Detecting community structures is applicable in a wide range of scientific fields such as biological and social sciences. Community detection is one of the most renowned problems in the field of social networks mining. Thus, many methods have been introduced and developed in order to meet diverse needs of community detection. The aim of community detection is to partition the network in such a way that relations between components of network are dense. Since the relations between the members of partitions are strong, it is possible to consider them as a community or a cluster. In this paper, we have considered community detection as a multi-objective problem. The objective functions are modularity and community scores, which are two of the most well-known objectives in the literature. In order to optimize these objective functions, two Algorithms, which are the enhanced versions of NSGAII and NRGA, have been proposed. These methods use a greedy Algorithm to obtain initial population. Moreover, new crossover and mutation operators have been designed. The crossover operator is based on closeness of nodes. The mutation operator is based on TOPSIS method. The proposed crossover and mutation operators always generate feasible solutions. Furthermore, the closeness index helps to form distinct and high quality communities. We have compared the performance of the proposed methods with those of classical NSGAII, NRGA, and a well-known method called MOGA-Net by conducting several numerical experiments in six real-world networks. The experiments split into two parts. In the first part, we have compared the solutions of these five Algorithms regarding the values of objective functions. The second part is dedicated to the comparisons made based on various multi-objective metrics. We have considered spacing, generational distance, inverted generational distance, set coverage, normalized mutual information, computation time, and the number of non-dominated solutions obtained by each method. In order to ensure that the solutions obtained by the proposed Algorithms are significantly better than the ones provided by the other three methods, we have conducted several two-sample t-tests. The results showed significant improvement, and the proposed Algorithms outperformed the other three methods regarding various criteria.

Data classification is one of the main issues in management science which took into account from different approaches. Artificial intelligence methods are among the most important classification methods, most of them consider total accuracy function in performance evaluation. Since in imbalanced data sets this function considers the cost of prediction errors as a fix amount, in this research a sensitivity function in used in addition to the accuracy function in order to increase the accuracy in all of the predefined classes. In addition, due to complexity in process of seeking information from decision maker, NSGA II Algorithm is used to extract the parameters (Weight vector and cut levels between classes). In each iteration, based on the estimated weight vector and data sets, the Algorithm calculate the score of each alternative using Sum Product function and then allocates the alternative to one of the classes, comparing to the estimated cut levels, . Then, using the fitness functions, the estimation class and the actual class will compare by two Algorithms and this process will continue since optimizing the parameters. Comparison of the NSGA II and NRGA Algorithms show the high efficiency of the proposed Algorithm.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic Algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic Algorithm and multi-objective optimization goals. The genetic Algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the Algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

Home Care (HC) staff assignment problem is defined as deciding which staff to assign to each patient. In this study, a multi-objective non-linear mathematical programming model is presented to address staff assignment problem considering crosstraining of caregivers for HC services. The first objective of the model is to minimize the cost of workload balancing, cross-training, and maintenance. The second objective minimizes the number of employees for each service, while the third objective function maximizes the satisfaction level of caregivers. Several constraints including skill matching, staff preferences, regularity, synchronization, staff absenteeism, and multi-functionality are considered to build a service plan. Due to NP-hardness of the problem, a Non-dominated Sorting Genetic Algorithm (NSGA-II) with a proposed who-rule heuristic initialization procedure is applied. Due to the absence of benchmark available in the literature, a Non-dominated Ranking Genetic Algorithm (NRGA) is employed to validate the obtained results. The data required to run the model are gathered from a real-world HC provider. The results indicate that the proposed NSGA-II is superior to the NRGA with regard to comparison indexes. Based on the results obtained, it is now possible to determine which staff to cross-train for each service and how to assign staff to services.

Increasing population and food demand, disproportionate cultivation and annual production of various agricultural products with market needs and low productivity of the agricultural sector and the loss of water and soil resources have made it necessary to determine and implement the country's optimal cropping pattern. In this study, due to the limitations and problems of classical methods in order to reduce processing time and improve the quality of solutions, the Multi-Objective Chaotic Particle Swarm Optimization was used to determine the optimal cultivation pattern of Sistan plain in optimal conditions and deficit irrigation. The results of the Multi-Objective Chaotic Particle Swarm Optimization for the dominant cultures in the region showed that the current cropping pattern of the region is not optimal and with the implementation of the proposed model, the profit per unit area under cultivation will increase. The results of application of deficit irrigation during different growing periods of wheat, barley, alfalfa, sorghum, watermelon and grapes showed that applying deficit irrigation in this plain is not a good strategy and therefore only a full irrigation strategy is recommended. The results of sensitivity analysis of the model showed that at low prices, farmers reaction is less and at higher prices more reaction to price changes and with increasing prices, the program efficiency is lower.

One of the basic topics in hydrological and river engineering studies is flood routing.Flood flooding is common in multi-tributary rivers and rivers without intermediate basin statistics. Therefore, to achieve the determination of slopes and cross-sections in all sections of the river, the Muskingum hydrological model is a useful method that helps to save information on the depth and flow of the flood at any time by saving time and money. To specify. In this study, the nonlinear parameters of the new Muskingum model are optimized based on the fly Algorithm (MA). In this non-linear model of Muskingum, which has eight parameters, the recovery coefficient γ is used, which has more or less values ​​than the number of peaks discharged in the output hydrograph.To evaluate the performance of Muskingum's new nonlinear model with the new MA Algorithm, the Wilson and Weisman-Lewis case study has been used by many previous researchers for validation.The results of the MA Algorithm for Wilson and Weissman-Lewis rivers show the minimization of the residual squares (SSQ) as the objective function, which is 3.21 for the Wilson River and 68722 for the Weissman River. The results of this study showed that the proposed model has high accuracy in estimating the output discharge values.

On one hand, the delicate physics and high sensitivity in the process of paddy conversion to white rice and on the another hand, the importance of quality and its role in the value added of the final product indicate the importance of managing three indicators including quality, cost and time in the rice production. Therefore, the purpose of this study was to achieve optimal layout of different methods with the lowest cost, minimum time and highest quality in the conversion process. For this purpose, all possible methods for each stage of the conversion process in the modern milling units were expressed and a series of fuzzy numbers was considered for them. Risk management was also done by applying fuzzy cutes from zero to one to investigate uncertainty. In the next step, the project management was adopted using the non-dominated sorting genetic Algorithm (NSGA-II) and non-dominated ranked genetic Algorithm (NRGA-II). Based on the results, the genetics Algorithm (NSGA-II) showed better performance in comparison with genetic Algorithm (NRGA-II) in solving this problem and finally, the lowest time, minimum cost and the highest quality in the specified conditions (α = 1) were founded 22. 22 hours, 8088170 Rial and 62%, respectively.

In this paper, a novel mathematical model for a preemption multi-mode multi-objective resource-constrained project scheduling problem with distinct due dates and positive and negative cash flows is presented. Although optimization of bi-objective problems with due dates is an essential feature of real projects, little effort has been made in studying the P-MMRCPSP while due dates are included in the activities. This paper tries to bridge this gap by studying tardiness MMRCPSP, in which the objective is to minimize total weighted tardiness and to maximize the net present value (NPV). In order to solve the given problem, we introduced a Non-dominated Ranking Genetic Algorithm (NRGA) and Non-Dominated Sort Genetic Algorithm (NSGA-II). Since the effectiveness of most meta-heuristic Algorithms significantly depends on choosing the proper parameters. A Taguchi experimental design method was applied to set and estimate the proper values of GAs parameters for improving their performances. To prove the efficiency of our proposed meta-heuristic Algorithms, a number of test problems taken from the project scheduling problem library (PSPLIB) were solved. The computational results show that the proposed NSGA-II outperforms the NRGA.

This article investigates the problem of simultaneous attitude and vibration control of a flexible spacecraft to perform high precision attitude maneuvers and reduce vibrations caused by the flexible panel excitations in the presence of external disturbances, system uncertainties, and actuator faults. Adaptive integral sliding mode control is used in conjunction with an attitude actuator fault iterative learning observer (based on sliding mode) to develop an active fault tolerant Algorithm considering rigid-flexible body dynamic interactions. The discontinuous structure of fault-tolerant control led to discontinuous commands in the control signal, resulting in chattering. This issue was resolved by introducing an adaptive rule for the sliding surface. Furthermore, the utilization of the sign function in the iterative learning observer for estimating actuator faults has not only enhanced its robustness to external disturbances through a straightforward design, but has also led to a decrease in computing workload. The strain rate feedback control Algorithm has been employed with the use of piezoelectric sensor/actuator patches to minimize residual vibrations caused by rigid-flexible body dynamic interactions and the effect of attitude actuator faults. Lyapunov's law ensures finite-time overall system stability even with fully coupled rigid-flexible nonlinear dynamics. Numerical simulations demonstrate the performance and advantages of the proposed system compared to other conventional approaches.