Search results for: Multi criteria optimization

Authors: Yanan Yang, Zhigang Wang, Xiang Chen

Abstract:

This paper presents the use of Legendre pseudospectral method for the optimization of finite-thrust orbital transfer for spacecrafts. In order to get an accurate solution, the System-s dynamics equations were normalized through a dimensionless method. The Legendre pseudospectral method is based on interpolating functions on Legendre-Gauss-Lobatto (LGL) quadrature nodes. This is used to transform the optimal control problem into a constrained parameter optimization problem. The developed novel optimization algorithm can be used to solve similar optimization problems of spacecraft finite-thrust orbital transfer. The results of a numerical simulation verified the validity of the proposed optimization method. The simulation results reveal that pseudospectral optimization method is a promising method for real-time trajectory optimization and provides good accuracy and fast convergence.

Keywords: Finite-thrust, Orbital transfer, Legendre pseudospectral method

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Authors: Saif Ullah, Guan Zailin, Xu Xianhao, He Zongdong, Wang Baoxi

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Assembly line balancing problem is aimed to divide the tasks among the stations in assembly lines and optimize some objectives. In assembly lines the workload on stations is different from each other due to different tasks times and the difference in workloads between stations can cause blockage or starvation in some stations in assembly lines. Buffers are used to store the semi-finished parts between the stations and can help to smooth the assembly production. The assembly line balancing and buffer sizing problem can affect the throughput of the assembly lines. Assembly line balancing and buffer sizing problems have been studied separately in literature and due to their collective contribution in throughput rate of assembly lines, balancing and buffer sizing problem are desired to study simultaneously and therefore they are considered concurrently in current research. Current research is aimed to maximize throughput, minimize total size of buffers in assembly line and minimize workload variations in assembly line simultaneously. A multi objective optimization objective is designed which can give better Pareto solutions from the Pareto front and a simple example problem is solved for assembly line balancing and buffer sizing simultaneously. Current research is significant for assembly line balancing research and it can be significant to introduce optimization approaches which can optimize current multi objective problem in future.

Keywords: Assembly line balancing, Buffer sizing, Pareto solutions.

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Authors: Chiharu Okuma, Jun Murakami, Naoki Yamamoto

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In digital signal processing it is important to approximate multi-dimensional data by the method called rank reduction, in which we reduce the rank of multi-dimensional data from higher to lower. For 2-dimennsional data, singular value decomposition (SVD) is one of the most known rank reduction techniques. Additional, outer product expansion expanded from SVD was proposed and implemented for multi-dimensional data, which has been widely applied to image processing and pattern recognition. However, the multi-dimensional outer product expansion has behavior of great computation complex and has not orthogonally between the expansion terms. Therefore we have proposed an alterative method, Third-order Orthogonal Tensor Product Expansion short for 3-OTPE. 3-OTPE uses the power method instead of nonlinear optimization method for decreasing at computing time. At the same time the group of B. D. Lathauwer proposed Higher-Order SVD (HOSVD) that is also developed with SVD extensions for multi-dimensional data. 3-OTPE and HOSVD are similarly on the rank reduction of multi-dimensional data. Using these two methods we can obtain computation results respectively, some ones are the same while some ones are slight different. In this paper, we compare 3-OTPE to HOSVD in accuracy of calculation and computing time of resolution, and clarify the difference between these two methods.

Keywords: Singular value decomposition (SVD), higher-order SVD (HOSVD), higher-order tensor, outer product expansion, power method.

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Authors: M. Mahdavi, H. Monsef, A. Bagheri

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Discrete particle swarm optimization (DPSO) is a powerful stochastic evolutionary algorithm that is used to solve the large-scale, discrete and nonlinear optimization problems. However, it has been observed that standard DPSO algorithm has premature convergence when solving a complex optimization problem like transmission expansion planning (TEP). To resolve this problem an advanced discrete particle swarm optimization (ADPSO) is proposed in this paper. The simulation result shows that optimization of lines loading in transmission expansion planning with ADPSO is better than DPSO from precision view point.

Keywords: ADPSO, TEP problem, Lines loading optimization.

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Authors: Sidhartha Panda, N.P.Padhy, R.N.Patel

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This paper presents a procedure for modeling and tuning the parameters of Thyristor Controlled Series Compensation (TCSC) controller in a multi-machine power system to improve transient stability. First a simple transfer function model of TCSC controller for stability improvement is developed and the parameters of the proposed controller are optimally tuned. Genetic algorithm (GA) is employed for the optimization of the parameter-constrained nonlinear optimization problem implemented in a simulation environment. By minimizing an objective function in which the oscillatory rotor angle deviations of the generators are involved, transient stability performance of the system is improved. The proposed TCSC controller is tested on a multi-machine system and the simulation results are presented. The nonlinear simulation results validate the effectiveness of proposed approach for transient stability improvement in a multimachine power system installed with a TCSC. The simulation results also show that the proposed TCSC controller is also effective in damping low frequency oscillations.

Keywords: Genetic algorithm, TCSC, transient stability, multimachinepower system.

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Authors: Mogeeb A. Ahmed, Mona F. Taher, Sayed M. Metwalli

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Shape memory alloy (SMA) actuators have found a wide range of applications due to their unique properties such as high force, small size, lightweight and silent operation. This paper presents the development of compact (SMA) actuator and cooling system in one unit. This actuator is developed for multi-fingered hand. It consists of nickel-titanium (Nitinol) SMA wires in compact forming. The new arrangement insulates SMA wires from the human body by housing it in a heat sink and uses a thermoelectric device for rejecting heat to improve the actuator performance. The study uses optimization methods for selecting the SMA wires geometrical parameters and the material of a heat sink. The experimental work implements the actuator prototype and measures its response.

Keywords: Optimization, Prosthetic hand, Shape memory alloy, Thermoelectric device, Actuator system

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Authors: M. Suresh Kumar, K. Ramani

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This paper proposed the comparison made between Multi-Carrier Pulse Width Modulation, Sinusoidal Pulse Width Modulation and Selective Harmonic Elimination Pulse Width Modulation technique for minimization of Total Harmonic Distortion in Cascaded H-Bridge Multi-Level Inverter. In Multicarrier Pulse Width Modulation method by using Alternate Position of Disposition scheme for switching pulse generation to Multi-Level Inverter. Another carrier based approach; Sinusoidal Pulse Width Modulation method is also implemented to define the switching pulse generation system in the multi-level inverter. In Selective Harmonic Elimination method using Genetic Algorithm and Particle Swarm Optimization algorithm for define the required switching angles to eliminate low order harmonics from the inverter output voltage waveform and reduce the total harmonic distortion value. So, the results validate that the Selective Harmonic Elimination Pulse Width Modulation method does capably eliminate a great number of precise harmonics and minimize the Total Harmonic Distortion value in output voltage waveform in compared with Multi-Carrier Pulse Width Modulation method, Sinusoidal Pulse Width Modulation method. In this paper, comparison of simulation results shows that the Selective Harmonic Elimination method can attain optimal harmonic minimization solution better than Multi-Carrier Pulse Width Modulation method, Sinusoidal Pulse Width Modulation method.

Keywords: Multi-level inverter, Selective Harmonic Elimination Pulse Width Modulation, Multi-Carrier Pulse Width Modulation, Total Harmonic Distortion, Genetic Algorithm.

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Authors: M. Rokonuzzaman, Z. Gürdal

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This paper describes the design optimization of ferrocement-laminated plate made up of reinforcing steel wire mesh(es) and cement mortar. For the improvement of the designing process, the plate is modeled as a multi-layer medium, dividing the ferrocement plate into layers of mortar and ferrocement. The mortar layers are assumed to be isotropic in nature and the ferrocement layers are assumed to be orthotropic. The ferrocement layers are little stiffer, but much more costlier, than the mortar layers due the presence of steel wire mesh. The optimization is performed for minimum weight design of the laminate using a genetic algorithm. The optimum designs are discussed for different plate configurations and loadings, and it is compared with the worst designs obtained at the final generation. The paper provides a procedure for the designers in decision-making process.

Keywords: Buckling, Ferrocement-Laminated Plate, Genetic Algorithm, Plate Theory.

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Authors: Myeongjin Park, Ohmin Kwon, Juhyun Park, Sangmoon Lee

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This paper proposes improved delay-dependent stability conditions of the linear time-delay systems of neutral type. The proposed methods employ a suitable Lyapunov-Krasovskii’s functional and a new form of the augmented system. New delay-dependent stability criteria for the systems are established in terms of Linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. Numerical examples showed that the proposed method is effective and can provide less conservative results.

Keywords: Neutral systems, Time-delay, Stability, Lyapunovmethod, LMI.

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Authors: C. Ardil

Abstract:

The decision to select an unmanned combat aircraft is complicated since several options and conflicting criteria must be considered at simultaneously. When making multiple criteria decision, it is important to consider the selected evaluation criteria, including priceability, payloadability, stealthability, speedability , and survivability. The fundamental goal of the study is to select the best unmanned combat aircraft by taking these evaluation criteria into account. The optimal aircraft was chosen using the fuzzy proximity measure method, which enables decision-makers to designate preferences as standard fuzzy set numbers during the multiple criteria decision-making process. To assess the applicability of the proposed approach, a numerical example is provided. Finally, by comparing determined unmanned combat aircraft, the proposed method produced a successful application, and the best aircraft was selected.

Keywords: standard fuzzy sets (SFS), unmanned combat aircraft selection, multiple criteria decision making (MCDM), multiple criteria group decision making (MCGDM), proximity measure method (PMM)

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Authors: A. Vasan

Abstract:

Particle swarm optimization (PSO) technique is applied to design the water distribution pipeline network. A simulation-optimization model is formulated with the objective of minimizing cost and is applied to a benchmark water distribution system optimization problem. The benchmark problem taken for the application of PSO technique to optimize the pipe size of the water distribution network is New York City water supply system problem. The results from the analysis infer that PSO is a potential alternative optimization technique when compared to other heuristic techniques for optimal sizing of water distribution systems.

Keywords: Water distribution systems, Optimization, Particle swarm optimization, Swarm intelligence, New York water supply system.

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Authors: Badr M. Alshammari, T. Guesmi

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In recent years, the combined economic and emission power dispatch is one of the main problems of electrical power system. It aims to schedule the power generation of generators in order to minimize cost production and emission of harmful gases caused by fossil-fueled thermal units such as CO, CO₂, NO_x, and SO₂. To solve this complicated multi-objective problem, an improved version of the particle swarm optimization technique that includes non-dominated sorting concept has been proposed. Valve point loading effects and system losses have been considered. The three-unit and ten-unit benchmark systems have been used to show the effectiveness of the suggested optimization technique for solving this kind of nonconvex problem. The simulation results have been compared with those obtained using genetic algorithm based method. Comparison results show that the proposed approach can provide a higher quality solution with better performance.

Keywords: Power dispatch, valve point loading effects, multiobjective optimization, Pareto solutions.

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Authors: S. Adhirai, R. P. Mahapatra, Paramjit Singh

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Optimization is an important tool in making decisions and in analysing physical systems. In mathematical terms, an optimization problem is the problem of finding the best solution from among the set of all feasible solutions. The paper discusses the Whale Optimization Algorithm (WOA), and its applications in different fields. The algorithm is tested using MATLAB because of its unique and powerful features. The benchmark functions used in WOA algorithm are grouped as: unimodal (F1-F7), multimodal (F8-F13), and fixed-dimension multimodal (F14-F23). Out of these benchmark functions, we show the experimental results for F7, F11, and F19 for different number of iterations. The search space and objective space for the selected function are drawn, and finally, the best solution as well as the best optimal value of the objective function found by WOA is presented. The algorithmic results demonstrate that the WOA performs better than the state-of-the-art meta-heuristic and conventional algorithms.

Keywords: Optimization, optimal value, objective function, optimization problems, meta-heuristic optimization algorithms, Whale Optimization Algorithm, Implementation, MATLAB.

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Authors: D. Danesh, M. J. Ryan, A. Abbasi

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Project Portfolio Management (PPM) is an essential component of an organisation’s strategic procedures, which requires attention of several factors to envisage a range of long-term outcomes to support strategic project portfolio decisions. To evaluate overall efficiency at the portfolio level, it is essential to identify the functionality of specific projects as well as to aggregate those findings in a mathematically meaningful manner that indicates the strategic significance of the associated projects at a number of levels of abstraction. PPM success is directly associated with the quality of decisions made and poor judgment increases portfolio costs. Hence, various Multi-Criteria Decision Making (MCDM) techniques have been designed and employed to support the decision-making functions. This paper reviews possible options to enhance the decision-making outcomes in organisational portfolio management processes using the Analytic Hierarchy Process (AHP) both from academic and practical perspectives and will examine the usability, certainty and quality of the technique. The results of the study will also provide insight into the technical risk associated with current decision-making model to underpin initiative tracking and strategic portfolio management.

Keywords: Analytic hierarchy process, decision support systems, multi-criteria decision-making, project portfolio management.

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Authors: Ibrahim Cil, Onur Kurtcu, H. Ibrahim Demir, Furkan Yener, Yusuf. S. Turkan, Muharrem Unver, Ramazan Evren

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Fuzzy AHP (Analytic Hierarchy Process) method is decision-making way at the end of integrating the current AHP method with fuzzy structure. In this study, the processes of production planning, inventory management and purchasing department of a system were analysed and were requested to decide the performance criteria of each area. At this point, the current work processes were analysed by various decision-makers and comparing each criteria by giving points according to 1-9 scale were completed. The criteria were listed in order to their weights by using Fuzzy AHP approach and top three performance criteria of each department were determined. After that, the performance criteria of supply chain consisting of three departments were asked to determine. The processes of each department were compared by decision-makers at the point of building the supply chain performance system and getting the performance criteria. According to the results, the criteria of performance system of supply chain by using Fuzzy AHP were determined for which will be used in the supply chain performance system in the future.

Keywords: AHP, fuzzy, performance evaluation, supply chain.

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Authors: S. K. Pillai, M. K. Jeyakumar

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Estimation of model parameters is necessary to predict the behavior of a system. Model parameters are estimated using optimization criteria. Most algorithms use historical data to estimate model parameters. The known target values (actual) and the output produced by the model are compared. The differences between the two form the basis to estimate the parameters. In order to compare different models developed using the same data different criteria are used. The data obtained for short scale projects are used here. We consider software effort estimation problem using radial basis function network. The accuracy comparison is made using various existing criteria for one and two predictors. Then, we propose a new criterion based on linear least squares for evaluation and compared the results of one and two predictors. We have considered another data set and evaluated prediction accuracy using the new criterion. The new criterion is easy to comprehend compared to single statistic. Although software effort estimation is considered, this method is applicable for any modeling and prediction.

Keywords: Software effort estimation, accuracy, Radial Basis Function, linear least squares.

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Authors: Mehtap Dursun, E. Ertugrul Karsak, Melis Almula Karadayi

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The management of the health-care wastes is one of the most important problems in Istanbul, a city with more than 12 million inhabitants, as it is in most of the developing countries. Negligence in appropriate treatment and final disposal of the healthcare wastes can lead to adverse impacts to public health and to the environment. This paper employs a fuzzy multi-criteria group decision making approach, which is based on the principles of fusion of fuzzy information, 2-tuple linguistic representation model, and technique for order preference by similarity to ideal solution (TOPSIS), to evaluate health-care waste (HCW) treatment alternatives for Istanbul. The evaluation criteria are determined employing nominal group technique (NGT), which is a method of systematically developing a consensus of group opinion. The employed method is apt to manage information assessed using multigranularity linguistic information in a decision making problem with multiple information sources. The decision making framework employs ordered weighted averaging (OWA) operator that encompasses several operators as the aggregation operator since it can implement different aggregation rules by changing the order weights. The aggregation process is based on the unification of information by means of fuzzy sets on a basic linguistic term set (BLTS). Then, the unified information is transformed into linguistic 2-tuples in a way to rectify the problem of loss information of other fuzzy linguistic approaches.

Keywords: Group decision making, health care waste management, multi-criteria decision making, OWA, TOPSIS, 2-tuple linguistic representation

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Authors: Refaat H. Abd El Razek, Ahmed M. Diab, Sherif M. Hafez, Remon F. Aziz

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Time-Cost Optimization "TCO" is one of the greatest challenges in construction project planning and control, since the optimization of either time or cost, would usually be at the expense of the other. Since there is a hidden trade-off relationship between project and cost, it might be difficult to predict whether the total cost would increase or decrease as a result of the schedule compression. Recently third dimension in trade-off analysis is taken into consideration that is quality of the projects. Few of the existing algorithms are applied in a case of construction project with threedimensional trade-off analysis, Time-Cost-Quality relationships. The objective of this paper is to presents the development of a practical software system; that named Automatic Multi-objective Typical Construction Resource Optimization System "AMTCROS". This system incorporates the basic concepts of Line Of Balance "LOB" and Critical Path Method "CPM" in a multi-objective Genetic Algorithms "GAs" model. The main objective of this system is to provide a practical support for typical construction planners who need to optimize resource utilization in order to minimize project cost and duration while maximizing its quality simultaneously. The application of these research developments in planning the typical construction projects holds a strong promise to: 1) Increase the efficiency of resource use in typical construction projects; 2) Reduce construction duration period; 3) Minimize construction cost (direct cost plus indirect cost); and 4) Improve the quality of newly construction projects. A general description of the proposed software for the Time-Cost-Quality Trade-Off "TCQTO" is presented. The main inputs and outputs of the proposed software are outlined. The main subroutines and the inference engine of this software are detailed. The complexity analysis of the software is discussed. In addition, the verification, and complexity of the proposed software are proved and tested using a real case study.

Keywords: Project management, typical (repetitive) large scale projects, line of balance, multi-objective optimization, genetic algorithms, time-cost-quality trade-offs.

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Authors: H. El Ayech, A. Trabelsi

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In this article we are going to discuss the improvement of the multi classes- classification problem using multi layer Perceptron. The considered approach consists in breaking down the n-class problem into two-classes- subproblems. The training of each two-class subproblem is made independently; as for the phase of test, we are going to confront a vector that we want to classify to all two classes- models, the elected class will be the strongest one that won-t lose any competition with the other classes. Rates of recognition gotten with the multi class-s approach by two-class-s decomposition are clearly better that those gotten by the simple multi class-s approach.

Keywords: Artificial neural network, letter-recognition, Multi class Classification, Multi Layer Perceptron.

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Authors: Parvaneh Shabanzadeh, Malik Hj Abu Hassan, Leong Wah June, Maryam Mohagheghtabar

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Clustering is one of an interesting data mining topics that can be applied in many fields. Recently, the problem of cluster analysis is formulated as a problem of nonsmooth, nonconvex optimization, and an algorithm for solving the cluster analysis problem based on nonsmooth optimization techniques is developed. This optimization problem has a number of characteristics that make it challenging: it has many local minimum, the optimization variables can be either continuous or categorical, and there are no exact analytical derivatives. In this study we show how to apply a particular class of optimization methods known as pattern search methods to address these challenges. These methods do not explicitly use derivatives, an important feature that has not been addressed in previous studies. Results of numerical experiments are presented which demonstrate the effectiveness of the proposed method.

Keywords: Clustering functions, Non-smooth Optimization, Nonconvex Optimization, Pattern Search Method.

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Authors: B. Mukanova, N. Glazyrina, S. Glazyrin

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The formulated problem of optimization of the technological process of water treatment for thermal power plants is considered in this article. The problem is of multiparametric nature. To optimize the process, namely, reduce the amount of waste water, a new technology was developed to reuse such water. A mathematical model of the technology of wastewater reuse was developed. Optimization parameters were determined. The model consists of a material balance equation, an equation describing the kinetics of ion exchange for the non-equilibrium case and an equation for the ion exchange isotherm. The material balance equation includes a nonlinear term that depends on the kinetics of ion exchange. A direct problem of calculating the impurity concentration at the outlet of the water treatment plant was numerically solved. The direct problem was approximated by an implicit point-to-point computation difference scheme. The inverse problem was formulated as relates to determination of the parameters of the mathematical model of the water treatment plant operating in non-equilibrium conditions. The formulated inverse problem was solved. Following the results of calculation the time of start of the filter regeneration process was determined, as well as the period of regeneration process and the amount of regeneration and wash water. Multi-parameter optimization of water treatment process for thermal power plants allowed decreasing the amount of wastewater by 15%.

Keywords: Direct problem, multiparametric optimization, optimization parameters, water treatment.

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Authors: Naveen Virmani, Rajeev Saha, Rajeshwar Sahai

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There is drastic change in manufacturing era in last two decades. It has become mandatory for the industries to become aware of latest and advanced manufacturing technologies and strategies. Leagile manufacturing focuses on minimizing the wastes and meeting customers’ requirements in minimum time possible. However, it becomes difficult to implement all leagile tools simultaneously in industry. In this paper, 17 main criteria of leagile manufacturing have been found and DEMATEL (Decision Making Trial and Evaluation Laboratory) approach has been applied to analyze importance of criteria and casual relations among these criteria.

Keywords: Agile, DEMATEL approach, lean, leagile manufacturing.

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Authors: Akbar H. Borzabadi, Omid S. Fard

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In this paper we introduce an approach via optimization methods to find approximate solutions for nonlinear Fredholm integral equations of the first kind. To this purpose, we consider two stages of approximation. First we convert the integral equation to a moment problem and then we modify the new problem to two classes of optimization problems, non-constraint optimization problems and optimal control problems. Finally numerical examples is proposed.

Keywords: Fredholm integral equation, Optimization method, Optimal control, Nonlinear and linear programming

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Authors: Wu Qinglong, Zhou Qicai, Xiong Xiaolei, Zhang Richeng

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In order to achieve the layout and size optimization of the web members of tower crane boom, a truss topology and cross section size optimization method based on continuum is proposed considering three typical working conditions. Firstly, the optimization model is established by replacing web members with web plates. And the web plates are divided into several sub-domains so that periodic soft kill option (SKO) method can be carried out for topology optimization of the slender boom. After getting the optimized topology of web plates, the optimized layout of web members is formed through extracting the principal stress distribution. Finally, using the web member radius as design variable, the boom compliance as objective and the material volume of the boom as constraint, the cross section size optimization mathematical model is established. The size optimization criterion is deduced from the mathematical model by Lagrange multiplier method and Kuhn-Tucker condition. By comparing the original boom with the optimal boom, it is identified that this optimization method can effectively lighten the boom and improve its performance.

Keywords: Tower crane boom, topology optimization, size optimization, periodic, soft kill option, optimization criterion.

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Authors: Ahmed Al-Ani

Abstract:

The Ant Colony Optimization (ACO) is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It has recently attracted a lot of attention and has been successfully applied to a number of different optimization problems. Due to the importance of the feature selection problem and the potential of ACO, this paper presents a novel method that utilizes the ACO algorithm to implement a feature subset search procedure. Initial results obtained using the classification of speech segments are very promising.

Keywords: Ant Colony Optimization, ant systems, feature selection, pattern recognition.

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Authors: Tommaso Adamo, Gianpaolo Ghiani, Antonio D. Grieco, Emanuela Guerriero

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Batch production plants provide a wide range of scheduling problems. In pharmaceutical industries a batch process is usually described by a recipe, consisting of an ordering of tasks to produce the desired product. In this research work we focused on pharmaceutical production processes requiring the culture of a microorganism population (i.e. bacteria, yeasts or antibiotics). Several sources of uncertainty may influence the yield of the culture processes, including (i) low performance and quality of the cultured microorganism population or (ii) microbial contamination. For these reasons, robustness is a valuable property for the considered application context. In particular, a robust schedule will not collapse immediately when a cell of microorganisms has to be thrown away due to a microbial contamination. Indeed, a robust schedule should change locally in small proportions and the overall performance measure (i.e. makespan, lateness) should change a little if at all. In this research work we formulated a constraint programming optimization (COP) model for the robust planning of antibiotics production. We developed a discrete-time model with a multi-criteria objective, ordering the different criteria and performing a lexicographic optimization. A feasible solution of the proposed COP model is a schedule of a given set of tasks onto available resources. The schedule has to satisfy tasks precedence constraints, resource capacity constraints and time constraints. In particular time constraints model tasks duedates and resource availability time windows constraints. To improve the schedule robustness, we modeled the concept of (a, b) super-solutions, where (a, b) are input parameters of the COP model. An (a, b) super-solution is one in which if a variables (i.e. the completion times of a culture tasks) lose their values (i.e. cultures are contaminated), the solution can be repaired by assigning these variables values with a new values (i.e. the completion times of a backup culture tasks) and at most b other variables (i.e. delaying the completion of at most b other tasks). The efficiency and applicability of the proposed model is demonstrated by solving instances taken from a real-life pharmaceutical company. Computational results showed that the determined super-solutions are near-optimal.

Keywords: Constraint programming, super-solutions, robust scheduling, batch process, pharmaceutical industries.

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Authors: C. Ardil, A. M. Pashaev, R.A. Sadiqov, P. Abdullayev

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In this paper, multiple criteria decision making analysis technique, is presented for ranking and selection of a set of determined alternatives - fighter aircraft - which are associated with a set of decision factors. In fighter aircraft design, conflicting decision criteria, disciplines, and technologies are always involved in the design process. Multiple criteria decision making analysis techniques can be helpful to effectively deal with such situations and make wise design decisions. Multiple criteria decision making analysis theory is a systematic mathematical approach for dealing with problems which contain uncertainties in decision making. The feasibility and contributions of applying the multiple criteria decision making analysis technique in fighter aircraft selection analysis is explored. In this study, an integrated framework incorporating multiple criteria decision making analysis technique in fighter aircraft analysis is established using entropy objective weighting method. An improved integrated multiple criteria decision making analysis method is utilized to aggregate the multiple decision criteria into one composite figure of merit, which serves as an objective function in the decision process. Therefore, it is demonstrated that the suitable multiple criteria decision making analysis method with decision solution provides an effective objective function for the decision making analysis. Considering that the inherent uncertainties and the weighting factors have crucial decision impacts on the fighter aircraft evaluation, seven fighter aircraft models for the multiple design criteria in terms of the weighting factors are constructed. The proposed multiple criteria decision making analysis model is based on integrated entropy index procedure, and additive multiple criteria decision making analysis theory. Hence, the applicability of proposed technique for fighter aircraft selection problem is considered. The constructed multiple criteria decision making analysis model can provide efficient decision analysis approach for uncertainty assessment of the decision problem. Consequently, the fighter aircraft alternatives are ranked based their final evaluation scores, and sensitivity analysis is conducted.

Keywords: Fighter Aircraft, Fighter Aircraft Selection, Multiple Criteria Decision Making, Multiple Criteria Decision Making Analysis, MCDMA

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Authors: T. Niknam, M. Nayeripour, B.Bahmani Firouzi

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Clustering techniques have received attention in many areas including engineering, medicine, biology and data mining. The purpose of clustering is to group together data points, which are close to one another. The K-means algorithm is one of the most widely used techniques for clustering. However, K-means has two shortcomings: dependency on the initial state and convergence to local optima and global solutions of large problems cannot found with reasonable amount of computation effort. In order to overcome local optima problem lots of studies done in clustering. This paper is presented an efficient hybrid evolutionary optimization algorithm based on combining Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), called PSO-ACO, for optimally clustering N object into K clusters. The new PSO-ACO algorithm is tested on several data sets, and its performance is compared with those of ACO, PSO and K-means clustering. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handing data clustering.

Keywords: Ant Colony Optimization (ACO), Data clustering, Hybrid evolutionary optimization algorithm, K-means clustering, Particle Swarm Optimization (PSO).

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Authors: Hyunsang Park, Kwangwoo Lee, Yunho Lee, Seungjoo Kim, Dongho Won

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The online office is one of web application. We can easily use the online office through a web browser with internet connected PC. The online office has the advantage of using environment regardless of location or time. When users want to use the online office, they access the online office server and use their content. However, recently developed and launched online office has the weakness of insufficient consideration. In this paper, we analyze the security vulnerabilities of the online office. In addition, we propose the evaluation criteria to make secure online office using Common Criteria. This evaluation criteria can be used to establish trust between the online office server and the user. The online office market will be more active than before.

Keywords: Online Office, Vulnerabilities, CommonCriteria(CC)

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Authors: C. Thanga Raj, S. P. Srivastava, Pramod Agarwal

Abstract:

This paper presents an optimal design of poly-phase induction motor using Quadratic Interpolation based Particle Swarm Optimization (QI-PSO). The optimization algorithm considers the efficiency, starting torque and temperature rise as objective function (which are considered separately) and ten performance related items including harmonic current as constraints. The QI-PSO algorithm was implemented on a test motor and the results are compared with the Simulated Annealing (SA) technique, Standard Particle Swarm Optimization (SPSO), and normal design. Some benchmark problems are used for validating QI-PSO. From the test results QI-PSO gave better results and more suitable to motor-s design optimization. Cµ code is used for implementing entire algorithms.

Keywords: Design, harmonics, induction motor, particle swarm optimization

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