• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.327-332
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• 2005

We investigatea new fuzzy-neural networks-Hybrid Fuzzy set based polynomial Neural Networks (HFSPNN). These networks consist of genetically optimized multi-layer with two kinds of heterogeneous neurons thatare fuzzy set based polynomial neurons (FSPNs) and polynomial neurons (PNs). We have developed a comprehensive design methodology to determine the optimal structure of networks dynamically. The augmented genetically optimized HFSPNN (namely gHFSPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of gHFSPNN leads to the selection leads to the selection of preferred nodes (FSPNs or PNs) available within the HFSPNN. In the sequel, the structural optimization is realized via GAs, whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFSPNN is quantified through experimentation where we use a number of modeling benchmarks synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.

• Fire Science and Engineering
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• v.28 no.3
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• pp.1-9
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• 2014

In this study, it was developed to the design program optimization the design factors of the carbon dioxide fire extinguishing system on the basis of design theory for the carbon dioxide fire extinguishing system, national emergency management agency (NEMA) notice No. 2012-11, KS B 6261 and steepest descent method of optimization. The design program was developed to C++ compiler based on established the logic and algorithms and was to operate on the Windows operating system. The optimization of design factors for the carbon dioxide fire extinguishing system are minimized subject to constraint on agent flow rate, emission time and design variables (pipe size etc.). It was verified to the design program performance for test system, and it was provided to the foundation for optimal design in fire fighting field. Also, it is considered to improve the efficiency of the fire extinguishing system and to maximize of fire suppression as the construction of the carbon dioxide extinguishing system based on the optimal design factors.

• Journal of Computational Design and Engineering
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• v.2 no.3
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• pp.157-164
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• 2015

This paper discusses an optimization-based approach for the design of a product platform for industrial three-axis linear-type robots, which are widely used for handling objects in manufacturing lines. Since the operational specifications of these robots, such as operation speed, working distance and orientation, weight and shape of loads, etc., will vary for different applications, robotic system vendors must provide various types of robots efficiently and effectively to meet a range of market needs. A promising step toward this goal is the concept of a product platform, in which several key elements are commonly used across a series of products, which can then be customized for individual requirements. However the design of a product platform is more complicated than that of each product, due to the need to optimize the design across many products. This paper proposes an optimization-based fundamental framework toward the design of a product platform for industrial three-axis linear-type robots; this framework allows the solution of a complicated design problem and builds an optimal design method of fundamental features of robot frames that are commonly used for a wide range of robots. In this formulation, some key performance metrics of the robot are estimated by a reducedorder model which is configured with beam theory. A multi-objective optimization problem is formulated to represent the trade-offs among key design parameters using a weighted-sum form for a single product. This formulation is integrated into a mini-max type optimization problem across a series of robots as an optimal design formulation for the product platform. Some case studies of optimal platform design for industrial three-axis linear-type robots are presented to demonstrate the applications of a genetic algorithm to such mathematical models.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.6
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• pp.264-273
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• 2006

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) by means of consecutive optimization and also discuss its comprehensive design methodology involving mechanisms of genetic optimization. The network is based on a structurally as well as parametrically optimized fuzzy polynomial neurons (FPNs) conducted with the aid of information granulation and genetic algorithms. In structurally identification of FPN, the design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). For the parametric identification, we obtained the effective model that the axes of MFs are identified by GA to reflect characteristic of given data. Especially, the genetically dynamic search method is introduced in the identification of parameter. It helps lead to rapidly optimal convergence over a limited region or a boundary condition. To evaluate the performance of the proposed model, the model is experimented with using two time series data(gas furnace process, nonlinear system data, and NOx process data).

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.213-220
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• 2004

Evolutionary algorithms are well-suited for multi-objective optimization problems involving several. often conflicting objective. Pareto-based evolutionary algorithms, in particular, have shown better performance than other multi-objective evolutionary algorithms in comparison. Recently, pareto-based evolutionary algorithms uses a density information in fitness assignment scheme for generating uniform distributed global pareto optimal front. However, the usage of density information is not Important elements in a whole evolution path but plays an auxiliary role in order to make uniform distribution. In this paper, we propose an evolutionary algorithms for multi-objective optimization which assigns the fitness using pareto dominance rank and density weighting, and thus pareto dominance rank and density have similar influence on the whole evolution path. Furthermore, the experimental results, which applied our method to the six multi-objective optimization problems, show that the proposed algorithms show more promising results.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.561-570
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• 2004

In this paper, new architectures and comprehensive design methodologies of Genetic Algorithms(GAs) based Genetically optimized Neurofuzzy Networks(GoNFN) are introduced, and a series of numeric experiments are carried out. The proposed GoNFN is based on the rule-based Neurofuzzy Networks(NFN) with the extended structure of the premise and the consequence parts of fuzzy rules being formed within the networks. The premise part of the fuzzy rules are designed by using space partitioning in terms of fuzzy sets defined in individual variables. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and quadratic are taken into consideration. The structure and parameters of the proposed GoNFN are optimized by GAs. GAs being a global optimization technique determines optimal parameters in a vast search space. But it cannot effectively avoid a large amount of time-consuming iteration because GAs finds optimal parameters by using a given space. To alleviate the problems, the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. In a nutshell, the objective of this study is to develop a general design methodology o GAs-based GoNFN modeling, come up a logic-based structure of such model and propose a comprehensive evolutionary development environment in which the optimization of the model can be efficiently carried out both at the structural as well as parametric level for overall optimization by utilizing the separate or consecutive tuning technology. To evaluate the performance of the proposed GoNFN, the models are experimented with the use of several representative numerical examples.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1321-1321
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• 2024

Concrete arch dams, unlike conventional concrete gravity dams, have thin arch-shaped cross sections and must be designed considering a three-dimensional shape. In particular, double-curvature arch dams, which have arch-shaped vertical and horizontal sections, require careful consideration during design due to their unique shape. Although stress analysis is complex, and various factors need to be considered during the design, these dams offer economic advantages as they require less material. Consequently, numerous double-curvature arch dams have been constructed worldwide, and ongoing research focuses on optimizing their shapes. In this study, an efficient optimization algorithm was developed for the shape optimization of concrete arch dams with double-curvature using genetic algorithms and improved population initializing technique. The developed technique utilized domain knowledge in the field of arch dams to generate an excellent initial population. To assess the relevance of domain knowledge, an investigation was conducted on the accumulated knowledge and empirical formulas from literature. Two pieces of domain knowledge can be gleaned from the iterative structural design experiences associated with arch dams. First, it concerns the thickness of the central cantilever of an arch dam. For minimum tensile stress, it is best to make the thickness as thin as possible at the dam crest and gradually become thicker as it goes down. The second aspect concerns the sliding stability of the arch dam, which depends on the central angle of the horizontal section. This angel is important for stability because the plane arch serves to transfer the hydraulic load from the reservoir to both abutments. Also, preliminary design formulas for arch dams from a manual written by the United States Bureau of Reclamation (USBR) were used. On the other hand, since domain knowledge is based on engineering experiences and data from existing dams, its usability should be verified by comparing it with the results of design optimization performed by classic genetic algorithms. To validate the performance of the optimization algorithm with the improved population initialization technique, a test site with an existing dam was selected, and algorithmic application tests were conducted. Stress analysis is performed for each design iteration, evaluating constraints and calculating fitness as the objective function. The results confirmed that the algorithm developed in this study exhibits superior performance in terms of average fitness and convergence rate compared to classic genetic algorithms.

• Journal of Korean Society for Quality Management
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• v.45 no.2
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• pp.191-207
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• 2017

Purpose: To develop an interactive version of the conventional process capability-based approach, called 'Interactive Process Capability-Based Approach (IPCA)' in multi-response surface optimization to obtain a satisfactory compromise which incorporates a decision maker(DM)'s preference information precisely. Methods: The proposed IPCA consists of 4 steps. Step 1 is to obtain the estimated process capability indices and initialize the parameters. Step 2 is to maximize the overall process capability index. Step 3 is to evaluate the optimization results. If all the responses are satisfactory, the procedure stops with the most preferred compromise solution. Otherwise, it moves to Step 4. Step 4 is to adjust the preference parameters. The adjustment can be made in two modes: relaxation and tightening. The relaxation is to make the importance of one of the satisfactory responses lower, which is implemented by decreasing its weight. The tightening is to make the importance of one of the unsatisfactory responses higher, which is implemented by increasing its weight. Then, the procedure goes back to Step 2. If there is no response to be adjusted, it stops with the unsatisfactory compromise solution. Results: The proposed IPCA was illustrated through a multi-response surface problem, colloidal gas aphrons problem. The illustration shows that it can generate a satisfactory compromise through an interactive procedure which enables the DM to provide his or her preference information conveniently. Conclusion: The proposed IPCA has two major advantages. One is to obtain a satisfactory compromise which is faithful to the DM preference structure. The other is to make the DM's participation in the interactive procedure easier by using the process capability index in judging satisfaction/unsatisfaction. The process capability index is very familiar with quality practitioners as well as indicates the process performance levels numerically.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1340-1345
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• 2003

In this paper, an integrated optimal design software system for structural components has been developed which interfaces existing commercial codes for CAD, CAE and Optimization. They include specialized optimal design software codes such as iSIGHT and VisualDOC, optimization module imbedded in CAD software developed by CAD developers, and optimal design software systems based on API of commercial CAD software. The advantages of the CAD imbedded optimal design approach and those of specialized optimal design software are taken to develop the system. The user defines optimal design formulation in the user interface for problem definition in the CAD control stage, where design variables are directly selectable from the CAD model and various properties and performance functions defined. The commercial CAD codes, Open I-DEAS are used for the development. The resulting software is minimally connected to CAD and CAE systems while keeping maximum independence from each other. This assures flexibility and freedom for problem definition. Fatigue life optimization is taken as a nontrivial application area. As a specific example, the shape design of a knuckle part of an automobile is performed, where the minimum fatigue life over the material domain in terms of the number of cycles of a curb strike are maximized under the constraint of not exceeding the current mass. The fatigue life has been improved by four times of the initial life. The developed software is illustrated to maintain the advantages of existing optimal design software systems while improving independency and flexibility.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.3
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• pp.821-831
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• 2014

Stochastic rainfall generators or stochastic simulation have been widely employed to generate synthetic rainfall sequences which can be used in hydrologic models as inputs. The calibration of Poisson cluster stochastic rainfall generator (e.g. Modified Bartlett-Lewis Rectangular Pulse, MBLRP) is seriously affected by local minima that is usually estimated from the local optimization algorithm. In this regard, global optimization techniques such as particle swarm optimization and shuffled complex evolution algorithm have been proposed to better estimate the parameters. Although the global search algorithm is designed to avoid the local minima, reliable parameter estimation of MBLRP model is not always feasible especially in a limited parameter space. In addition, uncertainty associated with parameters in the MBLRP rainfall generator has not been properly addressed yet. In this sense, this study aims to develop and test a Bayesian model based parameter estimation method for the MBLRP rainfall generator that allow us to derive the posterior distribution of the model parameters. It was found that the HBM based MBLRP model showed better performance in terms of reproducing rainfall statistic and underlying distribution of hourly rainfall series.