Today, with the rapid advances in technology and the expansion of industries, electric motors are used extensively and consume a large part of the electrical energy produced by power plants. Therefore, researchers and experts have always been seeking solutions to make electric motors with high reliability and low losses. The machines are made of a high-temperature superconducting motor with high efficiency, came to be called high-temperature superconductor/induction synchronous motor. This paper studies the high-temperature superconducting induction/synchronous motor (HTS-ISM). The torque density and structural dimensions of HTS-ISM are optimized using one of the newest optimization methods, the Collective decision optimization algorithm (CDOA). The results show a torque of about 51. 75% increase via the optimization process. Also, the particle swarm optimization algorithm (PSO) as a commonly used optimization method is employed to compare the results. The comparison proves the high capability of the CDOA method to optimize the motor design parameters. All algorithms in this paper are run with the MATLAB software package.

decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Classical crisp decision trees (DT) are widely applied to classification tasks. Nevertheless, there are still a lot of problems especially when dealing with numerical (continuous valued) attributes. Some of those problems can be solved using fuzzy decision trees (FDT). Over the years, additional methodologies have been investigated and proposed to deal with continuous or multi-valued data, and with missing or noisy features. Recently, with the growing popularity of fuzzy representation, a few researchers independently have proposed to utilize fuzzy representation in decision trees to deal with similar situations. Fuzzy representation bridges the gap between symbolic and non symbolic data by linking qualitative linguistic terms with quantitative data. In this paper, a new method of fuzzy decision trees is presented. This method proposed a new method for handling continuous valued attributes with user defined membership. The results of crisp and fuzzy decision trees are compared at the end.

Collective decision-making is the process by which a group of people makes a single decision on a particular issue to achieve improvement by realizing their goal. Collective decision-making is known as one of the most important ways to achieve optimal decision-making. This paper aims to provide a comprehensive picture of the dimensions of Collective decision-making and to answer a set of related questions in this field via a systematic literature review (SLR). In this regard, in the first part, the Collective decision-making will be introduced and the SLR protocol will be examined in 5 steps. In the next section, the most important keywords and areas of research are identified and a co-word analysis map in the field of Collective decision-making is drawn for the period between 1990 and March 2021 on the Web of Science. In the following, research questions will be answered. In response to these questions, the need for use and examples of the use of Collective decision-making is stated. Then, effective factors in Collective decision-making are examined and the way to reach a Collective decision is explained. Finally, the advantages and disadvantages of Collective decision-making are discussed and its methods and tools are described. Also, models of Collective decision-making are given as a sample and an analysis of the results is provided.

Background and Purpose: One of the most important topics in Persian Medicine is the knowledge of temperament identification and many of the instructions for preserving health, diagnosis and treatment of diseases are different based on the individual's temperament. Discovering and recognizing standard methods of temperament determination, is one of the most important research priorities in Persian Medicine. In this research, fuzzy decision tree for data classification and Genetic algorithm (GA) to optimize the features necessary for the diagnosis of temperament is used. Materials and Methods: In this study, two datasets with 52 and 221 samples were used. For datasets, data recognition and modeling Mizaj (Temperament) diagnosis based on fuzzy decision tree with GA was performed. To do this, first, a subset of features was selected using GA and then a fuzzy decision tree was used to make the rules. Results: For each dataset, two decision trees were generated for warmth/cold and wet/dry and the produced rules by the Persian Medicine specialist were evaluated. The results showed that the produced correct rules in the second dataset are 44% for warm/cold Mizaj and 33% for wet/dry Mizaj. In the first dataset, the generated correct rules by the fuzzy decision tree with the GA for wet/dry Mizaj was 9. 5%. Conclusion: Comparison of the results with the previous research shows that the use of GA and subset selection of features, reduces the computational volume, size of the tree and error percentage so that better results can be achieved. Although, according to Persian Medicine experts’ opinion, the results of this research are not currently applicable, they can be a starting point for further researches in the optimization of intelligent swarm algorithms for the diagnosis of Mizaj.

Optimal operation of reservoirs is one of the most important issues in water resources management. In the present study after introducing Whale optimization algorithm (WOA), the performance of this algorithm is evaluated separately and in the hybrid with the Genetic algorithm (hybrid WOA-GA) in the optimal operation problem of Salman Farsi dam reservoir. In the present optimization problem, the objective function is defined as minimizing the total deficit during the operation period. Also, the constraints of the reservoir continuity equation, reservoir storage volume and released volume from the reservoir have been applied to the objective function of the problem. The Performance of proposed algorithms is compared with the performance of the Genetic algorithm (GA) and Non-Linear Programming (NLP). The performance of models has been evaluated based on Reliability, reversibility, vulnerability and stability criteria. The results of optimal solutions showed that the absolute optimum is equal to 0. 181 based on NLP method and using Lingo software and the optimal solutions for the models of the hybrid WOA-GA, GA and WOA with 2. 9, 24. 2 and 337 percent increase compared to the absolute optimum are ranked first to third respectively. A Multi-Criteria decision-Making technique (MCDM) has been used to select the best model based on the objective function and evaluation criteria of the models' performance. The results of this technique showed that the performance of the hybrid WOA-Ga model is ranked first, and the models of NLP, GA and WOA are in the next ranks, respectively.

The main issue in management science is decision making, and the success or failure of any manager can be considered dependent on his decisions. One of the methods that can help a lot in the correctness of managers' decisions is Collective decision-making. This article tries to explain the concepts related to Collective decision making and then the most important questions related to this method are answered using the grounded theory method. In this article, using the semi-structured interview, the opinions of 36 organizational and academic experts have been used. After collecting data through interviews, the components were coded openly, axially, and selectively. The results show that there are 35 concepts around the central phenomenon of Collective decision-making that can be divided into 6 categories: causal conditions, contextual conditions, intervening conditions, central phenomenon, strategies, and consequences. An important innovation of this paper is the presentation of a Collective decision model using the grounded theory method that can convey useful information to managers in a concise manner, and motivate them to use this method.

In the absence of satellite ephemeris data and inner geometry of satellite’ s sensor, utilization of Rational Function Models (RFMs) is one of the best approaches to georeferencing satellite images and extracting spatial information from them. However, since RFMs have high number of coefficients, then usually high number of control points is needed for their estimation. In the other hand, RFM terms are uninterpretable and all of them causes over-parametrization error which count as the most important weakness of the terrain-dependent RFMs. Utilization of optimization algorithms is one of the best approaches to eliminate these weaknesses. Therefore, various optimization algorithms have been used to discover the optimal composition of RFM’ s terms. Since the mechanism of these algorithms is different, the performance and feature characteristics of these algorithms differ in the discovery of the optimal composition train-dependent RFM’ s terms. But the existing differences not comprehensively analyzed. In this paper, in order to comprehensive assessment the abilities of Genetic optimization algorithm (GA), Genetic modified algorithm (GM), and a modified Particle Swarm optimization (PSO) in terms of accuracy, quickness, number of control points required, and reliability of results, are evaluated. These methods are evaluated using for different datasets including a GeoEye-1, an IKONOS-2, a SPOT-3-1A, and a SPOT-3-1B satellite images. In terms of accuracy achieved, difference between these methods was less than 0. 4 pixel. In terms of speed of evaluation of parameters, GM was 10 to 12 time more quickly in comparison with two other algorithms. In terms of control points required, degree of freedom of modified PSO was 45. 25 percent and 27 percent more than GM and GA respectively, and finally in terms of reliability, the dispersion of RMSE obtained in 10 runs of three algorithms are relatively same. These results indicated that accuracy and reliability of all three methods are almost the same, speed of GM is higher and modified PSO needs less control points to optimize terrain-dependent RFM.