CAE technology has been applied for the crash safety design of vehicles, however, the optimum design has not been completed for the crash behavior of the vehicles. The authors have proposed a Statistical Design Suppor...
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CAE technology has been widely applied for the crash safety design of vehicles. Nevertheless, an optimum design approach has not been completed for the crash behavior of the vehicles because of the nonlinearity of the...
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CAE technology has been widely applied for the crash safety design of vehicles. Nevertheless, an optimum design approach has not been completed for the crash behavior of the vehicles because of the nonlinearity of the dynamic problem. The authors have proposed a Statistical Design Support System (SDSS), and suggested this is one of the available optimal approaches for nonlinear and dynamic problems such as the vehicles. In this study, the SDSS was applied for the multi-objectiveoptimization design of the main reinforced members of a vehicle. The thicknesses of each member were chosen as the design variables, and the total weight and the deformation of the cabin as the multi-objective functions. It was shown that the SDSS could satisfactorily be used as a practicalmulti-objectiveoptimization design tool for the crash safety design of vehicles, and the design cycle could be significantly reduced using SDSS.
Linear constraint databases (LCDBs) extend relational databases to include linear arithmetic constraints in both relations and queries. A LCDB can also be viewed as a powerful extension of linear programming (LP) wher...
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Linear constraint databases (LCDBs) extend relational databases to include linear arithmetic constraints in both relations and queries. A LCDB can also be viewed as a powerful extension of linear programming (LP) where the system of constraints is generalized to a database containing constraints and the objective function is generalized to a relational query containing constraints. Our major concern is query optimization in LCDBs. Traditional database approaches are not adequate for combination with LP technology. Instead, we propose a new query optimization approach, based on statistical estimations and iterated trials of potentially better evaluation plans. The resulting algorithms are not only effective on LCDBs, but also applicable to existing query languages. A number of specific constraint algebra algorithms are also developed: select-project-join for two relations, constraint sort-join and constraint multi-join.
This paper reviews several genetic algorithm (GA) approaches to multi-objectiveoptimization problems (MOPs). The keynote point of GAs to MOPs is designing efficient selection/reproduction operators so that a variety ...
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ISBN:
(纸本)0780329031
This paper reviews several genetic algorithm (GA) approaches to multi-objectiveoptimization problems (MOPs). The keynote point of GAs to MOPs is designing efficient selection/reproduction operators so that a variety of Pareto-optimal solutions are generated. From this viewpoint, the present paper reviews several devices proposed for multi-objectiveoptimization by GAs such as the parallel selection method, the Pareto-based ranking, and the fitness sharing. Characteristics of these approaches have been confirmed through computational experiments with a simple example. Moreover, two practical applications of the GA approaches to MOPs are introduced briefly.
The purpose of this research is to propose an innovative methodology that solves practical business planning problems efficiently. Business planning models are typically prone to be complex and large-scale by nature. ...
The purpose of this research is to propose an innovative methodology that solves practical business planning problems efficiently. Business planning models are typically prone to be complex and large-scale by nature. The new method proposed herein employs two main approaches to these inevitable conditions. The first approach is to build the basic model relatively simple with respect to its original structure. Then the method incorporates any additional information into the model framework by an interactive way in multiobjectiveoptimization. The second approach is to employ an ellipsoid interior point algorithm in order to improve computational efficiency. By utilizing parametric decomposition theory in multi-objective programming for the first approach, the method provides several alternative nondominated optimal solutions. In this way, intangible information which is normally difficult to formulate can be added into the model to satisfy the decision maker through the decision-making process. The interior point algorithm developed in this research for the second approach is an extension of a series of ellipsoid algorithms for multiobjective programming. The algorithms trace their origin to an ellipsoid method developed in the early 1980s. The proposed method is distinguished from the previous algorithms by several new concepts which include a linearization at the analytic center and an approximate efficient point with equivalent satisfactory levels of each objective function. Two application examples were chosen from financial and production planning problems, and their robustness to cope with the uncertain business environment was discussed. The efficiency of the method was tested by codes written on Lotus 1-2-3 and GWBASIC software. The attempt to use microcomputers has also confirmed a better interface between the decision maker and the computer. Although the method was originally designed to solve a particular type of business planning problem, it is also appli
The paper reviews several genetic algorithm (GA) approaches to multiobjectiveoptimization problems (MOPs). The keynote point of GAs to MOPs is designing efficient selection/reproduction operators so that a variety o...
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The paper reviews several genetic algorithm (GA) approaches to multiobjectiveoptimization problems (MOPs). The keynote point of GAs to MOPs is designing efficient selection/reproduction operators so that a variety of Pareto optimal solutions are generated. From this viewpoint, the paper reviews several devices proposed for multiobjectiveoptimization by GAs such as the parallel selection method, the Pareto based ranking, and the fitness sharing. Characteristics of these approaches have been confirmed through computational experiments with a simple example. Moreover, two practical applications of the GA approaches to MOPs are introduced briefly.
The enormous practical need for solving global optimization problems coupled with a rapidly advancing computer technology has allowed one to consider problems which a few years ago would have been considered computati...
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ISBN:
(数字)9783662025987
The enormous practical need for solving global optimization problems coupled with a rapidly advancing computer technology has allowed one to consider problems which a few years ago would have been considered computationally intractable. As a consequence, we are seeing the creation of a large and increasing number of diverse algorithms for solving a wide variety of multiextremal global optimization problems. The goal of this book is to systematically clarify and unify these diverse approaches in order to provide insight into the underlying concepts and their pro perties. Aside from a coherent view of the field much new material is presented. By definition, a multiextremal global optimization problem seeks at least one global minimizer of a real-valued objective function that possesses different local n minimizers. The feasible set of points in IR is usually determined by a system of inequalities. It is well known that in practically all disciplines where mathematical models are used there are many real-world problems which can be formulated as multi extremal global optimization problems.
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