Simulated Annealing Technique Research Articles

Cost Reduction in Clustering Based Unit Commitment Employing Hybrid Genetic-Simulated Annealing Technique

Fuel cost savings can be obtained by proper commitment of available generating units. This paper describes a new approach to the unit commitment problem through classification of units into various clusters based on hybrid technique of genetic algorithm and simulated annealing. This classification is carried out in order to reduce the overall operating cost and to satisfy the minimum up/down constraints easily. Unit commitment problem is an important optimizing task in daily operational planning of power systems which can be mathematically formulated as a large scale nonlinear mixed-integer minimization problem. A new methodology employing the concept of cluster algorithm called as additive and divisive hierarchical clustering has been employed based on hybrid technique of genetic algorithm and simulated annealing in order to carry out the technique of unit commitment. Proposed methodology involves two individual algorithms. While the load is increasing, additive cluster algorithm has been employed while divisive cluster algorithm is used when the load is decreasing. The technique that has been developed has been tested on system with generating units in range of 10–100 and the superior performance of the technique has been reported through simulation results.

Solution of Second-Order Ordinary Differential Equations via Simulated Annealing

In this paper, we approach the problem of obtaining approximate solution of second-order initial value problems by converting it to an optimization problem. It is assumed that the solution can be approximated by a polynomial. The coefficients of the polynomial are then optimized using simulated annealing technique. Numerical examples with good results show the accuracy of the proposed approach compared with some existing methods.

Task allocation based modified bacterial foraging algorithm for maximising reliability of a heterogeneous distributed system

modified bacterial foraging algorithm for allocation and scheduling tasks on processors and links with high reliability is proposed (MBFA). MBFA ensure high reliability without redundancy of processors or links by using an efficient and simple structure. MBFA is improved by adding an efficient model to deal with the weak solution (i.e., allocation with low reliability). Also a simulated annealing technique is integrated into MBFA in order to strengthen the search of best solution. The efficiency and effectiveness of MBFA are tested using random generated problems. MBFA is tested over the published approaches HPSO and GAA using density parameter (DS), number of nodes (N) and the communication to computation ratio parameter (CCR). The results show the superiority of MBFA over the published approaches in all test cases.

A Simulated Annealing Algorithm for Scheduling Problems

An algorithm using the heuristic technique of Simulated Annealing to solve a scheduling problem is presented, focusing on the scheduling issues. The approximated method is examined together with its key parameters (freezing, tempering, cooling, number of contours to be explored), and the choices made in identifying these parameters are illustrated to generate a good algorithm that efficiently solves the scheduling problem.

Optimization of elasticity of unidirectionnal non-overlapping fiber reinforced materials

The aim of this work is to efficiently select samples of non-overlapping parallel fiber reinforced composites with regard to their elasticity and their fiber distribution in the composite cross-section. The samples were built with the help of the simulated annealing technique according to chosen Radial Distribution Functions. For each sample the fields of local stresses were simulated by finite element method, then homogenized by volume averaging in order to investigate their elastic properties. The effect of RDF shape on elastic properties was quantified. The more the fiber distributions deviate from Poisson’s Law the higher the effective elastic moduli are. A method to select samples of real fiber reinforced composites according to their elasticity is proposed.

Towards to virtual infrastructure allocation on multiple IaaS providers with survivability and reliability requirements

The diversity of services, prices, and geographical footprints has turned the clouds into a complex and heterogeneous environment. Moreover, the survivability and reliability aspects are often disregarded by tenants, eventually resulting in heavy losses due to unavailability of services that are hosted on Virtual Infrastructures (VIs). We present an alternative to improve Vis' survivability and reliability, which considers the use of replicas and the spreading of virtual resources atop providers, regions, and zones. We formulate the VI allocation with survivability and reliability requirements as a mixed integer program, and three strategies to solve the formulation are proposed. First, the binary constraints are relaxed to obtain a Linear Program (LP), and the LP solution is given as input for the simulated annealing technique. Then, two GPU-accelerated algorithms are proposed to speed up the allocation of large-scale scenarios. Simulation with different reliability requests indicate an increasing in survivability without inflating costs.

Towards to virtual infrastructure allocation on multiple IaaS providers with survivability and reliability requirements

The diversity of services, prices, and geographical footprints has turned the clouds into a complex and heterogeneous environment. Moreover, the survivability and reliability aspects are often disregarded by tenants, eventually resulting in heavy losses due to unavailability of services that are hosted on Virtual Infrastructures (VIs). We present an alternative to improve Vis' survivability and reliability, which considers the use of replicas and the spreading of virtual resources atop providers, regions, and zones. We formulate the VI allocation with survivability and reliability requirements as a mixed integer program, and three strategies to solve the formulation are proposed. First, the binary constraints are relaxed to obtain a Linear Program (LP), and the LP solution is given as input for the simulated annealing technique. Then, two GPU-accelerated algorithms are proposed to speed up the allocation of large-scale scenarios. Simulation with different reliability requests indicate an increasing in survivability without inflating costs.

Multiple-class multidimensional knapsack optimisation problem and its solution approaches

The knapsack problem is well known in the field of combinatorial optimisation. In this paper, we propose a novel multiple-class multidimensional knapsack optimisation problem (MCMKOP) in which the aim is to minimise the total cost of the chosen knapsacks created by a set of knapsack classes. This scenario has a host of practical applications. As MCMKOP is a new combinatorial optimisation problem, we analyse its complexity and prove that it is NP-hard. Because of the high computational complexity of MCMKOP, three heuristic approaches are proposed. The first is a genetic algorithm (GA), the second is a specialised simulated annealing (SA) technique, and the third is a hybrid algorithm (HSAGA) that combines the GA and SA approaches to overcome their respective limitations and integrate their advantages. The commercial exact solvers CPLEX and Gurobi were also applied to MCMKOP. The performance of the proposed heuristics, CPLEX, and Gurobi was evaluated using 144 randomly generated test instances. The experimental results show that HSAGA gives better solutions than both the SA and GA methods, and outperformed CPLEX and Gurobi on 122 problems out of 144 test instances. The results of a paired-t test also indicate that HSAGA works considerably better than the other four algorithms on all datasets, with maximum gaps between HSAGA and GA, SA, CPLEX, and Gurobi of 95.96, 90.9, 88.9, and 96.16, respectively.

Recognizing dissimilarities between resilience engineering and EFQM approaches to ensure safety in hospitals

AbstractA common ground between resilience engineering (RE) and European Foundation for Quality Management (EFQM) model is to address safety issues. However, the advent of RE may adversely become inefficient or may be regarded redundant if there is no clear uniqueness with RE. This study aims to achieve a clear distinction between the perspectives of RE and EFQM model to address safety issues, using a questionnaire‐based comparative approach, in a large hospital. The paper contributes to the scientific field by enabling the comparison using an integrated approach of fuzzy analytical hierarchy process (AHP), simulated annealing (SA), and clustering techniques. Fuzzy AHP provides assessment scores of RE and EFQM. The SA identifies the RE items having the least correlation with the EFQM ones. Finally, the identified RE items are categorized by k‐means clustering into a set of unique healthcare‐focused RE factors to help healthcare practitioners have a clear understanding of RE concepts and principles.

Derivation of Transferable Pair Potentials and the Calculation of Intrinsic Defect Properties for Xenotime

A new force field has been empirically derived that is transferable across the YPO4, Y2O3, and P2O5 phases, utilizing a reverse Monte Carlo method. This method employs a simulated annealing technique with a logarithmic quench to fit potential parameters to observed crystallographic and mechanical properties, producing a force field suitable for simulating radiation damage events in an atomistic molecular dynamics regime. These potentials are used to investigate the defect properties of xenotime, where a wide range of intrinsic defects including Schottky, Schottky-like, Frenkel pairs, and anti-site defects have been investigated, both at infinite dilution and as defect clusters. A common feature in the lowest energy defect configurations was the presence of polymerized phosphate tetrahedra, forming P2O7 units. The trend in the formation energies for the Frenkel pair defects at infinite dilution was in good agreement with previously published simulations. However, the binding energy associated with the aggr...

Design of Network Coding for Wireless Broadcast and Multicast With Optimal Decoders

This paper considers the design of network coding schemes for reliable wireless broadcast and multicast transmissions, in which the same packet is broadcast to a group of receivers. Network coding across multiple broadcasted packets is employed to generate redundant packets for the broadcast retransmissions so that the lost packets can be recovered. It is assumed that optimal decoders are employed at the receivers and the focus is on the design of short block codes with small numbers of redundant bits. To this end, use if first made of the residual graph representation to calculate the error probability of the optimal decoder. Then two code design schemes are proposed to minimize the error probability, including a low-complexity deterministic greedy code design algorithm as well as a stochastic code construction algorithm inspired by the simulated annealing technique. Extensive simulation studies have been carried out to assess the performance of the proposed schemes. It is seen that for a given number of retransmissions, the proposed network coding schemes can considerably increase the average number of recovered packages per user at the receivers and thereby improve the spectral efficiency over traditional coding methods.

Optimum Groundwater Pumping Rate in Safwan-Zubair Area, Basrah Province, Using Simulated Annealing Technique

Safwan-Zubair area is located in the south –west part of Basrah Province in the south of Iraq, this area is involved within the Dibdibba plain which is considered as a part of Iraqi Western Desert. Dibdibba formation consists mainly from sand and gravel with some cementing materials like silt and clay, especially in the west of Zubair area. A linked simulation-optimization model for obtaining the optimum pumping rate of groundwater is presented in this paper. Processing Modflow for Windows (PMWIN v.5) is selected to simulate the aquifer behavior being studied. This model is integrated with an optimization model which is based on the simulated annealing (SA) technique. The calibrated values of hydraulic conductivity vary over the range (15-150) m/day; while, the calibrated values of specific yield vary over the range (0.125-0.45). The objective function converges to a maximum value of (0.636925E+8 m3/year). The percentage ratio of inactive wells is 10.9 %, many wells in the study area that occupy incorrect positions, these wells must be turn off for obtaining optimum pumping rate in the study area.

Thermal and mechanical analysis of a sodium-cooled solar receiver operating under a novel heliostat aiming point strategy

The nature in which a solar receiver in a concentrated solar power plant interacts with an accompanying heliostat field plays a significant role in plant performance and economics. An appropriate heat flux distribution should help deliver maximum receiver thermal performance, while minimising mechanical damage – thereby maximising power production and reducing costs. The current work presents an investigation into the thermal performance and mechanical reliability of a sodium-cooled solar receiver operating under heat flux profiles generated by a novel heliostat aiming strategy. A modification of the HFLCAL model is used to generate heat flux profiles for individual heliostats in a representative plant, and simulated annealing optimisation techniques are used to produce a novel heliostat aiming strategy. The importance of giving consideration to receiver limitations under non-uniform thermal boundary conditions in the development of a heliostat aiming strategy is demonstrated in this study, with mathematical optical, thermal, and mechanical models used to complete the analysis. An investigation has been conducted for a point-in-time resulting in maximum thermal loading conditions, with theoretical modelling techniques used to calculate receiver tube temperatures for aiming strategy yielded heat flux profiles, thereby allowing for the determination of heat losses and mechanical reliability through creep-fatigue damage. Results show that the simulated annealing algorithm can significantly improve heat flux homogeneity on the receiver, potentially reducing peak heat flux to less than 10% that of a single aiming point strategy, given an appropriate spillage allowance and aiming point grid size. A satisfactory configuration of spillage allowance and aiming grid size exists so as to supply maximum power to the receiver, while uniformly distributing the incident heat flux in order to meet mechanical reliability requirements. Based on the receiver design and conditions simulated in the analysis, a grid constructed of more than 81 aiming points (receiver area coverage of 32.7%), and an additional spillage allowance of 10% allows the receiver to deliver maximum power output while retaining mechanical durability through a 30 year plant life cycle.

Heuristic Parameter Estimation for a Continuous Fermentation Bioprocess

Zymomonas mobilis continuous fermentation bioprocess has the ability of producing energy from glucose catabolism, which promises a relevant application for biomass conversion into fuel, and therefore it represents an industrial scale production alternative for our country. However, it has demonstrated high complexity regarding the non-linear and non-Gaussian characteristics of its dynamics. Several works have been dealing not only with the bioprocess modeling but also with controller design and implementation. These works have developed state and parameter estimation strategies based on particle filters and Gaussian methods, as well as closing the loop with nonlinear controllers. Nevertheless, there is a need to improve previous parameter estimation results, enabling future design of control strategies for industrial applications. We present a set of heuristics algorithms for the non-linear system parameter estimation evaluated with data from 150 hours of fermentation. Some algorithms such as local search methods, simulated annealing, population heuristics, differential evolution, bacterial chemotaxis and other techniques were tested for the bioprocess. Simulations of the microorganism model and experimental verifications showed the good performance in parameter accuracy and convergence speed of some of the heuristic methods proposed here. Moreover, the reliability and acceptable computational costs of these methods demonstrate that they could also be applied as parameter estimators for other bioprocesses of a similar complexity.

A Hybrid Simulated Annealing/Linear Programming Approach for the Cover Printing Problem

TheNP-hard cover printing problem addressed here consists in determining the number and composition of equal size impression grids, as well as the number of times each grid is printed, in order to fulfill the demand of different book covers at minimum total printing cost. The considered costs come from printing sheets and for composing grids. Thus, to deal with this combinatorial optimization problem we investigated two heuristics: one combines simulated annealing and linear programming techniques and the other is a hybrid of Tabu Search and an ad hoc procedure. Through intensive testing on available instances, these algorithms proved to be superior to previous approaches.

A hybrid symbiotic organisms search and simulated annealing technique applied to efficient design of PID controller for automatic voltage regulator

This article is motivated by incorporating a hybrid symbiotic organisms search and simulated annealing (hSOS-SA) technique into efficient design of PID controller for automatic voltage regulator (AVR). Symbiotic organism search (SOS) algorithm is contemplated first to optimize parameters of PID controller using a new cost function which considers both time-domain and frequency-domain specifications. The excellence of SOS over some state-of-the-art techniques is confirmed through transient response analysis, root locus analysis and bode analysis for the identical AVR system. To fine-tune controller parameters for enhancing the system stability margin further, simulated annealing algorithm is invoked subsequently at the instant SOS has converged. Extensive numerical results computed from time and frequency response specifications affirm the superiority of proposed hSOS-SA algorithm such that after a minimal overshoot, hSOS-SA tuned AVR system settles to the step reference quickly and follows it with the least steady-state error. Such response is found to ensure a better stability margin than that using original SOS and earlier studies. Finally, robustness analysis is realized to verify that the designed controller is robust with regard to parameter uncertainties.

A hybrid solver for protein multiple sequence alignment problem.

In this work, a novel hybrid model called PSOSA for solving multiple sequence alignment (MSA) problem is proposed. The developed approach is a combination between particle swarm optimization (PSO) algorithm and simulated annealing (SA) technique. In our PSOSA approach, PSO is exploited in global search, but it is easily trapped into local optimum and may lead to premature convergence. SA is incorporated as local improvement approach to overcome local optimum problem and intensify the search in local regions to improve solution quality. Numerical results on BAliBASE benchmark have shown the effectiveness of the proposed method and its ability to achieve good quality solutions when compared with those given by other existing methods.

Optimized Solutions for Defect Characterization in 2-D Inverse Eddy Current Testing Problems Using Subregion Finite Element Method

A computational technique is presented in this paper which improves finite element method (FEM) in solving inverse problems. Subregion method is used to select and isolate the area of interest where design parameters for defect shape are updated. An elastic mesh generator is developed in this paper to generate optimal meshes in the selected area to save connectivity matrix until having the most accurate design parameters. Using subregion FEM in solving inverse problems will help in minimizing processing time and memory usage in addition of reducing solution complexity. A 2-D eddy current testing (ECT) problem of detecting and characterizing the location and shape of a defect by separating the defect from entire domain is used to validate the presented subregion FEM algorithm. The elastic mesh generator is investigated to update the preselected design parameters of the defect in each iteration. This meshing technique adds the specialty of using subregion method in inverse problems where elements and nodes numberings are saved inside and outside the defect region. Both the genetic algorithm and simulated annealing optimization techniques are developed to get the accurate defect parameters. The presented subregion FEM results have been verified computationally using conventional FEM. Excellent results of signals agreement and processing time minimization with a factor of 90% with an accuracy of 98% have been achieved. In addition, the presented subregion FEM algorithm has been verified experimentally using aluminum (T6061-T6) and mild steel plate (0.15%–0.30% carbon and Fe) samples. This experiment is carried using an elongated excitation coil in a fixed position mounted on the top of the sample and tunneling magnetoresistive sensor to measure magnetic field. The measured magnetic fields were used as input to the inverse subregion FEM solver and the machined artificial defects were characterized with excellent accuracy.

Nuclear Norm Clustering: a promising alternative method for clustering tasks

Clustering techniques are widely used in many applications. The goal of clustering is to identify patterns or groups of similar objects within a dataset of interest. However, many cluster methods are neither robust nor sensitive to noises and outliers in real data. In this paper, we present Nuclear Norm Clustering (NNC, available at https://sourceforge.net/projects/nnc/), an algorithm that can be used in various fields as a promising alternative to the k-means clustering method. The NNC algorithm requires users to provide a data matrix M and a desired number of cluster K. We employed simulated annealing techniques to choose an optimal label vector that minimizes nuclear norm of the pooled within cluster residual matrix. To evaluate the performance of the NNC algorithm, we compared the performance of both 15 public datasets and 2 genome-wide association studies (GWAS) on psoriasis, comparing our method with other classic methods. The results indicate that NNC method has a competitive performance in terms of F-score on 15 benchmarked public datasets and 2 psoriasis GWAS datasets. So NNC is a promising alternative method for clustering tasks.

Parallel three-dimensional electrical capacitance data imaging using a nonlinear inversion algorithm and [formula omitted] norm-based model regularization

In order to improve image reconstructions, different classes of nonlinear inversion algorithms are developed and used in different research topics like imaging processes in oil industry or the characterization of complex porous media or multiphase flows. These algorithms are able to avoid local minima and to reach more adapted minima of a given misfit function between observed/measured and computed data. Techniques as different as electrical, ultrasound or potential methods, are used. We present here a nonlinear algorithm that allows us to produce permittivity images by using electrical capacitance tomography (ECT). ECT is a non-invasive technique to image non-conductive permittivity distributions and is used in many oil industry imaging applications such as multiphase flows in pipelines, fluidized bed reactors, mixing vessels, and tanks of phase separation. Even if the ECT technique provides low resolution reconstructions, it is cheap, robust and very fast when compared to other imaging tools. In this method one or more rings of electrodes excite a medium to be imaged at high frequencies, and more particularly at frequencies for which a static electrical potential field has fully developed. In many studies of other research groups only one ring of sources is introduced but the reconstruction accuracy was not totally satisfactory due to the 3D nature of the problem to be solved. Instead of using nonlinear stochastic algorithms like the simulated annealing (SA) technique that we optimized in previous studies to image permittivity distributions of granular or solid materials as well as real oil–gas or two-phase flows in 2D cylindrical vessel configurations, we propose here a new ECT inversion tool to image permittivities in a 3D cylindrical configuration. 3D stochastic optimization methods such as SA, neural networks, genetic algorithms can become computationally too prohibitive, and classical local or linear inversion methods excessively smooth images in many cases. Therefore, we propose here a 3D parallel inversion procedure with different numbers of rings and different Lp norms, with1<p⩽2, applied to the model regularization of the misfit function to increase the resolution of the models after inversion. We are able to better reconstruct two-phase and three-phase (oil, gas and solids) mixtures by combining Lp-norm regularizations of the misfit function to minimize and several rings of electrodes. All these algorithms have been implemented in a more general parallel framework TOMOFAST-X designed for multi-physics joint inversion purposes, and could also be used in other fields of research such as larger-scale geophysical exploration for instance.