evolutionary algorithms

Considering the growing importance of energy storage technologies in large-scale construction to improve the performance of power grids, this paper examines the investment in these technologies from an economic perspective. The main article focuses on the use of large battery technologies such as redox vanadium batteries, polysulfide-bromine batteries, and zinc-bromine batteries for power grids due to their special characteristics such as long life, good efficiency, and affordable costs. Smart are very suitable. The proposed model of this research is designed using golf optimization algorithm, which is considered as the main innovation of this research. This algorithm is specifically used to optimize performance and investment in large-area energy storage systems. Using this model, the effects of using different types of technologies are analyzed. The results of the simulations show that considering different battery technologies and the operation of the golf optimization algorithm can have significant economic effects in optimizing the performance of energy storage systems.

Nowadays, clustering plays an important role in most research fields such as engineering, medicine, biology, data mining, etc. In fact, clustering means unsupervised division. By using it, the data are divided into categories that are more similar to each other in terms of the parameters of interest. One of the famous methods in this field is k-means. In this method, despite the dependence on initial conditions and convergence to local optimal points, N numbers of data are grouped into k clusters with high speed. In this article, to solve the existing problems, the combined method is used based on evolutionary algorithms, chaos theory and k-means; that is in addition to solving the mentioned problems, it will also be independent of the number of variables. In this article, for the purpose of validation, the proposed methods are implemented on 13 different famous collections, and the results are compared with genetic algorithm, particle community, bee colony, simulated refrigeration, differential evolution, harmony search, and k-means methods. The high ability and robustness of these methods will be evident based on the results.

The 3D integrated circuit is emerged as a promising solution to integrate very large-scale circuits on electronics chips. In such chips, several layers of silicon substrates are stacked which are separated by insulator interfaces. Interconnection between two layers is realized using Through Silicon Via (TSV). Fabrication of TSVs is challenging due to their large size and complex process. Consequently, the number of TSVs should be minimized in the circuit’s implementation. The 3D implementation consists of three main steps: Partitioning, Placement, and Routing. In this paper, the first two steps are accomplished using the Simulated Annealing-based optimization approach wherein minimization of the number of TSVs and total wire length are considered the main objectives. In this paper, an improved version of the pathfinder method has been developed which would efficiently generate the necessary interconnections among circuit modules. The results of simulations on MCNC benchmark circuits show that the proposed method outperforms the previous state-of-the-art methods in all aspects. In comparison with FSA, the number of TSVs is reduced by 6.15%, and the algorithm’s runtime is decreased by 27.79%. Moreover, in comparison with the hMETIS method, the number of TSVs is reduced by 9.78%, and the algorithm’s runtime is decreased by 31.73% .

An accurate prediction of the remaining useful life of the equipment is necessary for use, repairs and maintenance. Useful life prediction has been widely used, while the data obtained from it is not functional in different conditions. Many data-driven algorithms have been proposed and good results have been obtained in the field of predictive troubleshooting. Therefore, in this article, the relevant parameters are optimized using the meta-heuristic algorithm, so that the moving time window is used along with the mathematical model. Setting parameters related to data in the optimization framework allows the use of simple models such as neural networks with a small number of hidden layers and a small number of neurons in each layer, which can be used in environments with limited resources such as embedded systems. To evaluate the effectiveness of the proposed method, the root mean square error scoring index and useful life health score have been used. For this purpose, a random data set has been considered and the results show the acceptability of the method.

The safety analysis of software systems, especially safety-critical ones, should be performed exactly because even a minor failure in these systems may result in disaster consequences. Also, such analysis must be done before implementation, i.e. the design step and in the model level. Model checking is an exact and mathematical-based way that gets a model of a system and analyzes it through exploring all reachable states of the model. Due to the complexity of some systems and their models, this way may face the state space explosion problem, i.e. it cannot explore all available states. A solution to solve this problem in these systems is that model checking tries to refute them, instead of verifying them, by finding errors such as deadlock (if available).Although, a heuristic has been previously proposed to find a deadlock in the model's state space and it has been applied in several simple heuristic search and evolutionary algorithms, its detection speed has been low. In this paper, we propose a novel heuristic to detect a deadlock in the model's state space, and test and compare its detection speed by applying it in several simple heuristic search algorithms such as iterative deepening A*, beam search, and evolutionary algorithms such as genetic, particle swarm optimization, and Bayesian optimization. Comparison results confirm that the new heuristic can detect a deadlock in less time than the previous heuristic.

In recent years, meta-heuristic algorithms and their application in solving complicated, nonlinear, and high dimensions problems have increased dramatically and the fact that meta-heuristic algorithms are used to solve complex and changing problems of real life, has caused the algorithms world and their design to be very dynamic and alive; that's why new algorithms are constantly being created. Hence, the purpose of this research is to introduce a novel meta-heuristic algorithm called Military Optimization Algorithm (MOA). 

Inspired by military operations, the proposed algorithm was designed and presented. After coding, Standard test functions and benchmark algorithms were determined to evaluate the performance of the algorithm.

The performance of new algorithm is analyzed by 23 standard test functions and compared to 8 benchmark meta-heuristic algorithms including: Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony, Shuffled Frog Leaping Algorithm, and Imperialist Competitive Algorithm, Grey Wolf Optimizer, Whale Optimization Algorithm, and Grasshopper Optimization Algorithm, by considering three indices of "average answers", "time complexity of algorithm (speed)" and "Convergence speed/ time".  The results show the excellent performance of the proposed algorithm.

In this paper, inspired by military operations, a novel meta-heuristic algorithm called MOA is introduced. It is population-based and stable with "random search", "dividing solution space into several regions and allocating a part of the population to each region", "cavalry search", and "infantry search".

One of the most challenging issues with web services is the composition of them, which is presented as a graph to show the interaction between services. Each node in this graph is called an abstract web service which their function is specified but the quality features are unclear. For each abstract service, there is a set of candidate services with the same function but different qualitative features. Replacing a candidate web service for each abstract service so that an optimal combination is achieved is an NP-hard problem that cannot be solved in polynomials, hence to solve it using heuristic algorithms. Several methods have been proposed for the web services optimal composition, but most of these methods don't support the probability structure. Only one method supports a probability structure that is not scalable for large graphs, constraint based, and analyzes each path of the graph separately. This paper presents an integrated scalable multi-objective approach for analyzing graph to not only covering two new patterns of nested loops and parallel loops, but also improving performance with representing a method for simplifying web-service composition. In this method, evolutionary algorithms are used for optimal web services selection and scalability. The two selected evolutionary algorithms are NSGAII and SPEAII. In the proposed method, first in conditional graphs, each path is repeated according to its probability, and then the NSGAII algorithm is used to determine the best path in the graph and find better solutions.  At the end of the article, the results of 8 methods are presented, which compared to the best of them, the proposed method has improved 30% in the reliability parameter and 121 milliseconds in response time.

In this paper, to solve the multi-objective problem of distribution feeder reconfiguration (DFR) in the presence of distributed generation (DG), the hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) has been proposed, which is a combination of particle optimization (PSO) and gravitational (GSA) optimization algorithm. In this field, the power losses and operating costs are the two most used objective functions in the literature. In addition to the mentioned objective functions, this paper also considers the optimal generation capacity of DG resources and energy not supplied (ENS), which is one of the basic reliability indexes of distribution networks. In this paper, the values of different objective functions are normalized by the fuzzy method, and also the Fuzzy decision-maker is used to determine the most optimal solution among the Pareto-optimal solutions. The proposed algorithm is implemented on IEEE 70-bus and 119-bus test systems. The simulation results show the efficiency of the proposed PSOGSA in improving the considered objective functions. The proposed method, by establishing a suitable fit between different objective functions has introduced a more efficient structure with lower losses and operating costs, as well as greater reliability, compared to other optimization algorithms.

Quantum computing is a new method of information processing based on the concepts of quantum mechanics which leads to strange and powerful events in the field of quantum. Each unitary matrix represents a quantum gate. Synthesis of quantum circuits refers to the process of converting a quantum gate into a series of basic gates and is divided into two general categories, namely decomposition and composition-based. In the first category, quantum circuits are synthesized using matrix decomposition methods. This study deals with the second category, which uses evolutionary algorithms and especially genetic algorithms to multiply matrix gates to achieve the final characteristic of a quantum circuit.

Due to the growing desire of people to join and use social networks, communication and shar ing data in these networks has been considered by various sciences such as political science, psychology, sociology, economics, etc. Hence, researchers have begun to distinguish and extract relationships between individuals from the data contained in these networks, to create more accurate communities. However, there is still no effective method to identify and extract communities based on social media data. In this article, a method has been proposed for social network accurate clustering by using Imperial Competitive Evolutionary Algorithm (ICEA) and selecting the initial population based on the density-based clustering criterion. The proposed method has improved the result of modularity about 21.45% in average, compared to rival basic ICEA and extracted more densed communities.

Congestion of vehicles in metropolitan areas has increasingly raised concerns about the negative consequences for human health, the environment and the economy. The movement of public and private vehicles in the city to send goods, urban traffic or public transport, intensifies traffic and result in social anomalies, release of pollutants, increasing the psychological impact and cost of urban living, waste of fossil fuels, and ultimately degrading quality of urban life. In this regard, many researches have addressed this problem amongst, the Computational Intelligence (CI)-based algorithms are in the center of attention. In this paper, we have reviewed the most recent and influential CI-based methods presented for solving the Vehicle Transportation Routing System (VTRS) problem.

To optimize the management and optimal use of groundwater resources, it is necessary to be aware of the temporal-spatial changes of the stagnant level . For modeling and predicting hydrogeological variables, the question remains:To what extent these hybrid models can be effective compared to the individual model?, in this study four algorithms of particle overvoltage optimization (PSO) genetics (GA) ant colony (ACOR) and demand evolution (DE) were combined with the model of adaptive fuzzy-neural inference system (ANFIS).The four combined models performance developed with the ANFIS model and the time series model (SARIMA) as the reference model to estimate the average monthly groundwater level of the Sahneh plain aquifer in Kermanshah province was evaluated over 19 years.To better compare the results of the models, the same input variables of the groundwater level in different time steps (maximum four months based on the self-correlation function of aquifer level) were considered for them. The results of fitness indicators in the test and test phase showed that there was no significant difference between the SARIMA time series model compared to other combined models used.However, given that SARIMA applies average moving processes, authorization, seasonal changes, and delays in modeling, groundwater leveling can be given more attention in modeling. The RMSE values of the best hybrid model (ANFIS-GA) and SARIMA were 0.950 and 0.1012, respectively. The results also showed that the combination of optimization algorithms considered with the ANFIS model does not improve the model's results compared to the individual ANFIS model in terms of significance.

Topology optimization of structures, seeking the best distribution of mass in the design space to improve the performance and weight of a structure, is one of the most comprehensive issues raised in the field of structural optimization. In addition to the structure stiffness as the most common objective function, frequency optimization is of great importance in industries achieved by maximizing the fundamental frequency or the gap between two consecutive eigenfrequencies. The phenomenon of multiple frequencies, mesh dependency of topology responses, checkerboarding, geometric symmetry constraint, and occurrence of artificial localized vibration modes in low density regions are the most important challenges faced by the designer in stiffness and frequency optimization problems which influence the manufacturability of the design too. In this paper, BESO method is applied for a frequency and stiffness problem separately via creating a software package including a Matlab code and Abaqus FE solver. Also, in this paper the effect of geometric symmetry constraint is considered on resulted topologies from stiffness and frequency problems. So the BESO method is applied for modeling a 2D beam and its stiffness and frequency optimization and finally the optimization results of both objective functions will be compared with initial structure.

Maneuvering air targets Tracking has many applications in defensive and non-defensive areas. Target tracking requires the estimation of position, velocity, and acceleration, simultaneously. Common approaches for air targets tracking measures the distance to target and target heading angle which is a nonlinear function of system states. Since these measurements are noisy, using estimating and filtering methods for assessing the speed and acceleration of target is essential. Although, extended Kalman filter works well with nonlinear systems and Gaussian noises; in practice we encounter with non-Gaussian noises such as Glint which particle filters show better performance in them. In addition, due to the high computational load of particle filters, implementing and applying them is impossible. In this paper, the evolutionary algorithm of particle swarm optimization has been used in the sampling step to reduce computational load and improve the real-time performance of particle filter in solving air target tracking problems. The proposed method is simulated and evaluated in a scenario involving all possible motions of the target with high maneuvering. In addition, performance of an evolutionary particle filter with some particle filters and extended Kalman filter is compared. The simulation results indicate that the evolutionary particle filter has the capability of real time air targets tracking with maneuvering in comparison with the particle filter and extended Kalman filter while having high precision.