non dominated sorting genetic algorithm

Pair formation is an important step in pair trading that has only been examined manually or through numerical instructions. These methods fail in the multivariate mode and do not consider conflicting goals in the problem structure. In this research, a method is presented to create multivariate pair combinations by considering contradictory multiple goals in stock pair trading.

In this study, the statistical sample is limited to the top 30 companies listed on the Tehran Stock Exchange due to the need for high-frequency transactions. The problem is developed in the form of a Mixed Integer Programming (MIP) model, and due to non-convex constraints and exponential solution space, a multi-objective genetic algorithm is used to obtain multivariate pair combinations. To achieve multiple goals, the developed type of genetic algorithm, namely, The Chaotic Non-dominated Sorting Genetic Algorithm (CNSGA-II), was used. In this method, chaos theory is used to create the initial population of the genetic algorithm in order to obtain appropriate and high-precision solutions.

The results showed that the use of chaos theory could increase the degree of convergence in evolutionary algorithms. In addition, these results indicate the superiority of the multi-objective pair trading strategy based on the distance approach over the traditional single-objective model.

Originality/Value: 

In order to optimize pair trading, the Non-dominated Sorting Genetic Algorithm was used. Also, the initial population of individuals was created in a multi-objective genetic algorithm based on chaos theory.

Road network is one of the main infrastructure developments and it is considered as the national capital. Maintaining this huge capital requires long-term planning and continuous activity of maintenance. In the past, traditional maintenance methods were focused on activities intrinsically or structures. Because preventative maintenance measures to increase pavement life-time service-systems and decrease the cost of long-term facilities, the theory carry out maintenance before the start of the collapse of the pavement is been considered by managers. Crowded intercity routes of Mazandaran province, especially in the tourist region studied in this research that was chosen because of high traffic and vehicle traffic. In order to solve this issue, after coding in MATLAB software, based on mathematical models and algorithms different states based on different period, the number of choosing the path of repairs category and a limited budget is defined, then desired results is obtained. The characteristics of solutions is defined in different states and changes in cost, utility and total number of selected routes as well as maintenance paths of selection from each category is analyzed.

By developing the distributed generation units, the hybrid micro-grids usage besides the energy storage systems has changed the future of the electricity industry. In addition to the many benefits of the micro-grids, they can undermine security, reliability, stability, and other network indices if not properly scheduled. In this paper, a new attractiveness index is defined in order to optimal schedule the DGs generation and charging/discharging of the energy storage system (ESS) in a hybrid micro-grid. Also, the pollutant emission of the units is considered as the second objective along with the proposed attractiveness index, which constitutes a two-objective optimization problem. To solve this nonlinear and non-convex optimization problem, the Non-Dominated Sorting Genetic Algorithm (NSGA-II) has been used. The main advantages of this algorithm are the ability to escape the local optimal traps and fast convergence. For further comparison, the Quantum Particle Swarm Optimization (QPSO) algorithm has been implemented. The performance of both algorithms in solving the proposed optimization problem is evaluated on a standard hybrid micro-grid. The results show faster convergence and better performance of the NSGA-II algorithm in terms of the final optimization solution.

Today, information and knowledge are so intertwined with human life that the present age has been called the information era. In this situation, the role of human in the organization and the way of looking at him/her will have a significant contribution to the success or failure of the organization. The role of human resources is becoming more and more prominent and it is now mentioned as the only factor in gaining a sustainable competitive advantage. Furthermore, due to dramatic developments in the field of human resource management, competency-based human resource planning has found a special place. In this study, a project scheduling problem considering multi-skilled resource and manpower competency is investigated. To do this end, an integrated multi-objective optimization model of project scheduling and human resource assignment including total completion time and execution cost minimization and project quality maximization is presented. To solve problem exactly, a mixed-integer programming model is developed and then solved by ε-constraint method in GAMS software. As the proposed model is NP-hard, a non-dominated sorting genetic (NSGA-II) algorithm is developed to solve the large scale problem. Furthermore, in order to validate the proposed algorithm, its results compare with exact method for instance problem in small scale. Results indicate that the proposed algorithm is outperformed and it can be used for real case problems.

Currently, renewable power plants based on photovoltaic farms are considered as main sources of energy production which have been rapidly growing. However, changes in weather conditions are one of the issues facing this energy conversion system to provide the required power for the consumer. An efficient controller can appropriately control and improve the dynamic performance of the island microgrid based on the photovoltaic system. In this paper, a perturbation and observation method based on optimal fuzzy control is proposed to provide the required energy for the microgrid. In this regard, partial changes in the proportional and integral constants during the climate changes are calculated to ensure the convergence at the desired point. In order to find the desired parameters of the fuzzy system membership functions, a non-dominated sorting genetic algorithm is used. Then, the optimal duty cycle signal is injected into the boost converter. To verify and validate the performance of the proposed controller, a comparison is also made with the conventional observation and perturbation strategy. Also, different radiation conditions are considered for the under study photovoltaic system. Modeling of photovoltaic system and proposed control system has been performed by MATLAB / Simulink software. Finally, the simulation results show that the speed and accuracy of the maximum power point tracking by the proposed control system has been significantly improved.

Due to the competitiveness of the market, manufacturers have been forced to increase their activity effectiveness and efficiency. The shortening of the life cycle and the period of product supply to the market have forced manufacturers to increase the efficiency of their activities and production processes. As regards, the scheduling process and sequencing of efficient operations in manufacturing environments is one of the strategic issues for survival in the competitive market. Workshop environments such as job shop and flow shop are used in many industrial and service processes. One of the most challenging scheduling problems is the open shop scheduling one, but researches in this realm have not paid much attention to human resources. When there is no limit to the processing route of any job on shop machines, this model is referred to as an open shop. The open shop scheduling problem is a strategic issue. However, in most of available schedules in the literature, only workshop equipment, such as machines, is considered as limited resources, but in reality we are confronted with limited human and machine resources. In this study, a mixed-integer programming model is presented for the bi-objective open shop scheduling problem with limited human and machine dual resources. Small-sized problems are solved by using the exact epsilon-constraint method. According to the Np-hardness of this problem, two pareto-based meta-heuristics algorithms were used which are the Non-Dominated Sorting Genetic Algorithm (NSGAII) and Multi-objective Vibration Damping optimization (MOVDO). In order to analyze and compare the algorithms, we used four different indicators which include: The number of members of the first Pareto front, mean of ideal distance and diversity and spacing measures. Also, 30 problems in three scales (small, medium, large) have been generated. The computational results shows that the NSGAII is more functional and has better output in comparison to the other presented algorithm.

During recent years, increment of incentives for deliberate subversive activities against power systems along with the restrictions on the allocation of financial resources for protection of these infrastructures have absorbed significant attentions of researchers toward the necessity of optimal allocation of these finances; and such the way, to minimize damages and consequently energy not supplied in case of future events. Planners of subversive activities as fully strategic actors target those low probability events which are no considered in protection schemes to maximize quantity of damages to power system. In this study, a new game theory based scheme is proposed in form of a zero sum playoff in order to provide an optimal allocation strategy. In this paper, two budget allocation algorithms have been presented to protect transmission lines and substations against deliberate threats. In the first algorithm, allocation of a certain amount of annual funding to transmission lines and substations is formulated with the aim of attaining the best possible condition of power grid in term of system reliability; while, required budget and procedure of allocation of this budget to targeted system utilities against undefined strategy of malicious individuals are calculated in second algorithm, in order to reach a predefined level of system reliability. Proposed model is implemented employing MATLAB (i.e. to execute single-objective Cuckoo optimization algorithm as well as multi-objective non-dominated sorting genetic algorithm II) and GAMS (i.e. to determine reliability of grid by use of power system load flow) software. Totally, optimized game strategy of protectors to acquire the best condition of system reliability has been obtained. Results validate effectiveness and applicability of proposed method in cases of optimality of allocation technique and subsequently increase of reliability indices.

One of the reasons for the high waiting time for patients in hospitals is the the lack of sufficient staff in the hospital, so the inefficiency of costs and job satisfaction of hospital nursing staff stems from the use of traditional and unscientific methods in allocating nurses to shifts. The present study is designed to determine the minimum number of nurse required according to the number of patients referred at different times, determine the shift schedule with the least required hours and schedule shifts for nurses in each shift with the lowest cost for the emergency department. The research method of the present study is of the mathematical modeling and research community, patients referring to the emergency department and nurses of a medical center. Data analysis is a combination of predictive methods, queuing theory models, and linear numerical programming. To predict the number of patients referring to the emergency, the time series method and ARIMA tools were used, and the M/M/C/K model was used to examine the queue system with limited capacity. One of the most important results of this study is to determine the maximum number of nurses available in each shift. Another result of this study is the comparison of the performance of each of the meta-heuristic Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Bee Algorithm (BA) with respect to the defined indicators.

One of the reasons for the high waiting time for patients in hospitals is the the lack of sufficient staff in the hospital, so the inefficiency of costs and job satisfaction of hospital nursing staff stems from the use of traditional and unscientific methods in allocating nurses to shifts. The present study is designed to determine the minimum number of nurse required according to the number of patients referred at different times, determine the shift schedule with the least required hours and schedule shifts for nurses in each shift with the lowest cost for the emergency department. The research method of the present study is of the mathematical modeling and research community, patients referring to the emergency department and nurses of a medical center. Data analysis is a combination of predictive methods, queuing theory models, and linear numerical programming. To predict the number of patients referring to the emergency, the time series method and ARIMA tools were used, and the M/M/C/K model was used to examine the queue system with limited capacity. One of the most important results of this study is to determine the maximum number of nurses available in each shift. Another result of this study is the comparison of the performance of each of the meta-heuristic Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Bee Algorithm (BA) with respect to the defined indicators.