• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.41-42
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• 2012

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.6
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• pp.1375-1383
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• 1994

The water distribution system problem consists of finding a minimum cost system design subject to hydraulic and operational constraints. Since the municipal water distribution system problem is nonconvex with multiple local minima, classical optimization methods find a local optimum. An outer flow search - inner optimization procedure is proposed for choosing a better local minimum for the water distribution systems. The pipe network is judiciously subjected to the outer search scheme which chooses alternative flow configurations to find an optimal flow division among pipes. Because the problem is nonconvex, a global search scheme called Stochastic Probing method is employed to permit a local optimum seeking method to migrate among various local minima. A local minimizer is employed for the design of least cost diameters for pipes in the network. The algorithm can also be employed for optimal design of parallel expansion of existing networks. In this paper one municipal water distribution system is considered. The optimal solutions thus found have significantly smaller costs than the ones reported previously by other researchers.

• 한국전산유체공학회:학술대회논문집
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• 2005.10a
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• pp.163-167
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• 2005

A research to evaluate efficiency of design optimization was performed for aerodynamic design optimization problem in distributed computing environment. The aerodynamic analyses which take most of computational work during design optimization were divided into several jobs and allocated to associated PC clients through network. This is not a parallel process based on domain decomposition rather than a simultaneous distributed-analyses process using network-distributed computers. GBOM(gradient-based optimization method), SAO(Sequential Approximate Optimization) and RSM(Response Surface Method) were implemented to perform design optimization of transonic airfoil and to evaluate their efficiencies. One dimensional minimization followed by direction search involved in the GBOM was found an obstacle against improving efficiency of the design process in distributed computing environment. The SAO was found quite suitable for the distributed computing environment even it has a handicap of local search. The RSM is apparently the fittest for distributed computing environment, but additional trial and error works needed to enhance the reliability of the approximation model are annoying and time-consuming so that they often impair the automatic capability of design optimization and also deteriorate efficiency from the practical point of view.

• Structural Engineering and Mechanics
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• v.42 no.3
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• pp.335-352
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• 2012

Particle Swarm Optimization (PSO) is a stochastic population based optimization algorithm which has attracted attentions of many researchers. This method has great potentials to be applied to many optimization problems. Despite its robustness the standard version of PSO has some drawbacks that may reduce its performance in optimization of complex structures such as laminated composites. In this paper by suggesting a new variation scheme for acceleration parameters and inertial weight factors of PSO a novel optimization algorithm is developed to enhance the basic version's performance in optimization of laminated composite structures. To verify the performance of the new proposed method, it is applied in two multi-objective design optimization problems of laminated cylindrical. The numerical results from the proposed method are compared with those from two other conventional versions of PSO-based algorithms. The convergancy of the new algorithms is also compared with the other two versions. The results reveal that the new modifications inthe basic forms of particle swarm optimization method can increase its convergence speed and evade it from local optima traps. It is shown that the parameter variation scheme as presented in this paper is successful and can evenfind more preferable optimum results in design of laminated composite structures.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.811-814
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• 1997

In this paper, the torque optimization of a kinematically redundant manipulator for minimizing the torque demands is discussed. The minimum torque solution based on a local optimization has been known to encounter the instability problem and then the global torque optimization was suggested as one of the alternatives. Herein, by adopting the infinity-norm rather than the 2-norm for the magnitude of torques, we are to propose a new cost function more advantageous to the avoidance of torque limits. By the way, a solution to the global torque optimization formulated with the new cost function can not be obtained by the previous methods due to their difficulties such as inability to treat discontinuous cost functions and various constraints on the joint variables. Thus, to overcome those deficiencies, we are developing a new approach using the dynamic programming. The effectiveness of the proposed method is shown through simulation examples for a 3-link planar redundant manipulator.

• Journal of Mechanical Science and Technology
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• v.18 no.9
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• pp.1512-1518
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• 2004

Rescaled Simulated Annealing (RSA) has been adapted to solve combinatorial optimization problems in which the available computational resources are limited. Simulated Annealing (SA) is one of the most popular combinatorial optimization algorithms because of its convenience of use and because of the good asymptotic results of convergence to optimal solutions. However, SA is too slow to converge in many problems. RSA was introduced by extending the Metropolis procedure in SA. The extension rescales the state's energy candidate for a transition before applying the Metropolis criterion. The rescaling process accelerates convergence to the optimal solutions by reducing transitions from high energy local minima. In this paper, structural optimization examples using RSA are provided. Truss structures of which design variables are discrete or continuous are optimized with stress and displacement constraints. The optimization results by RSA are compared with the results from classical SA. The comparison shows that the numbers of optimization iterations can be effectively reduced using RSA.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.10 s.181
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• pp.2438-2450
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• 2000

The Design Axioms provide a general framework for design methodologies. The axiomatic design framework has been successfully applied to various design tasks. However, the axiomatic design has been rarely utilized in the detailed design process of structures where the optimization technology is generally carried out. The relationship between the axiomatic design and the optimization is investigated and Logical Decomposition method is developed for a systematic structural optimization. The entire optimization process is decomposed to satisfy the Independence Axiom. In the decomposition process, design variables are grouped according to sensitivities. The sensitivities are evaluated by the Analysis of Variance(ANOVA) to avoid considering only local values. The developed method is verified through examples such as the twenty -five members transmission tower and the two -bay-six-story frame.

• International Journal of CAD/CAM
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• v.6 no.1
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• pp.29-40
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• 2006

The paper presents an optimization system which integrates a parametric design tool, 3D diffraction-radiation analysis and hydrodynamic performance assessment based on short and long term wave statistics. Controlled by formal optimization strategies the system is able to design offshore structure hulls with superior seakeeping qualities. The parametric modeling tool enables the designer to specify the geometric characteristics of the design from displacement over principal dimensions down to local shape properties. The computer generates the hull form and passes it on to the hydrodynamic analysis, which computes response amplitude operators (RAOs) for forces and motions. Combining the RAOs with short and long-term wave statistics provides a realistic assessment of the quality of the design. The optimization algorithm changes selected shape parameters in order to minimize forces and motions, thus increasing availability and safety of the system. Constraints ensure that only feasible designs with sufficient stability in operation and survival condition are generated. As an example the optimization study of a semisubmersible is discussed. It illustrates how offshore structures can be optimized for a specific target area of operation.

• Structural Engineering and Mechanics
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• v.70 no.3
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• pp.367-380
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• 2019

The paper concerns topology and geometry optimization of statically determinate beams with arbitrary number of supports. The optimization problem is treated as a bi-criteria one, with the objectives of minimizing the absolute maximum bending moment and the maximum deflection for a uniform gravity load. The problem is formulated and solved using the Pareto optimality concept and the lexicographic ordering of the objectives. The non-dominated sorting genetic algorithm NSGA-II and the local search method are used for the optimization in the Pareto sense, whereas the genetic algorithm and the exhaustive search method for the lexicographic optimization. Trade-offs between objectives are examined and sets of Pareto-optimal solutions are provided for different topologies. Lexicographically optimal beams are found assuming that the maximum moment is a more important criterion. Exact formulas for locations and values of the maximum deflection are given for all lexicographically optimal beams of any topology and any number of supports. Topologies with lexicographically optimal geometries are classified into equivalence classes, and specific features of these classes are discussed. A qualitative principle of the division of topologies equivalent in terms of the maximum moment into topologies better and worse in terms of the maximum deflection is found.

• International Journal of Computer Science & Network Security
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• v.24 no.8
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• pp.72-84
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• 2024

Plant leaf classification is an imperative task when their use in real world is considered either for medicinal purposes or in agricultural sector. Accurate identification of plants is, therefore, quite important, since there are numerous poisonous plants which if by mistake consumed or used by humans can prove fatal to their lives. Furthermore, in agriculture, detection of certain kinds of weeds can prove to be quite significant for saving crops against such unwanted plants. In general, Artificial Neural Networks (ANN) are a suitable candidate for classification of images when small datasets are available. However, these suffer from local minima problems which can be effectively resolved using some global optimization techniques. Considering this issue, the present research paper presents an automated plant leaf classification system using optimized soft computing models in which ANNs are optimized using Grasshopper Optimization algorithm (GOA). In addition, the proposed model outperformed the state-of-the-art techniques when compared with simple ANN and particle swarm optimization based ANN. Results show that proposed GOA-ANN based plant leaf classification system is a promising technique for small image datasets.