Showing 5 results for Multiobjective Optimization
Volume 11, Issue 3 (10-2011)
Abstract
Abstract This paper presents a multiobjective power control algorithm that updates the transmitted power based on local information. The proposed algorithm is expanded by using multiobjective optimization schemes. The objectives to be optimized in this paper are determined so as to reduce the SINR fluctuations as well as maintaining the SINR to an acceptable level with minimizing an average transmitted power. The convergence properties of the proposed algorithm are studied theoretically and with numerical simulations. The results indicate that the algorithm converges more rapidly and has lower average transmitted power than other existing algorithms. The current study also suggests a practical version of the proposed algorithm and compares it to the existing totally distributed bang-bang power control (B-BPC) or fixed step power control (FSPC) and multiobjective totally distributed power control (MOTDPC) algorithms. Numerical results show that the proposed algorithm is potentially much more efficient in terms of convergence speed and average consumption power than the other two algorithms.
Volume 16, Issue 1 (3-2016)
Abstract
Tanks in water distribution networks are used to store water for emergency conditions, fire flow demand and demand oscillations controll. Construction of tanks spends a lot of money and therefore using whole volume of tanks is essential while operation. Otherwise, if tank volume will be more or less than what is required during operation, tank reliability is reduced. Accordingly, in this paper, a new relationship for tank reliability according to water level variation in tanks is defined. Therefore, maximum water level in tanks is defined as the decision variable. The definition of tank reliability is as follows. At first, the values of maximum level for each tank is computed such a way that optimal use is provided from balancing volume of tanks. In fact, for these maximum level values, maximum reliability is acheived for each tank. Now if during optimization process, a value lower than these computed maximum level is selected for decision variables, tank reliability is reduced. To compute the value of tank reliability, the values of tank water level for the selected decision variables is devided by the values of tank water level for maximum tank reliability. Also, because water level variation can effect on pressure and water age in demand nodes, this effect is investigated by considering hydraulic and quality reliability. In fact, variation of water level in tanks changes node demand pressures and in result actual node demands. Also, variation of water level or on the other hand variation of storage volume affects on water age in demand nodes. Besides, in order to investigate the simultaneous effect of water level variation on hydraulic and quality reliability, a relationship is also defined for integrated reliability. Definition of integrated reliability is to investigate whether there is optimum maximum tank level values that both hydraulic and quality reliability is improved simultaneusly while tank construction costs is minimum. Optimal management of tanks in water distribution networks to provide required water of consumers with desired quality is of high importance. To acheive this, optimization is defined as a powerful tool. In this paper, by focusing on operation phase, multiobjective optimization of water distribution performance is performed in which tank costs is considered as the first objective and tank reliability, node hydraulic reliability, node water age reliability and integrated reliability is considered as the second objective. Ant colony algorithm is codified in Microsoft Visual C++ for optimization due to its simplicity and high performance. The validity of the edited algorithm is tested on mathematical functions and proved to be applicable on water distribution networks. The created trade-off curve from multiobjective optimization helps the decision makers to select the top choice based on the importance of their own criterion whether it is hydraulic or quality.
F. Rashidi, H. Rashidi,
Volume 19, Issue 2 (2-2019)
Abstract
In this paper, using a thermodynamic rules, a multigeneration energy system with an initial stimulus of microturbine has been modeled. Then, using the concept of exergy and applying economic and environmental functions, exergy efficiency and total cost rate are calculated as two objective functions. Due to the contradiction of the objective functions, a multiobjective firefly algorithm is used to optimize the system. To accelerate the process of optimization and to prevent algorithm capture in local optimizations, new algorithms have been added to the innovative algorithm. The result of applying the algorithm on the multigeneration energy system will result in a set of Pareto-optimal solutions, indicating the compromise between the target functions. A fuzzy decision making based on max-min approach is used to select the desired solution between the Pareto-optimal solutions. In order to evaluate the efficiency of the proposed optimization algorithm, the results of this algorithm are compared with two particle swarm optimization algorithms and multi-objective genetic algorithm. Based on the results of system optimization, the exergy efficiency can increase up to 69%. Also, considering the total cost rate of the system as the only target function, this can be reduced to 572$/h.
M. Mohammadi Soleymani , S. Mirzadeh,
Volume 20, Issue 9 (9-2020)
Abstract
Due to the importance of tumbling mills in processing industries and factories and the lack of an acceptable model for identifying and predicting their performance, it is necessary to optimize these complexes, non-linear, and large systems. This paper aimed to study multi-objective optimization of operating parameters in a tumbling mill. To evaluate the effects of the mill working parameters such as mill speed, ball filling, slurry concentration, and slurry filling on grinding process, power draw, wear of lifters and size distribution of the mill product, it was tried to manufacture a pilot model with a smaller size than the actual mill. For this aim, a mill with 1×0.5m was implemented. The feed of the mill is copper ore with a size smaller than 1 inch. The experiments were done at 65 to 85% of the critical speed. In addition, the combination of the balls was used as grinding media with 10 to 30% of the total volume of the mill. Slurry concentration is 40 to 80% (the weight fraction of solid in slurry) and the slurry filling is between 0.5 and 2.5. In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) based multi-objective optimization (NSGA-II) of tumbling mill is done. Level diagrams are used to select the best solution from the Pareto front. The results showed that the best grinding occurs at 70-80% of the critical speed and ball filling of 15-20%. Optimized grinding was observed when the slurry volume is 1-1.5 times of the ball bed voidage volume and the slurry concentration is between 60 and 70%.
Volume 23, Issue 1 (3-2023)
Abstract
Non-destructive damage detection methods analyze the output data collected from sensors to track the changes in the dynamic characteristics of the structure and detect the occurrence of damages. continuous recording and analysis of data to be aware of its safety and serviceability requires a network of sensors that are selected optimally and intelligently. Saving the cost of equipping the structure with this optimal sensor network, along with reducing damage detection error, has turned the issue of selecting the number and location of sensors into an optimization problem from an economic and functional point of view. Model order reduction methods along with optimization tools can play an effective role in selecting the master degrees of freedom. These methods divide the degrees of freedom of the structure into two groups of master and slave degrees of freedom. The master degrees of freedom appear in the process of calculating the mode shapes and natural frequencies, and the slave degrees of freedom are excluded from the equations. Finally, using the transfer matrix, the complete mode shapes are calculated using the mode shapes of the master degrees of freedom. In this paper, considering the key role of modal parameter recognition in structural damage detection, the performance and accuracy of different methods of dynamical model order reduction in the optimal sensor placement problem was studied. The 2d truss stucture and two-dimensional shear frame are modeled and analyzed. The sensor placement should be considered in such a way that the mode shape identification is done with sufficient accuracy and proper recognition. One of the effective tools in order to achieve this goal is to use the capabilities of metaheuristic optimization algorithms along with the capability of dynamic model reduction methods in the stage of identifying the mode shapes and before identifying the damages of structure. Combining model order reduction methods with metaheuristic optimization algorithms so that the selection of appropriate degrees of freedom for sensor installation (master degrees of freedom) leads to the most accurate identification of structural modes shapes is one of the main objectives of this study. The objective functions selected based on modal assurance criteria (MAC) and Fisher information matrix (FIM) and the capabilities of multi objective particle swarm optimization algorithm (MOPSO) to achieve the optimal number and proper arrangement of sensors are used to better identify the structural mode shapes and proper arrangement of sensors and obtained for system identification purposes. The results report better performance of SEREP and IDC methods in selection of master degrees of freedom and identifying the mode shapes of 2d truss and shear frame structures. According to the modeling and analysis performed for optimal placement of sensors using different model reduction methods, it can be concluded that the improved dynamic condensation (IDC) method is more accurate than other methods in identifying shear frame mode shapes and gives a smaller maximum non-diagonal MAC matrix element. Also, as the number of sensors increases due to the addition of information to the Fisher matrix, the Fisher matrix determinant increases and second objective function decreases. On the other hand, by reducing the number of available sensors, a limited number of modes can be detected. In this case, the best way to receive the structural modal information would be to place more available sensors on the lower and upper floors of the shear frame. Eventually, it can be concluded that the use of IDC and SEREP methods to select master degrees of freedom for sensor installation leads to better identification of modal parameters of the structure. Therefore, the capabilities of these methods can be used to identify damage in structures with a limited number of sensors.
