In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Mo...In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Modified non-dominated sorting genetic algorithm II(NSGA II) was used for multi-objective optimization of automotive S-rail considering absorbed energy(E), peak crushing force(Fmax) and mass of the structure(W) as three conflicting objective functions. In the multi-objective optimization problem(MOP), E and Fmax are defined by polynomial models extracted using the software GEvo M based on train and test data obtained from numerical simulation of quasi-static crushing of the S-rail using ABAQUS. Finally, the nearest to ideal point(NIP)method and technique for ordering preferences by similarity to ideal solution(TOPSIS) method are used to find the some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. Results represent that the optimum design point obtained from TOPSIS method exhibits better trade-off in comparison with that of optimum design point obtained from NIP method.展开更多
A newly developed heuristic global optimization algorithm, called gravitational search algorithm （GSA）, was introduced and applied for simultaneously coordinated designing of power system stabilizer （PSS） and thyr...A newly developed heuristic global optimization algorithm, called gravitational search algorithm （GSA）, was introduced and applied for simultaneously coordinated designing of power system stabilizer （PSS） and thyristor controlled series capacitor （TCSC） as a damping controller in the multi-machine power system. The coordinated design problem of PSS and TCSC controllers over a wide range of loading conditions is formulated as a multi-objective optimization problem which is the aggregation of two objectives related to damping ratio and damping factor. By minimizing the objective function with oscillation, the characteristics between areas are contained and hence the interactions among the PSS and TCSC controller under transient conditions are modified. For evaluation of effectiveness and robustness of proposed controllers, the performance was tested on a weakly connected power system subjected to different disturbances, loading conditions and system parameter variations. The cigenvalues analysis and nonlinear simulation results demonstrate the high performance of proposed controllers which is able to provide efficient damping of low frequency oscillations.展开更多
Carbon nanotube(CNT)/polymer nanocomposites have vast application in industry because of their light mass and high strength. In this work, a cylindrical tube which is made up of functionally graded(FG) PmP V/CNT nanoc...Carbon nanotube(CNT)/polymer nanocomposites have vast application in industry because of their light mass and high strength. In this work, a cylindrical tube which is made up of functionally graded(FG) PmP V/CNT nanocomposite, is optimally designed for the purpose of torque transmission. The main confining parameters of a rotating shaft in torque transmission process are mass of the shaft, critical speed of rotation and critical buckling torque. It is required to solve a multi-objective optimization problem(MOP) to consider these three targets simultaneously in the process of design. The three-objective optimization problem for this case is defined and solved using a hybrid method of FEM and modified non-dominated sorting genetic algorithm(NSGA-II), by coupling two softwares, MATLAB and ABAQUS. Optimization process provides a set of non-dominated optimal design vectors. Then, two methods, nearest to ideal point(NIP) and technique for ordering preferences by similarity to ideal solution(TOPSIS), are employed to choose trade-off optimum design vectors. Optimum parameters that are obtained from this work are compared with the results of previous studies for similar cylindrical tubes made from composite or a hybrid of aluminum and composite that more than 20% improvement is observed in all of the objective functions.展开更多
A new strategy is presented to solve robust multi-physics multi-objective optimization problem known as improved multi-objective collaborative optimization （IMOCO） and its extension improved multi-objective robust c...A new strategy is presented to solve robust multi-physics multi-objective optimization problem known as improved multi-objective collaborative optimization （IMOCO） and its extension improved multi-objective robust collaborative （IMORCO）. In this work, the proposed IMORCO approach combined the IMOCO method, the worst possible point （WPP） constraint cuts and the Genetic algorithm NSGA-II type as an optimizer in order to solve the robust optimization problem of multi-physics of microstructures with uncertainties. The optimization problem is hierarchically decomposed into two levels： a microstructure level, and a disciplines levels, For validation purposes, two examples were selected： a numerical example, and an engineering example of capacitive micro machined ultrasonic transducers （CMUT） type. The obtained results are compared with those obtained from robust non-distributed and distributed optimization approach, non-distributed multi-objective robust optimization （NDMORO） and multi-objective collaborative robust optimization （McRO）, respectively. Results obtained from the application of the IMOCO approach to an optimization problem of a CMUT cell have reduced the CPU time by 44% ensuring a Pareto front close to the reference non-distributed multi-objective optimization （NDMO） approach （mahalanobis distance, D2M =0.9503 and overall spread, So=0.2309）. In addition, the consideration of robustness in IMORCO approach applied to a CMUT cell of optimization problem under interval uncertainty has reduced the CPU time by 23% keeping a robust Pareto front overlaps with that obtained by the robust NDMORO approach （D2M =10.3869 and So=0.0537）.展开更多
The present paper deals with a multiobjective optimization of truss topology by either Sequential Linear Programming （SLP） method or Linear Programming （LP） method. The ground structure approach is often used to s...The present paper deals with a multiobjective optimization of truss topology by either Sequential Linear Programming （SLP） method or Linear Programming （LP） method. The ground structure approach is often used to solve this kind of design problems. In this paper, the topology optimization is formulated as a Multiobjective Optimization Problem （MOP）, which is to find the cross-sectional area of truss members, such that both the total volume of members and the weighted mean compliance are minimized. Based upon the Karush-Kuhn-Tucker conditions （the optimality condition）, the Pareto optimal front of this problem can be obtained theoretically. The truss topology optimization under multiple load cases can be solved by the SLP. On the other hand, the LP such as the Simplex method or the interior point method can be applied to find one of the Pareto optimal solutions of the MOP under single load case. The applications of either the SLP or the LP are illustrated in numerical examples with discussion on characteristics of design results.展开更多
The transmission ratio is the key parameters influence power performance and economic performance of electric vehicle （EV）. As a class of heuristic algorithms, Dynamical Evolutionary Algorithm （DEA） is suitable to...The transmission ratio is the key parameters influence power performance and economic performance of electric vehicle （EV）. As a class of heuristic algorithms, Dynamical Evolutionary Algorithm （DEA） is suitable to solve multi-objective optimization problems. This paper presents a new method to optimize the transmission ratio using DEA. The fuzzy constraints and objective function of transmission ratio are established for parameter optimization problem of electric bus transmission. DEA is used to solve the optimiza- tion problem. The transmission system is also designed based on the optimization result. Optimization and test results show that the dynamical evolutionary algorithm is an effective method to solve transmission parameter optimization problems.展开更多
In the current scenario of global competition and short product life cycles, customer-defined satisfaction has attracted interest in artifact design. Accordingly, intelligent decision-making through multi-objective op...In the current scenario of global competition and short product life cycles, customer-defined satisfaction has attracted interest in artifact design. Accordingly, intelligent decision-making through multi-objective optimization has been proposed as an efficient method for human-centered manufacturing. However, previous vast researches on optimization have been mainly focused on optimization theory and optimization techniques and paid little interests on the process of problem formulation itself. In this paper, therefore, the authors present a total framework for supporting multi-objective decision making. Then, the authors try to solve the formulated multi-objective optimization problem that involves both qualitative and quantitative performance measures as a general consequence from the above procedure. Taking especially quality as a qualitative measure, the authors gave a new idea to evaluate the quality quantitatively. Additionally, to facilitate the portability of the proposed method in multidisciplinary decision-making environments, the authors implement the proposal algorithm in an Excel spreadsheet and validate the effectiveness of the approach through a case study.展开更多
Over the years, a number of methods have been proposed for the generation of uniform and globally optimal Pareto frontiers in multi-objective optimization problems. This has been the case irrespective of the problem d...Over the years, a number of methods have been proposed for the generation of uniform and globally optimal Pareto frontiers in multi-objective optimization problems. This has been the case irrespective of the problem definition. The most commonly applied methods are the normal constraint method and the normal boundary intersection method. The former suffers from the deficiency of an uneven Pareto set distribution in the case of vertical （or horizontal） sections in the Pareto frontier, whereas the latter suffers from a sparsely populated Pareto frontier when the optimization problem is numerically demanding （ill-conditioned）. The method proposed in this paper, coupled with a simple Pareto filter, addresses these two deficiencies to generate a uniform, globally optimal, well-populated Pareto frontier for any feasible bi-objective optimization problem. A number of examples are provided to demonstrate the performance of the algorithm.展开更多
文摘In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Modified non-dominated sorting genetic algorithm II(NSGA II) was used for multi-objective optimization of automotive S-rail considering absorbed energy(E), peak crushing force(Fmax) and mass of the structure(W) as three conflicting objective functions. In the multi-objective optimization problem(MOP), E and Fmax are defined by polynomial models extracted using the software GEvo M based on train and test data obtained from numerical simulation of quasi-static crushing of the S-rail using ABAQUS. Finally, the nearest to ideal point(NIP)method and technique for ordering preferences by similarity to ideal solution(TOPSIS) method are used to find the some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. Results represent that the optimum design point obtained from TOPSIS method exhibits better trade-off in comparison with that of optimum design point obtained from NIP method.
基金Project(UKM-DLP-2011-059) supported by the National University of Malaysia
文摘A newly developed heuristic global optimization algorithm, called gravitational search algorithm （GSA）, was introduced and applied for simultaneously coordinated designing of power system stabilizer （PSS） and thyristor controlled series capacitor （TCSC） as a damping controller in the multi-machine power system. The coordinated design problem of PSS and TCSC controllers over a wide range of loading conditions is formulated as a multi-objective optimization problem which is the aggregation of two objectives related to damping ratio and damping factor. By minimizing the objective function with oscillation, the characteristics between areas are contained and hence the interactions among the PSS and TCSC controller under transient conditions are modified. For evaluation of effectiveness and robustness of proposed controllers, the performance was tested on a weakly connected power system subjected to different disturbances, loading conditions and system parameter variations. The cigenvalues analysis and nonlinear simulation results demonstrate the high performance of proposed controllers which is able to provide efficient damping of low frequency oscillations.
文摘Carbon nanotube(CNT)/polymer nanocomposites have vast application in industry because of their light mass and high strength. In this work, a cylindrical tube which is made up of functionally graded(FG) PmP V/CNT nanocomposite, is optimally designed for the purpose of torque transmission. The main confining parameters of a rotating shaft in torque transmission process are mass of the shaft, critical speed of rotation and critical buckling torque. It is required to solve a multi-objective optimization problem(MOP) to consider these three targets simultaneously in the process of design. The three-objective optimization problem for this case is defined and solved using a hybrid method of FEM and modified non-dominated sorting genetic algorithm(NSGA-II), by coupling two softwares, MATLAB and ABAQUS. Optimization process provides a set of non-dominated optimal design vectors. Then, two methods, nearest to ideal point(NIP) and technique for ordering preferences by similarity to ideal solution(TOPSIS), are employed to choose trade-off optimum design vectors. Optimum parameters that are obtained from this work are compared with the results of previous studies for similar cylindrical tubes made from composite or a hybrid of aluminum and composite that more than 20% improvement is observed in all of the objective functions.
文摘A new strategy is presented to solve robust multi-physics multi-objective optimization problem known as improved multi-objective collaborative optimization （IMOCO） and its extension improved multi-objective robust collaborative （IMORCO）. In this work, the proposed IMORCO approach combined the IMOCO method, the worst possible point （WPP） constraint cuts and the Genetic algorithm NSGA-II type as an optimizer in order to solve the robust optimization problem of multi-physics of microstructures with uncertainties. The optimization problem is hierarchically decomposed into two levels： a microstructure level, and a disciplines levels, For validation purposes, two examples were selected： a numerical example, and an engineering example of capacitive micro machined ultrasonic transducers （CMUT） type. The obtained results are compared with those obtained from robust non-distributed and distributed optimization approach, non-distributed multi-objective robust optimization （NDMORO） and multi-objective collaborative robust optimization （McRO）, respectively. Results obtained from the application of the IMOCO approach to an optimization problem of a CMUT cell have reduced the CPU time by 44% ensuring a Pareto front close to the reference non-distributed multi-objective optimization （NDMO） approach （mahalanobis distance, D2M =0.9503 and overall spread, So=0.2309）. In addition, the consideration of robustness in IMORCO approach applied to a CMUT cell of optimization problem under interval uncertainty has reduced the CPU time by 23% keeping a robust Pareto front overlaps with that obtained by the robust NDMORO approach （D2M =10.3869 and So=0.0537）.
文摘The present paper deals with a multiobjective optimization of truss topology by either Sequential Linear Programming （SLP） method or Linear Programming （LP） method. The ground structure approach is often used to solve this kind of design problems. In this paper, the topology optimization is formulated as a Multiobjective Optimization Problem （MOP）, which is to find the cross-sectional area of truss members, such that both the total volume of members and the weighted mean compliance are minimized. Based upon the Karush-Kuhn-Tucker conditions （the optimality condition）, the Pareto optimal front of this problem can be obtained theoretically. The truss topology optimization under multiple load cases can be solved by the SLP. On the other hand, the LP such as the Simplex method or the interior point method can be applied to find one of the Pareto optimal solutions of the MOP under single load case. The applications of either the SLP or the LP are illustrated in numerical examples with discussion on characteristics of design results.
文摘The transmission ratio is the key parameters influence power performance and economic performance of electric vehicle （EV）. As a class of heuristic algorithms, Dynamical Evolutionary Algorithm （DEA） is suitable to solve multi-objective optimization problems. This paper presents a new method to optimize the transmission ratio using DEA. The fuzzy constraints and objective function of transmission ratio are established for parameter optimization problem of electric bus transmission. DEA is used to solve the optimiza- tion problem. The transmission system is also designed based on the optimization result. Optimization and test results show that the dynamical evolutionary algorithm is an effective method to solve transmission parameter optimization problems.
文摘In the current scenario of global competition and short product life cycles, customer-defined satisfaction has attracted interest in artifact design. Accordingly, intelligent decision-making through multi-objective optimization has been proposed as an efficient method for human-centered manufacturing. However, previous vast researches on optimization have been mainly focused on optimization theory and optimization techniques and paid little interests on the process of problem formulation itself. In this paper, therefore, the authors present a total framework for supporting multi-objective decision making. Then, the authors try to solve the formulated multi-objective optimization problem that involves both qualitative and quantitative performance measures as a general consequence from the above procedure. Taking especially quality as a qualitative measure, the authors gave a new idea to evaluate the quality quantitatively. Additionally, to facilitate the portability of the proposed method in multidisciplinary decision-making environments, the authors implement the proposal algorithm in an Excel spreadsheet and validate the effectiveness of the approach through a case study.
文摘Over the years, a number of methods have been proposed for the generation of uniform and globally optimal Pareto frontiers in multi-objective optimization problems. This has been the case irrespective of the problem definition. The most commonly applied methods are the normal constraint method and the normal boundary intersection method. The former suffers from the deficiency of an uneven Pareto set distribution in the case of vertical （or horizontal） sections in the Pareto frontier, whereas the latter suffers from a sparsely populated Pareto frontier when the optimization problem is numerically demanding （ill-conditioned）. The method proposed in this paper, coupled with a simple Pareto filter, addresses these two deficiencies to generate a uniform, globally optimal, well-populated Pareto frontier for any feasible bi-objective optimization problem. A number of examples are provided to demonstrate the performance of the algorithm.
