Conventional Simulated Annealing Research Articles

A Fast Convergent Simulated Annealing Algorithm for Protein-Folding: Simulated Annealing Outlier FLOODing (SA-OFLOOD) Method

Abstract A Simulated Annealing (SA) algorithm is combined with our recently developed conformational search technique, Outlier FLOODding (OFLOOD) method [J. Comput. Chem. 2015, 36, 97], to avoid a slow convergence in finding a global minimum by the conventional SA (CSA). This method is referred to as SA-OFLOOD and designed for an efficient SA algorithm that robustly detects global minimum states in the protein-folding problems. As a demonstration, SA-OFLOOD was applied to reproduce the folding process of Trp-cage from a fully extended to the native states in implicit solvent. Through the demonstration, SA-OFLOOD successfully predicted the native state of Trp-cage within errors of 0.6 ± 0.2 Å Cα root mean square deviation (RMSD) with only 200 ns simulation time. In contrast, both CSA with sufficiently slow temperature scheduling and canonical MD simulations at room temperature (300 K) failed to find the native state (more than 3.0 Å Cα RMSD), indicating the strength of SA-OFLOOD on the protein-folding problem. As an application to a realistic system, SA-OFLOOD was applied to the folding of Trp-cage in explicit solvent, and the native state was also sampled within Cα RMSD of 1.0 Å through a nanosecond-order simulation time.

Constructing Optimal Coarse-Grained Sites of Huge Biomolecules by Fluctuation Maximization.

Coarse-grained (CG) models are valuable tools for the study of functions of large biomolecules on large length and time scales. The definition of CG representations for huge biomolecules is always a formidable challenge. In this work, we propose a new method called fluctuation maximization coarse-graining (FM-CG) to construct the CG sites of biomolecules. The defined residual in FM-CG converges to a maximal value as the number of CG sites increases, allowing an optimal CG model to be rigorously defined on the basis of the maximum. More importantly, we developed a robust algorithm called stepwise local iterative optimization (SLIO) to accelerate the process of coarse-graining large biomolecules. By means of the efficient SLIO algorithm, the computational cost of coarse-graining large biomolecules is reduced to within the time scale of seconds, which is far lower than that of conventional simulated annealing. The coarse-graining of two huge systems, chaperonin GroEL and lengsin, indicates that our new methods can coarse-grain huge biomolecular systems with up to 10,000 residues within the time scale of minutes. The further parametrization of CG sites derived from FM-CG allows us to construct the corresponding CG models for studies of the functions of huge biomolecular systems.

Multigrid hierarchical simulated annealing method for reconstructing heterogeneous media

A reconstruction methodology based on different-phase-neighbor (DPN) pixel swapping and multigrid hierarchical annealing is presented. The method performs reconstructions by starting at a coarse image and successively refining it. The DPN information is used at each refinement stage to freeze interior pixels of preformed structures. This preserves the large-scale structures in refined images and also reduces the number of pixels to be swapped, thereby resulting in a decrease in the necessary computational time to reach a solution. Compared to conventional single-grid simulated annealing, this method was found to reduce the required computation time to achieve a reconstruction by around a factor of 70-90, with the potential of even higher speedups for larger reconstructions. The method is able to perform medium sized (up to 300(3) voxels) three-dimensional reconstructions with multiple correlation functions in 36-47 h.

Fast simulated annealing with a multivariate Cauchy distribution and the configuration’s initial temperature

We propose a multi-dimensional fast simulated annealing method based on a multivariate Cauchy probability distribution and an initial temperature estimated from the configuration’s variation. While conventional multi-dimensional fast simulated annealing adopts the product of onedimensional random variables generated by a univariate Cauchy distribution, the proposed method generates a random vector from a multivariate Cauchy distribution. In this way, fast simulated annealing for a multi-dimensional problem maintains the same annealing schedule as that for the one-dimensional case. The proposed method also utilizes the initial temperature estimated from the configuration’s variation to generate a candidate state in addition to the conventional initial temperature derived from the variation of the objective function for the acceptance probability. The proposed method is shown not only to guarantee a fast annealing schedule but also to enhance the search capability. The proposed method was tested against the optimization of real-valued functions. We empirically found that the configuration’s initial temperature, together with multivariate Cauchy distribution, is more suitable than the conventional scheme for a fast annealing schedule. Moreover, the proposed method outperforms the conventional one in optimization problems having many variables.

Pragmatic multi-stage simulated annealing for optimal placement of synchrophasor measurement units in smart power grids

Conventional power grids across the globe are reforming to smart power grids with cutting edge technologies in real time monitoring and control methods. Advanced real time monitoring is facilitated by incorporating synchrophasor measurement units such as phasor measurement units (PMUs) to the power grid monitoring system. Several physical and economic constraints limit the deployment of PMUs in smart power grids. This paper proposes a pragmatic multi-stage simulated annealing (PMSSA) methodology for finding the optimal locations in the smart power grid for installing PMUs in conjunction with existing conventional measurement units (CMUs) to achieve a complete observability of the grid. The proposed PMSSA is much faster than the conventional simulated annealing (SA) approach as it utilizes controlled uphill and downhill movements during various stages of optimization. Moreover, the method of integrating practical phasor measurement unit (PMU) placement conditions like PMU channel limits and redundant placement can be easily handled. The efficacy of the proposed methodology has been validated through simulation studies in IEEE standard bus systems and practical regional Indian power grids.

Stochastic reconstruction using multiple correlation functions with different-phase-neighbor-based pixel selection.

A reconstruction methodology based on threshold energy based energy minimization (TA) and different-phase-neighbor (DPN)-based pixel swapping is presented. The TA method uses an energy threshold rather than probabilities as an acceptance criteria for annealing steps. The DPN-based pixel selection method gives priority to pixels which are segregated from clusters instead of random selection. An in-house solver has been developed to obtain two-dimensional reconstructions of heterogeneous two-phase mediums. Compared to conventional simulated annealing with random pixel swapping, the proposed method was found to achieve an optimal structure with up to an order of magnitude reduction in energy. When selecting a threshold tolerance value, the proposed method showed a 50% improvement in convergence time compared to conventional simulated annealing with random pixel swapping. The improved algorithm is used to study the effect of multiple correlation functions during the reconstruction. It was found that a combination of two-point correlation function and lineal path function for both phases results in most accurate reconstructions.

Research on user pairing algorithm for LTE femtocell uplink

In this article, a new simulated annealing based bit error rate pairing scheduling (SA-BPS) algorithm for LTE femtocell uplink virtual multiple input multiple output (V-MIMO) systems is proposed, which uses Round Robin (RR) criterion to decide the first user, and simulated annealing (SA) algorithm with objective function of bit error rate (BER) is suggested to decide the pairing users. The SA-BPS is considered not only to be used for inner layer user pairing within femtocell user equipments (HUEs) to increase spectrum efficiency, but also to be used for cross layer user pairing between HUEs and macrocell user equipments (MUEs) to cancel the cross layer interference from MUEs to HUEs. As a growth control coefficient is used in SA-BPS to control the growth of average BER of users in the pairing group, rapid user BER deterioration can be prevented by adopting a proper coefficient. Simulation results show that, the SA-BPS outperforms RPS, DPS, and the conventional SA algorithm in terms of BER performance, whether used for inner layer user pairing or cross layer user pairing. Also, the mode switching signal to interference ratio (SIR) threshold between inner and cross layer user pairing are obtained by simulation in three cross layer interference power distribution scenarios.

CHAOTIC SIMULATED ANNEALING FOR SOLVING STOCHASTIC DYNAMIC FACILITY LAYOUT PROBLEM

ABSTRACT Stochastic dynamic facility layout (SDFLP) is a Combinatorial Optimization facility layout problem. SDFLP is modeled as QAP where the product demand is random over multiple periods. This paper proposes a novel approach of solving SDFLP using Chaotic Simulated Annealing (CSA). Unlike conventional Simulated Annealing (SA) which uses Gaussian distribution, CSA uses chaotic initialization and chaotic sequences to find the optimal solution. To evaluate the performance of CSA on SDFLP two cooling schedules have been considered - Exponential and Cauchy. The paper reports results of the CSA algorithm used on data sets available in literature for facility size N=12 and time period T=5. To prove the efficacy of the proposed SDFLP solution, CSA is applied on facility size for N=15, 20, 25, 30 and time period T=5 and its results are discussed. Keywords Facility layout; stochastic dynamic facility layout; simulated annealing; cooling schedule; meta-heuristic; chaotic simulated annealing

Optimal sensor placement for mode shapes using improved simulated annealing

Optimal sensor placement techniques play a significant role in enhancing the quality of modal data during the vibration based health monitoring of civil structures, where many degrees of freedom are available despite a limited number of sensors. The literature has shown a shift in the trends for solving such problems, from expansion or elimination approach to the employment of heuristic algorithms. Although these heuristic algorithms are capable of providing a global optimal solution, their greatest drawback is the requirement of high computational effort. Because a highly efficient optimisation method is crucial for better accuracy and wider use, this paper presents an improved simulated annealing (SA) algorithm to solve the sensor placement problem. The algorithm is developed based on the sensor locations' coordinate system to allow for the searching in additional dimensions and to increase SA's random search performance while minimising the computation efforts. The proposed method is tested on a numerical slab model that consists of two hundred sensor location candidates using three types of objective functions; the determinant of the Fisher information matrix (FIM), modal assurance criterion (MAC), and mean square error (MSE) of mode shapes. Detailed study on the effects of the sensor numbers and cooling factors on the performance of the algorithm are also investigated. The results indicate that the proposed method outperforms conventional SA and Genetic Algorithm (GA) in the search for optimal sensor placement.

Optimization of a total internal reflection lens by using a hybrid Taguchi-simulated annealing algorithm

In this paper, we propose a new method for optimization of a total internal reflection (TIR) lens by using a hybrid Taguchi-simulated annealing algorithm. The conventional simulated annealing (SA) algorithm is a method for solving global optimization problems and has also been used in non-imaging systems in recent years. However, the success of SA depends heavily on the annealing schedule and initial parameter setting. In this study, we successfully incorporated the Taguchi method into the SA algorithm. The new hybrid Taguchi-simulated annealing algorithm provides more precise search results and has lower initial parameter dependence.

A New Simulated Annealing Approach for Travelling Salesman Problem

The aim of this study is to improve searching capability of simulated annealing (SA) heuristic through integration of two new neighborhood mechanisms. Due to its ease of formulation, difficulty to solve and various real life applications several Travelling Salesman Problems (TSP) were selected from the literature for the testing of the proposed methods. The proposed methods were also compared to conventional SA with swap neighborhood. The results have shown that the proposed techniques are more effective than conventional SA, both in terms of solution quality and time.

A new adaptive mutation simulated annealing algorithm: application to the study of pure and mixed Pt–Pd clusters

In this article we would like to propose a modified simulated annealing procedure termed as adaptive mutation simulated annealing. In this, the parameters within the optimization scheme is dynamically updated keeping in view the requirements of the parameter values needed for efficient and unequivocal identification of the global minimum in a really rugged multiple minima surface. We apply this procedure to the problem of finding out the global minimum structures of pure Platinum and Palladium clusters as well as the mixed Pt–Pd ones (for cluster sizes upto 60), where the interactions among the constituents of the cluster are defined by the many body empirical Gupta potential. Once the structures are obtained, we try to find out the sizes for which the clusters possess greater stability. These so called magic numbers are compared with existing literature values. To test the efficiency of the proposed procedure we compare the results with conventional simulated annealing. We also analyse in detail and calculate various statistical properties and their evolution during the adaptive mutation simulated annealing run. This in-depth analysis gives an insight into why the current procedure outperforms the conventional simulated annealing.

Health‐state evaluation for aerospace systems

PurposeThe purpose of this paper is to evaluate the health‐states of unit under test (UUT) in aerospace systems by means of unreliable test outcomes, and the evaluation results can provide a guide for engineers to carry out proper maintenance prior to total failure.Design/methodology/approachIn this paper, the authors formulate the health‐state evaluation (HSE) problem with unreliable test outcomes based on Bayes rule, and develop the Lagrangian relaxation and adaptive genetic algorithm (LRAGA) to solve it. The solution scheme can be viewed as a two‐level coordinated solution framework for the HSE problem. At the top level, the Lagrange multipliers are updated by using AGA. At the bottom level, each of the sub‐problems is solved by using AGA.FindingsThe experimental results show that the HSE model appears promising and the LRAGA can obtain the higher quality solution and converge to it at a faster rate than conventional methods (i.e. Lagrangian relaxation (LR), genetic algorithm (GA), simulated annealing (SA) and Lagrangian relaxation and genetic algorithm (LRGA).Research limitations/implicationsThe proposed method for the HSE problem of large‐scale systems which include thousands of faults and tests needs to be verified further.Practical implicationsThe HSE results for aerospace systems can help engineers to carry out a schedule for prompt maintenance prior to UUTs' failure, to avoid the consequences of total failure. It is important to improve aerospace systems' safety, reliability, maintainability, affordability, and reduce life cycle cost.Originality/valueThis paper constructs the HSE model with unreliable test outcomes based on the Bayes rule and proposes a method based on LRAGA to solve the HSE problem.

Electrical Impedance Tomography Reconstruction Through Simulated Annealing using a New Outside-in Heuristic and GPU Parallelization

Electrical Impedance Tomography (EIT) is an imaging technique that attempts to reconstruct the conductivity distribution inside an object from electrical currents and potentials applied and measured at its surface. The EIT reconstruction problem is approached as an optimization problem, where the difference between the simulated and measured distributions must be minimized. This optimization problem can be solved using Simulated Annealing (SA), but at a high computational cost. To reduce the computational load, it is possible to use an incomplete evaluation of the objective function. This algorithm showed to present an outside-in behavior, determining the impedance of the external elements first, similar to a layer striping algorithm. A new outside-in heuristic to make use of this property is proposed. It also presents the impact of using GPU for parallelizing matrix-vector multiplication and triangular solvers. Results with experimental data are presented. The outside-in heuristic showed to be faster when compared to the conventional SA algorithm.

A novel hybrid algorithm for the design of the phase diffractive optical elements for beam shaping

In this paper, a novel hybrid algorithm for the design of a phase diffractive optical elements (PDOE) is proposed. It combines the genetic algorithm (GA) with the transformable scale BFGS (Broyden, Fletcher, Goldfarb, Shanno) algorithm, the penalty function was used in the cost function definition. The novel hybrid algorithm has the global merits of the genetic algorithm as well as the local improvement capabilities of the transformable scale BFGS algorithm. We designed the PDOE using the conventional simulated annealing algorithm and the novel hybrid algorithm. To compare the performance of two algorithms, three indexes of the diffractive efficiency, uniformity error and the signal-to-noise ratio are considered in numerical simulation. The results show that the novel hybrid algorithm has good convergence property and good stability. As an application example, the PDOE was used for the Gaussian beam shaping; high diffractive efficiency, low uniformity error and high signal-to-noise were obtained. The PDOE can be used for high quality beam shaping such as inertial confinement fusion (ICF), excimer laser lithography, fiber coupling laser diode array, laser welding, etc. It shows wide application value.

Hybrid local search algorithm via evolutionary avalanches for spin glass based portfolio selection

Nowadays, various imitations of natural processes are used to solve challenging optimization problems faster and more accurately. Spin glass based optimization, specifically, has shown strong local search capability and parallel processing. However, generally, spin glasses have a low rate of convergence, since they use Monte Carlo simulation techniques such as simulated annealing (SA). Here, we investigate a new hybrid local search method based on spin glass (SG) for using adaptive distributed system capability, extremal optimization (EO) for using evolutionary local search algorithm and SA for escaping from local optimum states and trap to global ones. This algorithm improves the state of spins by selecting and changing the low ordered spins with higher probability; after enough steps, the system reaches a high correlation where almost all spins have reached fitness above a certain threshold and ready to avalanche; this activity potentially makes any configuration accessible. Therefore, avalanches allow escaping from local minima and efficiently exploring the configuration space.As shown in this paper, this strategy can lead to faster rate of convergence and improved performance than conventional SA and EO algorithm. The resulting are then used to solve the portfolio selection multi-objective problem that is a non-deterministic polynomial complete (NPC) problem. This is confirmed by test results of five of the world’s major stock markets, reliability test and phase transition diagram; and finally, the convergence speed is compared to other heuristic methods such as Neural Network (NN), Tabu Search (TS), and Genetic Algorithm (GA).

Optimization of Operation Sequencing in CAPP Using Superhybrid Genetic Algorithms-Simulated Annealing Technique

Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environment. A problem in traditional CAPP system is that the multiple planning tasks are treated in a linear approach. This leads to an overconstrained overall solution space, and the final solution is normally far from optimal or even nonfeasible. A single sequence of operations may not be the best for all the situations in a changing production environment with multiple objectives such as minimizing number of setups, maximizing machine utilization, and minimizing number of tool changes. In general, the problem has combinatorial characteristics and complex precedence relations, which makes the problem more difficult to solve. The main contribution of this work is to develop an intelligent CAPP system for shop-floor use that can be used by an average operator and to produce globally optimized results. In this paper, the feasible sequences of operations are generated based on the precedence cost matrix (PCM) and reward-penalty matrix (REPMAX) using superhybrid genetic algorithms-simulated annealing technique (S-GENSAT), a hybrid metaheuristic. Also, solution space reduction methodology based on PCM and REPMAX upgrades the procedure to superhybridization. In this work, a number of benchmark case studies are considered to demonstrate the feasibility and robustness of the proposed super-hybrid algorithm. This algorithm performs well on all the test problems, exceeding or matching the solution quality of the results reported in the literature. The main contribution of this work focuses on reducing the optimal cost with a lesser computational time along with generation of more alternate optimal feasible sequences. Also, the proposed S-GENSAT integrates solution space reduction, hybridization, trapping out of local minima, robustness, and convergence; it consistently outperformed both a conventional genetic algorithm and a conventional simulated annealing algorithm.

Integration of simulated annealing and genetic algorithm to estimate optimal solutions for minimising surface roughness in end milling Ti-6AL-4V

In this study, simulated annealing (SA) and genetic algorithm (GA) soft computing techniques are integrated to search for a set of optimal cutting conditions value that leads to the minimum value of machining performance. Twointegration systems are proposed; integrated SA–GA-type1 and integrated SA–GA-type2. The considered machining performance is surface roughness (R a) in end milling. The results of this study showed that both of the proposed integration systems managed to estimate the optimal cutting conditions, leading to the minimum value ofmachining performance when compared to the result of real experimental data. The proposed integration systems have also reduced the number of iteration in searching for the optimal solution compared to the conventional GA and conventional SA, respectively. In other words, the time for searching the optimal solution can be made faster by using the integrated SA–GA.

A novel stochastic search method for the solution of economic dispatch problems with non-convex fuel cost functions

This paper develops a Novel Stochastic Search (NSS) method for the solution of economic dispatch problems with non-convex fuel cost functions. The NSS solution procedure consists of three steps, namely Direct Search (DS), Goal Neighborhood Approximation (GNA) and Marginal Cost Dispatch (MCD). The DS step identifies a set of feasible solutions in accordance with prescribed equality and inequality constraints. The GNA step processes those feasible solutions to identify an appropriate direction for searching the global optimal solution. Finally, in the MCD step, the marginal cost of each generating unit is regulated in order to establish the global optimal solution. The proposed NSS scheme is applied to solve three examples systems of increasing complexity. The results are compared to those obtained using the conventional Simulated Annealing (SA), Genetic Algorithm (GA), and Evolutionary Programming (EP) methods. The results demonstrate that the NSS method provides a fast, robust and highly effective scheme for the solution of economic dispatch.

Protein structure predictions by parallel simulated annealing molecular dynamics using genetic crossover

We propose a conformational search method to find a global minimum energy structure for protein systems. The simulated annealing is a powerful method for local conformational search. On the other hand, the genetic crossover can search the global conformational space. Our method incorporates these attractive features of the simulated annealing and genetic crossover. In the previous works, we have been using the Monte Carlo algorithm for simulated annealing. In the present work, we use the molecular dynamics algorithm instead. To examine the effectiveness of our method, we compared our results with those of the normal simulated annealing molecular dynamics simulations by using an α-helical miniprotein. We used genetic two-point crossover here. The conformations, which have lower energy than those obtained from the conventional simulated annealing, were obtained.