multiobjective optimization

The main objective of this article is to apply the control allocation approach for the landing phase of the F/A-18 aircraft. For this purpose, the non-linear three-degree-of-freedom model of the aircraft is used, and the intelligent control allocation approach, based on fuzzy logic, is utilized to design the longitudinal flight control system. The actuators involved in the aircraft landing process are the elevator angle and the thrust vector control angle of the aircraft engine. By allocating control signals between the two mentioned actuators, the plane starts the process of lowering the height and finally reaches the ground level. To improve the efficiency of the fuzzy controller, reduce the control effort and increase the accuracy and quality of the landing, the genetic algorithm based on the NSGA-II method is used and the variables of the fuzzy controller are modified. The results obtained from the simulation show that the proposed control allocation approach has a high ability to control and stabilize the aircraft in the landing process. Also, the output variables converge to a desired value and the aircraft completes the landing process with proper accuracy and low control effort.

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.

The main problem of water resources planning is the inappropriate allocation between different consumers. Water allocation planning is a complex, multi-variable, and multi-constraint problem, which requires advanced optimization methods to be solved. Classical optimization methods are facing some limitations such as being trapped in local optimum points, and difficulties in handling different variables. In this paper two of these methods including particle swarm optimization, PSO and multiobjective non-dominated sorting genetic algorithms, NGGAII were explored and their efficiency in optimization water reservoir operation problems is compared. Dealing with the necessary of multiobjective programing accuracy, two single objective models was developed separately using PSO to verify the NSGAII results.

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%.

Hirmand River is the main drainage system in the southern part of Afghanistan and has a significant impact on the socio-economic life of Sistan region. The assessment of water potential of Hirmand River is essential for long term period, which requires the use of rainfall-runoff model. The present study focuses on simulating the daily discharge of the upper Helmand basin of Afghanistan using the conceptual model FLEX in combination with multi-objective optimization, which has not been considered in previous studies. At first, the discharge was evaluated integrally by lumped model considering one objection function. Then it was simulated using semi-distributed model by adding the logarithm of the flow to the objection function. The results of this study indicates that the model is efficient in the semi-distributed mode by using two objection functions for calibration of parameters. The coefficients of NS, NS-log in the calibration and validation period were estimated to be 0.86, 0.92, 0.76 and 0.81, respectively. Pareto front analysis gave the best optimum set for each parameter used in model simulation. The model gives satisfactory results based on monthly calibration and can be used to investigate the drought and climate change research of studied basin.

In this paper, the automatic fuzzy clustering is presented in multiobjective optimization framework. Two objectives based on compactness and overlap-separation measures are used as the objective functions which are optimized simultaneously. The proposed optimization problem is a nonlinear and non-convex multi-objective optimization and accordingly, a grey wolf based optimization algorithm is proposed for solving it. For accelerating the optimization process and preventing local optimum trapping, new heuristic approaches are included to the original algorithm. Solving the multi-objective optimization problem using the proposed algorithm, results in several Pareto optimal solutions showing compromise between objective functions. The final solution, among the generated plans, is selected using DB cluster validity index. The proposed method is applied on synthetic and real-life data sets. As shown in the experiments, the approach provides promising solutions in well-separated, hyperspherical and overlapping clusters. This is demonstrated by the comparison with existing single-objective and multiobjective clustering techniques.

Three objective functions including minimizing investment cost, minimizing lines congestion cost and maximizing the investment from private parties for constructing transmission lines are considered. The proposed optimization problem is a nonlinear and non-convex multi-objective optimization and accordingly, a bat inspired based algorithm is proposed for solving it. For accelerating the optimization process and preventing local optimum trapping, new heuristic approaches are included to the original algorithm. Solving the multi-objective optimization problem using the proposed algorithm, results in several optimal plans showing compromise between objective functions. The final plan, among the generated plans, is selected using a max-min fuzzy decision making. The proposed method is applied on the IEEE 24 bus test system and effectiveness of the proposed method is verified. Simulation results show that in the presence of various uncertainties, the proposed algorithm in addition to minimizing the investment and reducing the congestion costs identifies low risk and profitable transmission lines to be invested by private parties.

In this paper, crashworthiness analysis of a tapered sandwich column under oblique impact loading against a rigid wall is investigated by nonlinear finite element analysis.The energy absorption characteristics of honeycomb sandwich cylindrical columns in oblique crushing process depend greatly on the amount of material which participates in the plastic deformation. The interaction effects between the honeycomb and column walls greatly improve the energy absorption efficiency. The response surface method with cubic basis functions is employed to formulate specific energy absorption and peak crushing force, which reduces considerably the computational cost of crush simulations by finite element method. Based on the results of crash modeling, it is observed that the specific energy absorption has a decreasing trend by increasing the impact angle and decreasing the column thickness. On the other hand, the peek crushing force reduces when the impact angle and the column thickness are increased. Therefore, multiobjective optimization is done to maximize the specific energy absorption and minimize the peek crushing force at the same time. Furthermore, maximizing the specific energy absorption and maximizing impact load angle is performed. Finally, both local and global sensitivity analyses are employed to assess the effect of impact angle and thickness on the specific energy absorption and peak crushing force. The global sensitivity of the specific energy absorption with respect to the impact angle is observed to be more than the column thickness, while the peak crushing force has more global sensitivity to the column thickness compared to the impact angle.

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.

This papers proposes Modified CSS Approach for Envionmental/Economic load dispatch for multiobjective scheduling of power systems.

Stiffness and strength of a structure are two vital and important parameters. Always designers try to find a method to produce equilibrium between these. Recent decade's investigations focused on optimization of stiffness. In these structures we can not ensure that structure has adequate strength or not. So survey of strength is a very important topic. To maximize the strength we have to minimize the maximum stress of the structures. In all of old researches stress is a constraint rather than objective in the optimization processes. Although this may guarantee the stress within a prescribed constraint (generally less than some limit), it cannot always make the maximum stress a minimum. In optimization of a single criterion (stiffness or strength), other criteria can not be maximum. Hence, multicriteria optimization is a logical method. In this research, we use Evolutionary Structural Optimization (ESO) method for size optimization to maximize stiffness and strength of structures. To obtain global criteria, we use weighting method and weight factors. A linear combination of both weighted criteria produces global criteria. In order to calculate suitable weight factors for any structure that we use in research, we examined different value of these and optimize the structures for this values. By comparison of the results, we select the best value of weight factors that can optimize strength and stiffness of the structure. This research has two important a) Thickness distribution at different positions of structure according to stiffness or strength alone does not optimize other criteria in order to have a structure with optimum stiffness and strength; we have to optimize two criteria simultaneously.b) Programming ESO method for multicriteria optimization is very easy and simple. We used a MATLAB code for the FE analysis and a loop for repeating the optimization procedure. Optimization of stiffness alone does not lead to a structure with adequate and vice versa. In order to have a structure with adequate stiffness and strength, we have to optimized the these criteria simultaneously. ESO is a very easy method for multicriteria optimization and programming. We optimize topology of the structures in this research.

Land cover classification is one of the most important applications of polarimetric radar images, especially in urban areas. There are numerous features that can be extracted from these images for the use of their high potential, hence feature selection plays an important role in PolSAR image classification. In this study, three main steps are used to improve the classification: 1) feature extraction in the form of three categories, namely original data features, target decomposition features, and SAR discriminators; 2) selection of minimum number of features to achieve the high classification accuracy; and 3) classification using the best subset of features. In the proposed methods, NSGA-II multiobjective optimization algorithm is employed as the search tool and Support Vector Machine (SVM) or Adaptive Neuro Fuzzy Inference System (ANFIS) is used in the evaluation step. The implementation results on the Radarsat-2 San Francisco Bay image showed that the proposed methods outperform the other approaches tested against them.