel function-based LS-SVM model optimized with coupled simulated annealing optimization technique were found to perform better compared to other models. The MLP-ANN and radial kernel function-based LS-SVM approaches lead to the same feature ranking of predictor variables. It was found that the significance order (higher to lower) of influential log variables are the true resistivity, bulk density, neutron porosity, photoelectric factor, gamma-ray, and sonic travel time based on their relative contribution to the water saturation. The strategy introduced in this study assists to forecast water saturation with a relatively few number of log variables, and thus, reduces the number of necessary logs to run during exploration stage, considerably lowering the exploration costs.

Structural transformation in $$(\hbox {MgO})_{{{n}}}$$ clusters using a gradient-only strategy and its comparison with a full Hessian-based calculation

In the work, we intend to compare performance of two different techniques for locating transition state and constructing reaction paths for structural transformations in $$(\hbox {MgO})_{{\textit{n}}}$$ cluster with n = 6, 8, 9, 10, 11, 12, 15 and 16. The work has been carried out using an empirical potential energy surface to describe major interactions and using the stochastic optimization technique of simulated annealing to search out the surface. The two different strategies taken up for investigation involve one in which a full evaluation of Hessian matrix and its subsequent diagonalization have been done. The other strategy is based on calculating first derivatives or gradient only and using the information of gradients to evaluate the sign of the first eigenvalue of Hessian. As the second strategy gets rid of the costly process of evaluating second derivatives, it is expected to be a computationally advantageous option. The entire work tries to establish this notion and presents quantitative results in support of the proposition.