Implementation Of Simulated Annealing Research Articles

Application of Neural Network in Seismic Risk Survey

The accuracy of inversion data is determined by the reliability of linear inversion method. Therefore, the study of nonlinear seismic inversion method is a meaningful work. This paper mainly aims at the application of nonlinear neural network inversion method in Fujian Risk Survey, the following work has been accomplished: (1) The theory and algorithm of the conventional multilayer feedforward neural network and some problems in the design of the model are analyzed and summarized. Aiming at the gradient descent function search algorithm in the BP algorithm, a method for global optimization is proposed by combining simulated annealing (global search algorithm) with Conjugate gradient method (local search algorithm) , the principle and implementation of simulated annealing and conjugate gradient algorithm are analyzed in detail. (2) Based on the analysis of conventional multilayer feedforward neural networks, the model structure and basic principle of probabilistic neural networks are studied. The object function search algorithm and its realization are analyzed. The method is applied to the risk elimination prediction for the first time, and the results are satisfactory. (3) The method and principle of inversion of reservoir parameters by using various seismic attribute information and neural network technology are discussed. In order to make full use of the effective information contained in seismic signal, the method of seismic attribute extraction and optimization is studied, the principle and implementation method of attribute optimal ranking by stepwise recursive method and selecting the most important attribute combination by interactive verification method are presented.

A Comprehensive Fault-System Inversion Approach: Methods and Application to NSHM23

ABSTRACT We present updated inversion-based fault-system solutions for the 2023 update to the National Seismic Hazard Model (NSHM23), standardizing earthquake rate model calculations on crustal faults across the western United States. We build upon the inversion methodology used in the Third Uniform California Earthquake Rupture Forecast (UCERF3) to solve for time-independent rates of earthquakes in an interconnected fault system. The updated model explicitly maps out a wide range of fault recurrence and segmentation behavior (epistemic uncertainty), more completely exploring the solution space of viable models beyond those of UCERF3. We also improve the simulated annealing implementation, greatly increasing computational efficiency (and thus inversion convergence), and introduce an adaptive constraint weight calculation algorithm that helps to mediate between competing constraints. Hazard calculations show that ingredient changes (especially fault and deformation models) are the primary driver of hazard changes between NSHM23 and UCERF3. Updates to the inversion methodology are also consequential near faults in which the slip rate in UCERF3 was poorly fit or was satisfied primarily using large multifault ruptures that are now restricted by explicit b-value and segmentation constraints.

SIMULATED ANNEALING ALGORITHM AND IMPLEMENTATION SOFTWARE FOR FABRIC CUTTING PROBLEM

Development of loom technology has significantly increased the efficiency of fabric output in the textile industry. Additionally, preventing the occurrence of defects during the manufacturing process on the fabric is not easy. Therefore, after the production completed, the aim is deciding the cutting location of the product, which has the error map, to increase the first quality product quantity by considering the customer quality parameters. In this article, a decision support system has been developed to help the inspector in the final stage which will also prevent losses. The first of the proposed two algorithms is Simulated Annealing algorithm, which is well known and rendered good results for the different type of problems in the literature, and the other is the K-means method which is frequently used in clustering. In the study, a sample problem is used to explain the adaptation of algorithms to the problem, the results of methods are compared, and design of the experiment is deployed to obtain the best parameter values for the selected algorithm. Finally, the software, which is prepared to use the algorithm in the real production environment, is introduced and the results of the performance analysis are evaluated. The results demonstrated that the developed software is capable of making high ratio first quality fabric decision within seconds.

SampPick: Selection of a Cohort of Subjects Matching a Population HLA Distribution.

Immune responses to therapeutic proteins and peptides can adversely affect their safety and efficacy; consequently, immunogenicity risk-assessments are part of the development, licensure and clinical use of these products. In most cases the development of anti-drug antibodies is mediated by T cells which requires antigen presentation by Major Histocompatibility Complex Class II (MHCII) molecules (also called Human Leucocyte Antigen, HLA in humans). Immune responses to many protein therapeutics are thus HLA-restricted and it is important that the distribution of HLA variants used in the immunogenicity assessments provides adequate coverage of the target population. Due to biases inherent to the collection of samples in a blood bank or donor pool, simple random sampling will not achieve a truly representative sample of the population of interest. To help select a donor cohort we introduce SampPick, an implementation of simulated annealing which optimizes cohort selection to closely match the frequency distribution of a target population or subpopulation. With inputs of a target background frequency distribution for a population and a set of available, HLA-typed donors, the algorithm will iteratively create a cohort of donors of a user selected size that will closely match the target population rather than a random sample. In addition to optimizing the HLA types of donor cohorts, the software presented can be used to optimize donor cohorts for any other biallelic or monoallelic trait.

Gravity Inversion of Talwani Model using Very Fast Simulated Annealing

Global approaches for estimating geophysical model parameters have been proposed by several authors, including their application for gravity interpretation, which is currently limited to simple and fixed geometrical problems. This paper proposes implementation of Very Fast Simulated Annealing (VFSA) in two-dimensional gravity interpretation problems, which are still rarely addressed. The modeling domain was divided into smaller sub-domains and gravity anomaly calculation was carried out based on the Talwani formulation. To improve the uniqueness of the solution of under-determined problems, specific constraints were added in addition to the assumed known symmetry axes. The inversion of VFSA was tested on synthetic data generated by simple models and on previously published real data to evaluate the applicability of the proposed approach to the interpretation of field data.

Artificial intelligence and DOE: an application to school bus routing problems

This paper presents the implementation of simulated annealing (SA) method, an artificial intelligence technique, to solve the optimization problem known as the school bus routing problem (SBRP). A specific challenge in all artificial intelligence optimization techniques is the selection of appropriate value parameters. One contribution of this paper is the implementation of a design of experiments technique to provide statistical support for parameter selection. The SBRP is formulated as a 0–1 integer linear programming model, where the objective function is to minimize the total cost. Because this problem is combinatorial in nature, it is not possible to find exact solutions in an adequate time, calling for the use of an artificial intelligence optimization technique. The proposed technique is SA due to its modeling flexibility and processing speed. To demonstrate the performance of the proposed algorithm, several experiments with real instances were carried out, showing that the metaheuristic algorithm performs better in quality and time than the classic routing method.

Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer

The Fujitsu Digital Annealer (DA) is designed to solve fully connected quadratic unconstrained binary optimization (QUBO) problems. It is implemented on application-specific CMOS hardware and currently solves problems of up to 1024 variables. The DA's algorithm is currently based on simulated annealing; however, it differs from it in its utilization of an efficient parallel-trial scheme and a dynamic escape mechanism. In addition, the DA exploits the massive parallelization that custom application-specific CMOS hardware allows. We compare the performance of the DA to simulated annealing and parallel tempering with isoenergetic cluster moves on two-dimensional and fully connected spin-glass problems with bimodal and Gaussian couplings. These represent the respective limits of sparse versus dense problems, as well as high-degeneracy versus low-degeneracy problems. Our results show that the DA currently exhibits a time-to-solution speedup of roughly two orders of magnitude for fully connected spin-glass problems with bimodal or Gaussian couplings, over the single-core implementations of simulated annealing and parallel tempering Monte Carlo used in this study. The DA does not appear to exhibit a speedup for sparse two-dimensional spin-glass problems, which we explain on theoretical grounds. We also benchmarked an early implementation of the Parallel Tempering DA. Our results suggest an improved scaling over the other algorithms for fully connected problems of average difficulty with bimodal disorder. The next generation of the DA is expected to be able to solve fully connected problems up to 8192 variables in size. This would enable the study of fundamental physics problems and industrial applications that were previously inaccessible using standard computing hardware or special-purpose quantum annealing machines.

Massively Parallel Implementation of IPSA on GPU Architecture for Multi-Criteria Optimization

Current implementation of inverse planning simulated annealing (IPSA) algorithm for clinical uses requires the physicists to manually adjust the input potentials used to minimize the cost function during the optimization process. This workflow produces a single plan which might not encapsulate all the plan goals set forth by the physicians. On the fly adjustment of the potentials are feasible and can be fast but are time-limited due to the patient waiting for treatment and can be dependent on the optimization skill of the user. The purpose of this work is to present a graphics processing unit (GPU) based implementation of IPSA (gIPSA) which can generate multiple plans in parallel for high dose rate (HDR) brachytherapy. Thus a Pareto Front of equally-optimal plans can be generated for each patient from which the user can chose the most suitable option depending on the clinical situation.

Identification of effective implementations of simulated annealing for optimizing thinning schedules for single forest stands

We explored the most effective implementation of simulated annealing (SA) to optimize thinning schedules for single even-aged stands, with respect to the reliability of optimality. To enable foresters to use this implementation in a variety of situations, four yield models were set as benchmarks. We defined 32 implementations, each a different combination of variable control methods, proposal densities, cooling functions, and controlling the width of the proposal density. We tested these implementations using 625 SA parameter sets, resulting from the combination of five values each for initial and final temperature, the number of temperature levels, and proposal density width. We defined an energy function that enables us to predict potentially appropriate ranges of temperature parameters that relate to minimum acceptance rate. We ran each implementation 40 times for each yield model with each parameter set. To evaluate the reliability of SA under optimal parameters, we compared the objective values for the 40 runs using the best parameter set between implementations. To evaluate robustness under suboptimal parameter settings, we compared the variation in the mean objective values for all parameter sets between implementations. The variable control method significantly affected the reliability of SA under optimal parameters. Use of the Cauchy distribution for proposal density improved robustness under suboptimal parameter settings. The widths of proposal densities were better controlled when not taking temperature into account. The effect of cooling-function choice was relatively non-significant. We identified the implementation that appears best for both evaluations, and discussed the adjustment of its parameters.

A computational study of hybrid approaches of metaheuristic algorithms for the cell formation problem

In this paper we solve the 0–1 cell formation problem where the number of cells is fixed a priori and where the objective is to maximize the overall efficiency of a production system by grouping together machines providing service to similar parts into a subsystem (denoted cell). Three different methods are introduced and compared numerically. The first local search method is an implementation of simulated annealing (SA) where the definition of the neighbourhood is specific to the application and requires using a diversification and intensification strategies. The second local search method is an adaptive simulated annealing method where the neighbourhood is selected randomly at each iteration. The procedure is adaptive in the sense that the probability of selecting a neighbourhood is updated during the process. The third method is a hybrid method (HM) of a population-based method and a local search method. To improve the solution obtained with HM, we apply a SA method afterward. The best variants are very efficient to solve the 35 benchmark problems commonly used in the literature.

Guaranteed Discrete Energy Optimization on Large Protein Design Problems.

In Computational Protein Design (CPD), assuming a rigid backbone and amino-acid rotamer library, the problem of finding a sequence with an optimal conformation is NP-hard. In this paper, using Dunbrack's rotamer library and Talaris2014 decomposable energy function, we use an exact deterministic method combining branch and bound, arc consistency, and tree-decomposition to provenly identify the global minimum energy sequence-conformation on full-redesign problems, defining search spaces of size up to 10(234). This is achieved on a single core of a standard computing server, requiring a maximum of 66GB RAM. A variant of the algorithm is able to exhaustively enumerate all sequence-conformations within an energy threshold of the optimum. These proven optimal solutions are then used to evaluate the frequencies and amplitudes, in energy and sequence, at which an existing CPD-dedicated simulated annealing implementation may miss the optimum on these full redesign problems. The probability of finding an optimum drops close to 0 very quickly. In the worst case, despite 1,000 repeats, the annealing algorithm remained more than 1 Rosetta unit away from the optimum, leading to design sequences that could differ from the optimal sequence by more than 30% of their amino acids.

Tabu search for the cyclic bandwidth problem

The Cyclic Bandwidth (CB) problem for graphs consists in labeling the vertices of a guest graph G by distinct vertices of a host cycle Cn (both of order n) in such a way that the maximum distance in the cycle between adjacent vertices in G is minimized. To the best of our knowledge, this is the first research work investigating the use of metaheuristic algorithms for solving this challenging combinatorial optimization problem in the case of general graphs.In this paper a new carefully devised Tabu Search algorithm, called TScb, for finding near-optimal solutions for the CB problem is proposed. Different possibilities for its key components and input parameter values were carefully analyzed and tuned, in order to find the combination of them offering the best quality solutions to the problem at a reasonable computational effort.Extensive experimentation was carried out, using 113 standard benchmark instances, for assessing its performance with respect to a Simulated Annealing (SAcb) implementation. The experimental results show that there exists a statistically significant performance amelioration achieved by TScb with respect to SAcb in 90 out of 113 graphs (79.646%). It was also found that our TScb algorithm attains 56 optimal solutions and establishes new better upper bounds for the other 57 instances. Furthermore, these competitive results were obtained expending reasonable computational times.

Genetic algorithms for batch sizing and production scheduling

This paper proposes three genetic algorithms with different types of crossovers dedicated to solve the problem of short-term scheduling of batch processes. The genetic algorithms suggested are able to determine the amount of batches produced, their production sequence, machinery assignments, and a different batch size for each batch. This is a more complex variation of the batch scheduling problem where genetic algorithms are usually limited to decide the batch production sequence. A manufacturing case study is used to test the performance when minimizing the makespan, in order to compare the proposed genetic algorithms against a simulated annealing implementation, a totally random search, and a simplified genetic algorithm with no crossover (to test the crossovers’ efficiency). The results show that one of the crossovers proposed has the fastest convergence in all scenarios, finding the best solutions in short searches. This crossover is slightly outperformed by the simulated annealing only in extensive searches with more iterations. The other two crossovers suggested have a good performance in certain scenarios, but show poor results in others.

Simulated annealing and tabu search approaches for the Corridor Allocation Problem

In the Corridor Allocation Problem, we are given n facilities to be arranged along a corridor. The arrangements on either side of the corridor should start from a common point on the left end of the corridor. In addition, no space is allowed between two adjacent facilities. The problem is motivated by applications such as the arrangement of rooms in office buildings, hospitals, shopping centers or schools. Tabu search and simulated annealing algorithms are presented to minimize the sum of weighted distances between every pair of facilities. The algorithms are evaluated on several instances of different sizes either randomly generated or available in the literature. Both algorithms reached the optimal (when available) or best-known solutions of the instances with n⩽30. For larger instances with size 42⩽n⩽70, the simulated annealing implementation obtained smaller objective values, while requiring a smaller number of function evaluations.

Optimization of task allocation and priority assignment in hard real-time distributed systems

The complexity and physical distribution of modern active safety, chassis, and powertrain automotive applications requires the use of distributed architectures. Complex functions designed as networks of function blocks exchanging signal information are deployed onto the physical HW and implemented in a SW architecture consisting of a set of tasks and messages. The typical configuration features priority-based scheduling of tasks and messages and imposes end-to-end deadlines. In this work, we present and compare formulations and procedures for the optimization of the task allocation, the signal to message mapping, and the assignment of priorities to tasks and messages in order to meet end-to-end deadline constraints and minimize latencies. Our formulations leverage worst-case response time analysis within a mixed integer linear optimization framework and are compared for performance against a simulated annealing implementation. The methods are applied for evaluation to an automotive case study of complexity comparable to industrial design problems.

Hybridization of Genetic Algorithm with Parallel Implementation of Simulated Annealing for Job Shop Scheduling

Problem statement: The Job Shop Scheduling Problem (JSSP) is observed as one of the most difficult NP-hard, combinatorial problem. The problem consists of determining the most efficient schedule for jobs that are processed on several mac hines. Approach: In this study Genetic Algorithm (GA) is integrated with the parallel version of Sim ulated Annealing Algorithm (SA) is applied to the job shop scheduling problem. The proposed algor ithm is implemented in a distributed environment using Remote Method Invocation concept. The new genetic operator and a parallel simulated annealing algorithm are developed for sol ving job shop scheduling. Results: The implementation is done successfully to examine the convergence and effectiveness of the proposed hybrid algorithm. The JSS problems tested with very well-known benchmark problems, which are considered to measure the quality of prop osed system. Conclusion/Recommendations: The empirical results show that the proposed geneti c algorithm with simulated annealing is quite successful to achieve better solution than the indi vidual genetic or simulated annealing algorithm.

The Simulated Annealing Algorithm and Its Application on Resource-saving Society Construction

Construct the resource-saving society, which not only help to implement the scientific development concept, change the economic growth mode, but also contribute to implement the sustainable development strategy. Evaluation index system construction is part and parcel of building a resource-saving society; a scientific and rational evaluation index system not only can evaluate the resource-saving society construction standard, but also can guide the resource-saving society construction. Based on the analysis of the resource-saving society evaluation status quo, this paper established an evaluation index system including economic, social, environmental and technological, described the optimization ideas, algorithms and implementation of Simulated Annealing(SA). In this paper, the Hebei region as a case, which verified the SA results accuracy compared to the traditional BP network algorithm, and applied to North China, Central China and Northeast China, including 11 provinces and municipalities, the results showed that in line with the actual situation and have certain guiding significance, and the model’s commonality is very good.

A hybrid swarm intelligence algorithm for multiuser scheduling in HSDPA

Multiuser scheduling is an important aspect in the performance optimization of a wireless network since it allows multiple users to access a shared channel efficiently by exploiting multiuser diversity. To perform efficient scheduling, channel state information (CSI) for users is required, and is obtained via their respective feedback channels. In this paper, a more realistic imperfect CSI feedback, in the form of a finite set of Channel Quality Indicator (CQI) values, is assumed as specified in the HSDPA standard. A mathematical model of the problem is developed for use in the optimization process. A hybrid heuristic approach based on particle swarm optimization and simulated annealing is used to solve the problem. Simulation results indicate that the hybrid approach outperforms individual implementations of both simulated annealing and particle swarm optimization.

Global search algorithms in surface structure determination using photoelectron diffraction

Three different algorithms to effect global searches of the variable-parameter hyperspace are compared for application to the determination of surface structure using the technique of scanned-energy mode photoelectron diffraction (PhD). Specifically, a new method not previously used in any surface science methods, the swarm-intelligence-based particle swarm optimisation (PSO) method, is presented and its results compared with implementations of fast simulated annealing (FSA) and a genetic algorithm (GA). These three techniques have been applied to experimental data from three adsorption structures that had previously been solved by standard trial-and-error methods, namely H2O on TiO2(110), SO2 on Ni(111) and CN on Cu(111). The performance of the three algorithms is compared to the results of a purely random sampling of the structural parameter hyperspace. For all three adsorbate systems, the PSO out-performs the other techniques as a fitting routine, although for two of the three systems studied the advantage relative to the GA and random sampling approaches is modest. The implementation of FSA failed to achieve acceptable fits in these tests.

New bounds for binary covering arrays using simulated annealing

Covering arrays (CAs) are combinatorial structures specified as a matrix of N rows and k columns over an alphabet on v symbols such that for each set of t columns (called the strength of the array) every t-tuple of symbols is covered. Recently they have been used to represent interaction test suites for software testing given that they provide economical sized test cases while still preserving important fault detection capabilities. This paper introduces an improved implementation of a simulated annealing algorithm, called ISA, for constructing CAs of strengths three through six over a binary alphabet (i.e., binary CAs). Extensive experimentation is carried out, using 127 well-known benchmark instances, for assessing its performance with respect to an existing simulated annealing implementation, a greedy method, and five state-of-the-art algorithms. The results show that our algorithm attains 104 new bounds and equals the best-known solutions for the other 23 instances consuming reasonable computational time. Furthermore, the implications of using these results as ingredients to recursive constructions are also analyzed.