A revised method for a robust optimal design one based on the minimax regret criterion is proposed. Based on the robust optimal design method, equipment capacities of energy supply system are determined efficiently so that they are robust economically against the uncertainty in energy demands. Equipment efficiently so that they are robust economically against the uncertainty in energy demands. Equipment capacities and utility contract demands as well as energy flow rates are determined to minimize the maximum regret in the annual total cost and to satisfy all the possible energy demands. This optimization problem is formulated as a kind of multilevel linear programming, and its solution is obtained by repeatedly evaluating lower and upper bounds for the maximum regret in the annual total cost. The revise is performed using dual problem and bilinear programming to efficiently evaluate the upper bound. Numerical studies on cogeneration systems are carried out to ascertain the validity and effectiveness of the proposed method.

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This paper presents an optimal design method regarding a large-scale acoustics-structure coupled model subject to frequency response. It is difficult to directly apply an optimal design method to a large-scale model, because this model includes more than 1 million degrees of freedom. First, the analytical sensitivities of the target sound pressure based on the concept of the mutual mean compliance is formulated using the coupled motion formulation Second, a new coupled reduction method based on frequency response functions is presented. Third, beam elements are selected as additional structures because cross sectional sizes of beam elements easily make engineers image new ideas as a small reinforcement. In addition to it, the analytical sensitivities of the sound pressure with respect to cross sectional properties is formulated. Moreover, an optimization problem, where the amplitude of a sound pressure, a total mass of additional elements and side lengths of square cross sections are regarding as an objective function, an equality constraint function and design variables, respectively, is formulated. Finally, we demonstrate the effectiveness of the proposed method through the simple model.

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It is very important to optimize the engine mounting layout to provide for NVH (Noise, Vibration, Harshness) problem. We use the FEM model for calculating the Idling Vibration of the vehicle, by adopting CMS (Component Mode Synthesis) for the efficiency of the calculation. In this research, we use the Method using MPOD for this optimization problem. This method consists of 2 processes that 1st : the approximate function is made from the learning data by HNN, 2nd : optimum design of the approximate function is added to the learning data to remake the approximate function, if it doesn't fulfill the requirement. These processes are repeated until success. With this method, we get the optimum design as good as that with GA, and it repeats the calculation of the objective function in less times than GA. So this method is very useful for the problem, where it takes long time to calculate the objective function.

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The fundamental design optimization of thermal power equipment has been studied using several optimum methods, such as, gradient method, response surface method, orthogonal array, and their combinations. It was found that these optimum methods are effective in improving the equipment efficiency and the optimum method which combines gradient method with orthogonal array shows the best performance in the design space with plural peaks.

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In this paper, we study an inverse kinematics problem in which optimum trajectories of walking robot toes are generated under the condition of minimum walking energy. It is important for the walking robot that the motions of legs are determined to achieve the desired body operation. A 4-legged walking robot with rinks and motors at joints of waist and knee is assumed. Mathematical optimization method is used for optimization of several parameters which describe the trajectory function of toe. We use neural networks to calculate optimum trajectory in real time. Neural network controllers are made up by using clustered data for several configurations of obstacles in walking environment and used by changing them.

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Abstract-This paper presents a novel gradient-type iterative algorithm for the solution of optimal control problems with constraints on the control variables, the terminal state and the state variables. In this algorithm, the gradient function is synthesized in a hierarchical manner so that it decides the convergence rates of the terminal constraints, the state constraints and the cost function separately, to improve the convergence performance. A clipping-off technique is used to deal with the input constraints. The proposed algorithm can be also applied to the problem with final time unspecified by introducing a time-scale factor. A numerical example is shown to illustrate the effectiveness of the proposed algorithm.

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Timing belt is used in a wide field such as OA machine, automobile, etc. In order to predict the movements of belt drive system in precise and early, it is necessary to clarify the inelastic time-dependent viscoelasticity property of timing belt. In this study, a method, which included 3 parts as below, is proposed. (1) Experimental device : The rotation velocity of drive & drived axis are measured. (2) Experimental device mechanical model : The rotation velocity of drived axis is computed. (3) Optimization : The belt Load-Strain curve is figured out by one factor experimental design and optimization method. Similar results as physical test are obtained when this method is applied to scanner simulation.

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The dynamic vibration absorber (DVA) is a passive vibration control device which is attached to a vibrating body subjected to exciting force or motion. The design optimization of the DVAs has long been, and often still is, an interesting optimization problem in the field of mechanical vibrations. Various types of DVAs and many optimization criteria are proposed until now. The optimization is performed algebraically or numerically. In this paper, we will introduce some closed-form algebraic expressions of the two types of DVAs (Voigt type and three-element type) on the basis of the three typical optimization criteria, which are called H_∞ optimization, H_2 optimization and stability maximization.

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This paper proposes two-objective design of frame structures for vibration suppression and weight reduction in mechanical systems. The H_∞ norm of the transfer function from disturbance sources to the output points where vibration should be suppressed, is adopted as the performance index to represent the magnitude of vibration transfer. To compute Pareto optimal solutions, interpolated sums of the H_∞ norm and the total weight are considered as the objective function to be minimized. The design parameters are the cross-sectional areas of frame members. For minimization of the objective function under the constraint of lower bounds of the cross-sectional areas, the sequential quadratic programming (SQP) method is employed. Numerical examples are presented to demonstrate the effectiveness of the proposed design method.

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This paper investigates the properties of the optimum structural design for supersonic flutter of the plate wing. The optimization method is based on the Evolutionary Structural Optimization method. In order to consider the properties of the optimum structural design, the optimum mass distribution, the optimum stiffness distribution and the optimum equivalent thickness distribution are compared in the numerical experiments. Numerical results show that the equivalent thickness distribution is the most suitable for the design variable of the structural optimization for the supersonic flutter. Numerical results also show that the optimum stiffness distribution has a relationship with the strain energy density of the 2nd flutter mode shape near the critical dynamic pressure.

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In most of engineering optimization problems, objective functions and constraint functions are not defined in explicit and sometime satisfying all of constraints are more difficult than improving objective function due to severe requirements to the problem. On the other hand, most of optimization algorithms assume that initial design point satisfy all of constraints (feasible design) or even if it is not so, the violations of constraints are not so large. And this consideration lack of existence of feasible design often make the optimization works in failure. So, it is necessary to assess feasible region before starting optimization in such cases. We propose non-linear discriminant analysis as such an assessment method. Discriminant analysis is one of common and widely used statistical method as one of major data-mining methods.

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In this study, the response surface method is applied to the reliability-based optimum design of a laminated composite plate subject to in-plane loads in order to improve the calculation efficiency. The reliability is approximated by polynomials in terms of the lamination. Where the authors have already clarified that the reliability is distributed smoothly in the lamination parameter space for the symmetric balanced laminate consisting of 0°, ±45° and 90° plies. Using the response surface method, the reliability-based optimization problem is transformed from a duplicated-loop iteration problem into a single-loop iteration problem. In constructing the response surfaces, several experimental points are selected based on the D-optimality criterion using genetic algorithms. The efficiency of the response surface approximation on the reliability-based optimization is demonstrated through the two dual problems; the reliability-maximized design of the constant-thickness plate and the thickness-minimized design under the reliability constraint.

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LMS OPTIMUSは最適化するための設計空間を視覚化し探索する空間や設計に関する「トレードオフ」の変動の中で重大な洞察を得ることができる環境を提供できます。LMS OPTIMUSは応答曲面の中に多峰性や鞍部を有する問題に対し、比較的速く解を求めることができる勾配法と最新技術の遺伝的な方法を組み合わせて設計空間で最適なポイントを見いだすことができます。結果の変化はモンテカルロ法で評価されます。LMS OPTIMUSはプロセスを自動化し全体をモニタリングして、エンジニアリング設計でもっとも面倒な試行錯誤のタスクを回避することができます。

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A novel design optimization system using a neural network has been developed. This system aims for efficient and emerging computation by means of neural network learning and various adaptations of the trained neural network. The neural network learns a successive optimization history automatically and then 1) approximates a global optimum, 2) shifts optimization strategies according to learning residual of optimization problem and 3) detects a set of important parameters as design knowledge. The system was verified by sample optimization problems of mathematical test functions and a two-dimensional blade shape design. The NEWT unstructured flow solution system simulated flow fields for the blade design, establishing a fully automated simulation process. It was proved by the test problems that the system is able to not only shorten a time to a global optimum, but also has emerging characteristic of creating knowledge on design problems.

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In this paper, a new parametric design optimization system that is suitable for a global computing environment is proposed. Various studies on parallel and distributed calculation have been performed. In relation to a collection of computing resources in a local area network. On the other hand, researches on the highly efficient wide area calculation system called "Grid Computing" have recently appeared. In the Grid Computing System, the computing resources connected to the wide area network are recognized as a single system by using middleware. Ninf system is a kind of such middleware called Grid RPC. In design optimization, evaluation procedures are executed frequently, and each procedure is executable asynchronously. In this study, evaluation procedures are called asynchronously using "Ninf RPC". Each evaluation is performed using ADVENTURE which is a parallel finite element analysis system implemented by MPI.

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In a bag-packing problem, it is important to determine which items are to be put in a bag in order to make the pack weigh the given desired weight. An algorithm is developed that can enumerate candidate solutions effectively. In some cases, it may be that none of solution proposals to the problem achieves a weight satisfactorily close enough to the desired standard. In such cases, although all the items for the present must be dumped according to the conventional way, one proposed the policy in which a single item is dumped. Using the proposed policy, the total number of dumping items is reduced.

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