As CMOS technology scales down, NoC (Network on Chip) gradually becomes the mainstream of on-chip communication. In this paper we present a methodology to design fault-tolerant routing algorithms for regular direct interconnection networks. It supports fully adaptive routing, does not degrade performance in the absence of faults, and supports a reasonably large number of faults without significantly degrading performance. Consequently, this work examines fault tolerant communication algorithms for use in the Communication Networks including NoC domain. Before two different flooding algorithms, a random walk algorithm and an Intermediate Node Algorithm have been investigated. The first three algorithms have an exceedingly high communication overhead and cause huge congestion in usual traffics. The fourth one which is Intermediate Node algorithm is a static fault-tolerant algorithm which focuses on the faults knowing in advance where they are located. We have developed a new dynamic algorithm based on intermediate node concept and stress value concept to overcome all of blind sides of mentioned algorithms. We have designed a switch/router base on this algorithm and simulated by MAX PLUS II tool and verified it on a mesh NoC in Xilinx environment.

The focus of this study is on developing a framework for a Quantum Algorithm Processing Unit (QAPU) and a hybrid architecture for classical-quantum algorithms. The framework is used to increase the implementation performance of quantum algorithms and design Quantum Processing Units (QPU). The framework shows a general plan for the architecture of quantum processors which is capable to run the quantum algorithms. In particular, the QAPU can be used as a quantum node to design a quantum multicomputer. At first, the hybrid architecture is designed for the quantum algorithms. Then, the relationship between the classical and the quantum part of hybrid algorithms is extracted and main stages of the hybrid algorithm are determined. Next, the framework of the QAPU is designed and developed. Some gates and connections are projected in the framework that can be applied for future quantum algorithms. Furthermore, the framework is implemented and simulated for the existing quantum algorithms on a classic computer. It is shown that the framework is appropriate for quantum algorithms.

Land surface temperature (LST) is among the most important indices in the studies related to earth surface such as conservation, energy exchange and the water between the land surface and atmosphere. The main goal of this study is to present an algorithm in order to estimate the land surface temperature using the data of the Hyperspectral Thermal Emission Spectrometer (HYTES). The HYTES sensor has 256 bands in the range of 7.4-12 micrometers that bands in the range of 7.4-8 micrometers are removed due to strong water vapor in this spectral region and bands above 11.5 micrometers are removed due to issue of calibration. 202 bands remain, which we want in this study to obtain optimal bands from 202 bands using the genetic algorithm and then obtain the land surface temperature using those bands. We need to define a cost function and appropriate initial parameters for the genetic algorithm to select optimal bands. In this research, the cost function is to minimize the temperature difference between the thermal product of the sensor and the obtained land surface temperature with a split window algorithm and the number of variables in each gene is as large as the number of bands (202) and the initial population is 80 in genetic algorithm. The bands used in split window algorithm are selected using the genetic algorithm. In this study, we use split window algorithm that obtain land surface temperature through optimal bands that are selected using Genetic algorithm among 202 bands. Generally, in this study, first we use Genetic algorithm to choose optimal bands from 202 bands and obtain the coefficients of split window algorithm.The number of these coefficients is dependent on the number of bands which are selected by Genetic algorithm. Then, by using resulted coefficients that are obtained with least squares method and selected bands, land surface temperature is obtained for two different data through split window algorithm. In this research, a small part of the first data was used as training data for the genetic algorithm to obtain the coefficients algorithm of the split window and optimal bands to calculate the land surface temperature for the rest of the data. In a separate window algorithm, in addition to the algorithm coefficients, we need the emissivity of the relative bands used in split window algorithm. In this study, we used the emissivity product of the HYTES sensor. Among 202 bands, 110 bands are selected using the genetic algorithm. Using this 110 bands, split window algorithm coefficients and bands emissivity, land surface temperature is calculated for two data and evaluated. Finally, the thermal product of HYTES is used to evaluate the our proposed method and indicate its accuracy. The temperature obtained using proposed algorithm for both data is evaluated with reference data (thermal product) and the RMSE value is resulted as 0.025 and 0.999 for the first and second data respectively. Therefore, according to the obtained errors, we can argue that the proposed algorithm is an appropriate method to obtain the land surface temperature using the data of HYTES.

This paper aims at optimizing the parameters involved in stress analysis of perforated plates, in order to achieve the least amount of stress around the square-shaped holes located in a finite isotropic plate using metaheuristic optimization algorithms. Metaheuristics may be classified into three main classes: evolutionary, physics-based, and swarm intelligence algorithms. This research uses Genetic Algorithm (GA) from evolutionary algorithm category, Gravitational Search Algorithm (GSA) from physics-based algorithm category and Bat Algorithm (BA) from Swarm Intelligence (SI) algorithm category. The results obtained from the present study necessitate the determination of the actual boundary between finite and infinite plate for the plates with square-shaped holes. The design variables such as bluntness, hole orientation, and plate dimension ratio as effective parameters on stress distribution are investigated. The results obtained from comparing BA, GA and GSA indicate that BA as SI algorithm category competitive results, proper convergence to global optimal solution and more optimal stress level than the two mentioned algorithms. The obtained results showed that the aforementioned parameters have a significant impact on stress distribution around a square-shaped holes and that the structure’s load-bearing capability can be increased by proper selection of these parameters without needing any change in material properties.

 The complexity of mathematical models, exponential growth of the solution time for many methods, lack of access to gradient information and optimal local convergence are some of the problems that optimal classical algorithms face in solving complex problems. In order to eliminate these drawbacks, metaheuristic algorithms are widely used to solve complex and multivariate problems. Choosing the best and most suitable algorithm is difficult due to their high diversity. In previous studies, some of these methods have been summarized, but due to overpublicize of these methods in recent years, there is no specific article to describe and compare all of these methods. In this paper, the most important metaheuristic optimization algorithms are introduced from 2012 till now. In separate sections for each algorithm, the history, source of inspiration, objective function and number of its setting parameters are stated. These algorithms are then categorized and compared using several theories. Due to the type of application of each algorithm in engineering problems, it is not possible to introduce a single algorithm as the best methodology, but the Gray Wolf Optimization (GWO) algorithm is one of the algorithms with a high number of citations in recent years.

The k-nearest neighbor's algorithm (KNN) is one of the most widely used and useful nonparametric classification algorithms. The classification mechanism of this algorithm involves computing the distance between new instances and the instances whole classes are known. When the dataset contains non-numerical (ordinal and nominal) attributes, the performance of the algorithm can be significantly affected by how this distance is measured. In this paper, we attempt to improve the performance of the KNN algorithm by presenting a new solution for computing the distance of non-numerical traits. For this purpose, the Particle Swarm Optimization (PSO) algorithm is used. The task of this algorithm is to determine the best value of the distance between two states in a non-integer trait so that the accuracy of the KNN algorithm is increased. UCI University Learning Repository Data is used to test this idea. The results obtained from the proposed algorithm are compared with several other improved algorithms and show the useful improvement of this mechanism.

Stochastic gradient-based adaptation algorithms have received a great attention in various applications. The most well-known algorithm in this category is the Least Mean Squares (LMS) algorithm that tries to minimize the second-order criterion of mean squares of the error signal. On the other hand, it has been shown that higher-order adaptive filtering algorithms based on higher-order statistics can perform better in many applications, particularly in the presence of intense noises. However, these algorithms are more prone to instability and also their convergence rates decline in the vicinity of their optimum solutions. In attempt to make use of the useful aspects of these algorithms, it has been proposed to combine the second-order criterion with higher-order ones, e.g. that of the Least Mean Fourth (LMF) algorithm. The Least Mean Mixed-Norm (LMMN) algorithm is a stochastic gradient-based algorithm which aim is to minimize an affine combination of the cost functions of the LMS and LMF algorithms. This algorithm has inherited many properties and advantages of the LMS and the LMF algorithms and mitigated their weaknesses in some ways. These advantages are achieved at the cost of the additional computation burden of just one addition and four multiplications per iteration. The main issue of the LMMN algorithm is the lack of an analytical model for predicting its behaviour, the fact that has restricted its practical application. To address this issue, an analytical model is presented in the current paper that is able to predict the mean-square-error and the mean-weights-error behaviour with a high accuracy. This model is derived using the Isserlis’ theorem, based on two mild and practically valid assumptions; namely the input signal is stationary, zero-mean Gaussian and the measurement noise are additive zero-mean with an even probability distribution function (pdf). The accuracy of the derived model is verified using several simulation tests. These results show that the model is of a high accuracy in various settings for the noise’s power level and distribution as well as the unknown filter characteristics. Furthermore, since the LMF and the LMS algorithms are special cases of the more general LMMN algorithm, the proposed model can also be used for predicting the behaviour of these algorithms.

This work proposes a new metaheuristic technique that combines Differential evolution (DE) with gravity search in a consistent manner. Swarm intelligence benefits and the concept of tensile strength between two particles are combined to suggest superior meta-heuristic approaches for limitless optimization issues. The goal of this paper is to create a new algorithm that overcomes the shortcomings of the Gravitational search algorithm by leveraging the advantages of the Differential evolution algorithm in expanding search areas, overcoming early convergence problems, and improving the attractive algorithm's ability to converge towards the optimum. The GSA algorithm has been utilized in a search-oriented algorithm, whereas the Differential evolution algorithm is causing a high level of diversification in society, which leads to the establishment of search regions for the GSA algorithm. The effectiveness of the suggested approach was evaluated by solving a collection of 30 Real-Parameter Numerical Optimization problems that were presented at IEEE-CEC 2014. The findings are compared to 5 state-of-the-art unconstrained problem algorithms and 6 state-of-the-art unconstrained problem algorithms. The winner methods were also deduced from the results using the Wilcoxon signed test.

The genetic algorithm was invented in 1980 based on Darwin's evolutionary theory to solve optimization problems. In fact, the genetic algorithm is based on the principle of "continuing the life of the best" and "proliferation of the superior type". The main operators of the genetic algorithm include: coding, selection, integration operator and mutation operator. The performance of the genetic algorithm is very good in the first few repetitions, but with the continuation of the trend and the increase in the number of repetitions, we will face a multitude of excessive results and results, which increases the number of repetitions to the optimal response. The method of optimizing the ant is derived from the actual motion of the ant in nature to find food, which was first introduced in Marco Dorigo's doctoral dissertation in 1992 as the ant system. The algorithm introduced by Dorigo is based on two basic principles: 1- Injection and evaporation of the pheromone 2- potential tendency of the ant into the pheromone. The algorithm optimization of the ant nest has a slow convergence rate due to the absence of pheromones in the early repetitions. But with the continuation of the search process and the increase in the concentration of the pumped porose, it works very well in the final stages. The combined algorithm with the combination of two genetic algorithms and the curry optimization of the ant uses the benefits of both algorithms. In the hybrid algorithm, we first use a few repetitions of genetic algorithm, then consider the chromosome from the genetic algorithm as the primary answer of the ant algorithm, and continue the subsequent repetitions until the optimal answer with the ant algorithm. The combination algorithm performs better in terms of convergence and efficiency, better than genetic algorithms and ant nest optimization.

The importance of the parameters of any optimization algorithm, especially meta-heuristic algorithms that have been created to simplify the solution of optimization problems, is inevitable. The optimal values of these parameters, which generally depend on the specifics of the problem in question, have a significant impact on the performance of the mentioned algorithms and a better search of the solution space. Parameters selection of them will play an important role in performance and efficiency of the algorithms. This article examines the capability of various optimization algorithms and suggests dual hybrid optimization algorithms are named PSO-FA, PSO-GA, PSO-GWO, for solving the problem of computing the depth and location of cracks in cantilever beams. The performance of Particle swarm optimization (PSO), Genetic algorithm (GA), Grey wolf optimization (GWO), Firefly algorithm (FA), and hybrid of them base on PSO optimizer to determine the location and depth of crack for cantilever beam are proposed. These suggested algorithms are optimization algorithms based on intelligent optimization. So, the performance of these algorithms are analyzed when the control parameters vary.