Use Of Simulated Annealing Research Articles

Day-Ahead Dynamic Economic Emission Dispatch Considering Photovoltaic Predicted Output Using Sailfish Optimizer and Simulated Annealing

The Indonesian government continues to focuson boosting equitable access to environmentally friendlyelectricity to all levels of Indonesian society. This can be shownthrough the achievement of an electrification ratio of 99.40 percent in the third quarter of 2021 coupled with promisinggrowth in the capacity of Renewable Energy (RE) powerplants. Economic Dispatch (ED) aims to optimize the outputpower of generating units to minimize the total cost of powersystem operations. With the application of ED, the minimumgeneration cost will be obtained for the production of electricpower generated by generating units in an electricity systemwhile still paying attention to constraints. For hybrid plantssuch as the Selayar Solar Power Plant, the challenge toperforming ED is the intermittency of solar energy. So thatbefore the ED is carried out, solar radiation forecasting iscarried out using LSTM. To measure the accuracy offorecasting, RMSE, MSE MAE, and MAPE are used withaverage results of 6.311, 40.11, 4.61, and 4.87. With theapplication of ED, the minimum generation cost is obtained forthe production of electric power generated while stillconsidering the constraints. Through ED, fuel allocation can beoptimized, helping to reduce production costs and carbonemissions, and making a positive contribution toenvironmental sustainability. To improve ED results in thesailfish optimizer, the use of simulated annealing is added sothat the results have lower total cost and emission values thanED without simulated annealing. Rp 128.024 and 398 kg,respectively.

Metaheuristic method for searching quasi-optimal route based on the ant algorithm and annealing simulation

Today, for intelligent computer systems of general and special purpose, the task of finding the optimal route is actual. Currently, there is a problem of lack of efficiency of methods for finding the quasi-optimal route. The object of the research is the process of solving optimization problems of finding a route. The subject of the research is a method for finding a quasi-optimal route based on metaheuristics. The current work increases the efficiency of searching for a quasi-optimal route using a metaheuristic method based on the ant algorithm. To achieve this goal, the work was created a method based on the ant algorithm and simulated annealing for the traveling salesman problem, was formulated the problem of the shortest path in the world of tiles, was developed a method based on the ant algorithm and simulated annealing for the problem of the shortest path in the world of tiles. Advantages of the proposed methods include the following. First, for calculating the probability of an ant moving from the current vertex to other vertices at the initial iterations, the random pheromone level plays the main role, which makes it possible to implement a random search, and at the final iterations, the normalized previous pheromone levelplays the main role, which makes it possible to implement directed search. This is ensured by the use of simulated annealing and increases the accuracy of finding a quasi-optimal route. Second, for calculating the change in the pheromone level at the initial iterations, the pheromone increment plays the main role, which ensures the breadth of the search, and at the final iterations, the previous pheromone level plays the main role, which ensures the convergence of the method. This is ensured by the use of simulated annealing and increases the accuracy of finding a quasi-optimal route. Third, the modification of the ant algorithm by calculating the length of the edges based on the Chebyshev distance, placing all ants in the initial vertex, checking for a dead-end, checking that the target vertex has been reached, and using Moore's neighborhood allows solving problems of the shortest path in the world of tiles. The performed numerical study made it possible to evaluate both methods (for the first method, the root-mean-square error was 0.04, and for the second method it was 0.03). The proposed methods make it possible to expand the area of application of metaheuristics based on the ant algorithm, which is confirmed by its adaptation for the specified optimization problems and contributes to an increase in the efficiency of intelligent computer systems for general and special purposes. The prospects for future research are the study of the proposed methods for a wide class of artificial intelligence problems.

An Enhanced Adaptive k-Nearest Neighbor Classifier Using Simulated Annealing

The k-Nearest Neighbor classifier is a non-complex and widely applied data classification algorithm which does well in real-world applications. The overall classification accuracy of the k-Nearest Neighbor algorithm largely depends on the choice of the number of nearest neighbors(k). The use of a constant k value does not always yield the best solutions especially for real-world datasets with an irregular class and density distribution of data points as it totally ignores the class and density distribution of a test point’s k-environment or neighborhood. A resolution to this problem is to dynamically choose k for each test instance to be classified. However, given a large dataset, it becomes very tasking to maximize the k-Nearest Neighbor performance by tuning k. This work proposes the use of Simulated Annealing, a metaheuristic search algorithm, to select optimal k, thus eliminating the prospect of an exhaustive search for optimal k. The results obtained in four different classification tasks demonstrate a significant improvement in the computational efficiency against the k-Nearest Neighbor methods that perform exhaustive search for k, as accurate nearest neighbors are returned faster for k-Nearest Neighbor classification, thus reducing the computation time.

Appropriate search techniques to estimate Weibull function parameters in a Pinus spp. plantation

The Weibull function, a continuous probability distribution, is widely used for diameter distribution modelling, in which parameter estimation performance is affected by stand attributes and fitting methods. The Weibull cumulative distribution function is nonlinear, and classical fitting methods may provide a not optimal solution. Invoking the use of artificial intelligence by metaheuristics is reasonable for this optimisation task. Therefore, aimed and compared (1) the metaheuristics genetic algorithm and simulated annealing performance over the moment and percentile methods; (2) the hybrid strategy merging the metaheuristics tested and the percentile method and, (3) the metaheuristics fitness functions under basal area and density errors. A long-term experiment in a Pinus taeda stand subjected to crown thinning was used. According to our findings, all methods have a similar performance, independent of the thinning regimes and age. The pattern of the estimated parameters tends to be acceptable, as b increases over time and c increases after thinning. Overall, our findings suggest that methods based on metaheuristics have a higher precision than classical methods for estimating Weibull parameters. According to the classification test, the methods that involved simulated annealing stood out. The hybrid method involving this metaheuristic also stood out, with accurate estimates. Classical methods showed the poorest performance, and we therefore suggest the use of simulated annealing due to its faster processing time and high-quality solution.

СЕГМЕНТАЦІЯ ДВОВИМІРНОГО СИГНАЛУ, ЯКИЙ ПРЕДСТАВЛЯЄ СОБОЮ ЗОБРАЖЕННЯ ТОВАРУ, ЩО ЗАМОВЛЯЄТЬСЯ СПОЖИВАЧЕМ

The subject of study in this article is the means of segmentation of the image of the ordered goods by the consumer online store. The goal is to determine the means of segmentation of the image of a two-dimensional signal. Objectives: analyze existing methods of image segmentation, select metaheuristic clustering with an interactive task of the number of clusters, conduct research. Methods used: segmentation of a two-dimensional signal, which is an image of a product ordered by a consumer in an online store. A meta-heuristic clustering method was implemented with an interactive assignment of the number of clusters. The method is based on the optimization of particle swarm (PSO) and annealing simulation (SA), an adaptive optimization of particle swarm (APSO), which underlies the image segmentation, is proposed. The following results were obtained. The use of simulated annealing in the proposed adaptive optimization of a particle swarm provides: control of the rate of convergence of a given metaheuristic method; research in the early stages of the entire search space, and in the final stages - the focus of the search. To determine the effectiveness of the proposed method, studies have been conducted that prove that the mean square error does not exceed 0.05, which in turn proves the effectiveness of the chosen method in image segmentation. Conclusions. The scientific novelty lies in the fact that to solve the problem of determining the method of image preprocessing, a clustering method with a given number of clusters was used, namely a metaheuristic method based on optimizing the particle swarm (PSO) and simulating annealing (SA) using adaptive particle swarm optimization (APSO), which underlies the image segmentation. The use of simulated annealing in the proposed adaptive optimization of a swarm of particles provides: control of the rate of convergence of a given meta-heuristic method and research in the early stages of the entire search space, and in the final stages the direction of the search. As a result of a numerical study, it was found that the mean square error does not exceed 0.05.

On Automated Generation of Keyboard Layout to Reduce Finger-Travel Distance

The QWERTY keyboard layout can be very inefficient for one-finger typing on virtual keyboards since the letters in many common digrams are placed on opposite sides of the keyboard, resulting in a long finger travel. This paper reports on use of simulated annealing for finding alternate arrangements of the letters of the English alphabet on keyboards with different number of rows, to reduce finger-travel distance for entering text. The use of simulated annealing led to arrangements of the letters on 3 × 10, 4 × 7, and 5 × 6 layouts with a lower weighted sum of finger-travel distances for all digrams (denoted by d) compared to the QWERTY layout (lower by about 40%). The layout with the least value of d among those found in this work is a 5 × 6 layout for which the value of d is only 1.78 key widths compared to 3.31 key widths (the value of d for QWERTY). Alternate automated methods to solve this problem, connections between keyboard layouts and facility layouts, and many new applications of the ideas in this work are discussed.

Learning of interval and general type-2 fuzzy logic systems using simulated annealing: Theory and practice

This paper reports the use of simulated annealing to design more efficient fuzzy logic systems to model problems with associated uncertainties. Simulated annealing is used within this work as a method for learning the best configurations of interval and general type-2 fuzzy logic systems to maximize their modeling ability. The combination of simulated annealing with these models is presented in the modeling of four benchmark problems including real-world problems. The type-2 fuzzy logic system models are compared in their ability to model uncertainties associated with these problems. Issues related to this combination between simulated annealing and fuzzy logic systems, including type-2 fuzzy logic systems, are discussed. The results demonstrate that learning the third dimension in type-2 fuzzy sets with a deterministic defuzzifier can add more capability to modeling than interval type-2 fuzzy logic systems. This finding can be seen as an important advance in type-2 fuzzy logic systems research and should increase the level of interest in the modeling applications of general type-2 fuzzy logic systems, despite their greater computational load.

A novel framework for optimal design of hybrid renewable energy-based autonomous energy systems: A case study for Namin, Iran

An optimization model is developed to determine the most advantageous size of autonomous hybrid photovoltaic/wind turbine/fuel cell, wind turbine/fuel cell and photovoltaic/fuel cell systems for electrification of a remote area involving five homes (1 block) located in Namin, Ardabil, Iran. The model is developed based on three decision variables related to the system renewable energy components: number of storage tanks, total swept area by the rotating turbine blades and total area occupied by the set of photovoltaic panels. The former is an integer decision variable, while the latter two are continuous decision variables. All the components are modeled and an objective function is defined based on minimizing the life cycle cost and satisfying the maximum allowable loss of power supply probability. To determine optimal values of the variables that satisfy the load in the most cost-effective way, the use of simulated annealing and a combination of simulated annealing with harmony search and chaotic search is proposed. The simulation results indicate that the grid-independent hybrid photovoltaic/wind turbine/fuel cell system is the most cost-effective for supplying the block's electrical energy demands and that the simulated annealing-based harmony search algorithm yields more promising results than the other algorithms.

SAGRAD: A Program for Neural Network Training with Simulated Annealing and the Conjugate Gradient Method.

SAGRAD (Simulated Annealing GRADient), a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. Neural network training in SAGRAD is based on a combination of simulated annealing and Møller’s scaled conjugate gradient algorithm, the latter a variation of the traditional conjugate gradient method, better suited for the nonquadratic nature of neural networks. Different aspects of the implementation of the training process in SAGRAD are discussed, such as the efficient computation of gradients and multiplication of vectors by Hessian matrices that are required by Møller’s algorithm; the (re)initialization of weights with simulated annealing required to (re)start Møller’s algorithm the first time and each time thereafter that it shows insufficient progress in reaching a possibly local minimum; and the use of simulated annealing when Møller’s algorithm, after possibly making considerable progress, becomes stuck at a local minimum or flat area of weight space. Outlines of the scaled conjugate gradient algorithm, the simulated annealing procedure and the training process used in SAGRAD are presented together with results from running SAGRAD on two examples of training data.

An Evaluation of the Use of Simulated Annealing to Optimize Thinning Rates for Single Even-Aged Stands

We evaluated the potential of simulated annealing as a reliable method for optimizing thinning rates for single even-aged stands. Four types of yield models were used as benchmark models to examine the algorithm’s versatility. Thinning rate, which was constrained to 0–50% every 5 years at stand ages of 10–45 years, was optimized to maximize the net present value for one fixed rotation term (50 years). The best parameters for the simulated annealing were chosen from 113patterns, using the mean of the net present value from 39 runs to ensure the best performance. We compared the solutions with those from coarse full enumeration to evaluate the method’s reliability and with 39 runs of random search to evaluate its efficiency. In contrast to random search, the best run of simulated annealing for each of the four yield models resulted in a better solution than coarse full enumeration. However, variations in the objective function for two yield models obtained with simulated annealing were significantly larger than those of random search. In conclusion, simulated annealing with optimized parameters is more efficient for optimizing thinning rates than random search. However, it is necessary to execute multiple runs to obtain reliable solutions.

Multi-criteria scheduling of Bag-of-Tasks applications on heterogeneous interlinked clouds with simulated annealing

Cloud computing has spurred the creation of a multitude of services that use the cloud to deliver their products on-demand. Behind it, stand multiple “Cloud Providers” that in the past few years have created data-centers, spread around the world, creating a mesh of distributed resources that can meet high availability and quality of service requirements. The growing number of cloud clients demand reliability, performance and better cost-to-performance ratios. Recently, scientific research has focused on the optimization of interlinked cloud systems, an aim which requires strategies for allocation of resources and distribution of computing tasks between them, while also considering their cost along with any factors that may differentiate them. In this study, we have evaluated the use of simulated annealing and thermodynamic simulated annealing in the scheduling of a dynamic multi-cloud system with virtual machines of heterogeneous performance serving Bag-of-Tasks applications. The scheduling heuristics applied, consider multiple criteria when scheduling said applications and try to optimize both for performance and cost, while also taking into account the heterogeneity of the virtual machines. Simulation results indicate that the use of these heuristics can have a significant impact in performance while maintaining a good cost-performance trade-off.

Use of simulated annealing in standardization and optimization of the acerola wine production

In this study, seven wine samples were prepared varying the amount of pulp of acerola fruits and the sugar content using the simulated annealing technique to obtain the optimal sensory qualities and cost for the wine produced. S. cerevisiae yeast was used in the fermentation process and the sensory attributes were evaluated using a hedonic scale. Acerola wines were classified as sweet, with 11°GL of alcohol concentration and with aroma, taste, and color characteristics of the acerola fruit. The simulated annealing experiments showed that the best conditions were found at mass ratio between 1/7.5-1/6 and total soluble solids between 28.6-29.0 °Brix, from which the sensory acceptance scores of 6.9, 6.8, and 8.8 were obtained for color, aroma, and flavor, respectively, with a production cost 43-45% lower than the cost of traditional wines commercialized in Brazil.

Optimal sterile insect release for area-wide integrated pest management in a density regulated pest population

To determine optimal sterile insect release policies in area-wide integrated pest management is a challenge that users of this pest control method inevitably confront. In this note we provide approximations to best policies of release through the use of simulated annealing. The discrete time model for the population dynamics includes the effects of sterile insect release and density dependence in the pest population. Spatial movement is introduced through integrodifference equations, which allow the use of the stochastic search in cases where movement is described through arbitrary dispersal kernels. As a byproduct of the computations, an assessment of appropriate control zone sizes is possible.

Sparse, mean reverting portfolio selection using simulated annealing

We study the problem of finding sparse, mean reverting portfolios based on multivariate historical time series. After mapping the optimal portfolio selection problem into a generalized eigenvalue problem, we propose a new optimization approach based on the use of simulated annealing. This new method ensures that the cardinality constraint is automatically satisfied in each step of the optimization by embedding the constraint into the iterative neighbor selection function. We empirically demonstrate that the method produces better mean reversion coefficients than other heuristic methods, but also show that this does not necessarily result in higher profits during convergence trading. This implies that more complex objective functions should be developed for the problem, which can also be optimized under cardinality constraints using the proposed approach.

Use of Simulated Annealing for Adaptive Control System

This work deals with using a method of artificial intelligence, mainly the generic probabilistic meta-algorithm can be used in such a difficult task which is analyzed and control of dynamical systems. Simulated annealing (SA) is used in this investigation. The adaptive control system was used in simulations with optimization by Simulated Annealing and the results are presented in graphs.

Use of simulated annealing for the design of multiple repetition time balanced steady‐state free precession imaging

Balanced steady-state free precession is an ultrafast sequence with high signal-to-noise efficiency, but it also generates a strong fat signal which can mask important features. One method of fat suppression is to modify the balanced steady-state free precession spectrum using multiple repetition times to create a wide stopband over the fat frequency. However, with three or more pulse repetition times, the number of parameters creates a vast search space with many local minima of a cost function. We report on the initial results of using simulated annealing to find optimal sequences for two applications of multiple-pulse repetition time balanced steady-state free precession: positive contrast imaging and fat suppression.

Fuzzy Particle Swarm Optimization with Simulated Annealing and Neighborhood Information Communication for Solving TSP

In this paper, an effective hybrid algorithm based on Particle Swarm Optimization (PSO) is proposed for solving the Traveling Salesman Problem (TSP), which is a well-known NP-complete problem. The hybrid algorithm combines the high global search efficiency of fuzzy PSO with the powerful ability to avoid being trapped in local minimum. In the fuzzy PSO system, fuzzy matrices were used to represent the position and velocity of the particles in PSO and the operators in the original PSO position and velocity formulas were redefined. Two strategies were employed in the hybrid algorithm to strengthen the diversity of the particles and to speed up the convergence process. The first strategy is based on Neighborhood Information Communication (NIC) among the particles where a particle absorbs better historical experience of the neighboring particles. This strategy does not depend on the individual experience of the particles only, but also the neighbor sharing information of the current state. The second strategy is the use of Simulated Annealing (SA) which randomizes the search algorithm in a way that allows occasional alterations that worsen the solution in an attempt to increase the probability of escaping local optima. SA is used to slow down the degeneration of the PSO swarm and increase the swarm’s diversity. In SA, a new solution in the neighborhood of the original one is generated by using a designed ? search method. A new solution with fitness worse than the original solution is accepted with a probability that gradually decreases at the late stages of the search process. The hybrid algorithm is examined using a set of benchmark problems from the TSPLIB with various sizes and levels of hardness. Comparative experiments were made between the proposed algorithm and regular fuzzy PSO, SA, and basic ACO. The computational results demonstrate the effectiveness of the proposed algorithm for TSP in terms of the obtained solution quality and convergence speed.

Problem Solving with the TOPAS Macro Language: Corrections and Constraints in Simulated Annealing and Rietveld Refinement

The TOPAS macro language can be a powerful tool for increasing the capabilities of X-ray powder diffraction analysis. New corrections and constraints can be implemented without altering the program's code, allowing for experimentation with new ideas and approaches. Examples are given, exposing the power and flexibility of the macro language to help solving problems with a few lines of code. The use of simulated annealing for structure solution of an organic material from data exhibiting preferential orientation is one example. Another one is about extraction of useful structural information in Rietveld refinement of natural hydrotalcite-group minerals, a problematic case that would normally be regarded as over-parameterized for the data available.

Data mining with a simulated annealing based fuzzy classification system

In this paper, the use of simulated annealing (SA) metaheuristic for constructing a fuzzy classification system is presented. In several previous investigations, the capability of fuzzy systems to solve different kinds of problems has been demonstrated. Simulated annealing based fuzzy classification system (SAFCS), hybridizes the learning capability of SA metaheuristic with the approximate reasoning method of fuzzy systems. The objective of this paper is to illustrate the ability of SA to develop an accurate fuzzy classifier. The use of SA in classification is an attempt to effectively explore and exploit the large search space usually associated with classification problems, and find the optimum set of fuzzy if–then rules. The SAFCS would be capable to extract accurate fuzzy classification rules from input data sets, and applies them to classify new data instances in different predefined groups or classes. Experiments are performed with eight UCI data sets. The results indicate that the proposed SAFCS achieves competitive results in comparison with several well-known classification algorithms.

Algorithm based on simulated annealing for land-use allocation

This article describes the use of simulated annealing for allocation of land units to a set of possible uses on, the basis of their suitability for those uses, and the compactness of the total areas allotted to the same use or kind of use, which are fixed a priori. The results obtained for the Terra Chá district of Galicia (N.W. Spain) using different objective weighting schemes are compared with each other and with those obtained for this district under the same area constraints, using hierarchical optimization, ideal point analysis, and multi-objective land allocation (MOLA) to maximize average use suitability. Inclusion of compactness in the simulated annealing objective function avoids the highly disperse allocations typical of optimizations that ignore this sub-objective.