• Proceedings of the Computational Structural Engineering Institute Conference
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• 1999.10a
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• pp.127-134
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• 1999

An optimization algorithm based on Genetic Algorithm(GA) is developed for discrete optimization of reinforced concrete plane frame by constructing databases. Under multiple loading conditions, discrete optimum sets of reinforcements for both negative and positive moments in beams, their dimensions, column reinforcement, and their column dimensions are found. Construction practice is also implemented by linking columns and beams by group ‘Connectivity’between columns located in the same column line is also considered. It is shown that the developed genetic algorithm was able to reach optimum design for reinforced concrete plane frame construction practice.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.33-38
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• 2006

In this paper, we propose a new architecture of Fuzzy Polynomial Neural Networks(FPNN) by means of genetically optimized Fuzzy Polynomial Neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially Genetic Algorithms(GAs). The conventional FPNN developed so far are based on mechanisms of self-organization and evolutionary optimization. The design of the network exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The proposed FPNN gives rise to a structurally optimized network and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. It is shown that the proposed advanced genetic algorithms based Fuzzy Polynomial Neural Networks is more useful and effective than the existing models for nonlinear process. We experimented with Medical Imaging System(MIS) dataset to evaluate the performance of the proposed model.

• Journal of Embryo Transfer
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• v.30 no.4
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• pp.315-317
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• 2015

In vitro culture of murine embryos is an important step for in vitro production systems including in vitro fertilization and generations of genetically engineered mice. M16 is widely used commercialized culture media for the murine embryos. Compared to other media such as potassium simplex optimization medium, commercial M16 (Sigma) media lacks of amino acid, glutamine and antibiotics. In the present study, we optimized M16 based embryo culture system using commercialized antibiotics-glutamine or amino acids supplements. In vivo derived murine zygote were M16 media were supplemented with commercial Penicillin-Streptomycin-Glutamine solution (PSG; Gibco) or MEM Non-Essential Amino Acids solution (NEAA; Gibco) as experimental design. Addition of PSG did not improved cleavage and blastocyst rates. On the other hand, cleavage rate is not different between control and NEAA treated group, however, blastocyst formation is significantly (P<0.05) improved in NEAA treated group. Developmental competence between PSG and NEAA treated groups were also compared. Between two groups, cleavage rate was similar. However, blastocyst formation rate is significantly improved in NEAA treated group. Taken together, beneficial effect of NEAA on murine embryos development was confirmed. Effect of antibiotics and glutamine addition to M16 media is still not clear in the study.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2888-2890
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• 2005

In this paper, we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C- Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.587-592
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• 2000

The optimization method based on an enhanced genetic algorithms is proposed for multimodal function optimization in this paper. This method is consisted of two main steps. The first step is global search step using the genetic algorithm(GA) and function assurance criterion(FAC). The belonging of an population to initial solution group is decided according to the FAC. The second step is to decide the similarity between individuals, and to research the optimum solutions by simplex method in reconstructive search space. Two numerical examples are also presented in this paper to comparing with conventional methods.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.423-434
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• 2004

In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the 'conventional' SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials as well as to consider a fixed number of input nodes at polynomial neurons (or nodes) located in each layer. However, this design process does not guarantee that the conventional SOPNN generated through learning results in optimal network architecture. The design procedure applied in the construction of each layer of the SOPNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomials, and input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between the approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented using pH neutralization process data as well as sewage treatment process data. A comparative analysis indicates that the proposed SOPNN is the model having higher accuracy as well as more superb predictive capability than other intelligent models presented previously.reviously.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.61-80
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• 2018

Location information of individual nodes is important in the implementation of necessary network functions. While extensive studies focus on localization techniques in 2D space, few approaches have been proposed for 3D positioning, which brings the location closer to the reality with more complex calculation consumptions for high accuracy. In this paper, an effective range-free localization scheme is proposed for 3D space localization, and the sensitivity of parameters is evaluated. Firstly, we present an improved algorithm (MDV-Hop), that the average distance per hop of the anchor nodes is calculated by root-mean-square error (RMSE), and is dynamically corrected in groups with the weighted RMSE based on group hops. For more improvement in accuracy, we expand particle swarm optimization (PSO) of intelligent optimization algorithms to MDV-Hop localization algorithm, called PMDV-hop, in which the parameters (inertia weight and trust coefficient) in PSO are calculated dynamically. Secondly, the effect of various localization parameters affecting the PMDV-hop performance is also present. The simulation results show that PMDV-hop performs better in positioning accuracy with limited energy.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.551-560
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• 2004

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. 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 (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series), A comparative analysis reveals that the proposed SOFPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literatures.

• Industrial Engineering and Management Systems
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• v.12 no.2
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• pp.151-160
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• 2013

In the past decades, several algorithms based on evolutionary approaches have been proposed for solving job shop scheduling problems (JSP), which is well-known as one of the most difficult combinatorial optimization problems. Most of them have concentrated on finding optimal solutions of a single objective, i.e., makespan, or total weighted tardiness. However, real-world scheduling problems generally involve multiple objectives which must be considered simultaneously. This paper proposes an efficient particle swarm optimization based approach to find a Pareto front for multi-objective JSP. The objective is to simultaneously minimize makespan and total tardiness of jobs. The proposed algorithm employs an Elite group to store the updated non-dominated solutions found by the whole swarm and utilizes those solutions as the guidance for particle movement. A single swarm with a mixture of four groups of particles with different movement strategies is adopted to search for Pareto solutions. The performance of the proposed method is evaluated on a set of benchmark problems and compared with the results from the existing algorithms. The experimental results demonstrate that the proposed algorithm is capable of providing a set of diverse and high-quality non-dominated solutions.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.662-674
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• 2017

This paper describes the multidisciplinary design optimization (MDO) process of a tailless unmanned combat aerial vehicle (UCAV) using global variable fidelity aerodynamic analysis. The developed tailless UAV design framework combines multiple disciplines that are based on low-fidelity and empirical analysis methods. An automated high-fidelity aerodynamic analysis is efficiently integrated into the MDO framework. Global variable fidelity modeling algorithm manages the use of the high-fidelity analysis to enhance the overall accuracy of the MDO by providing the initial sampling of the design space with iterative refinement of the approximation model in the neighborhood of the optimum solution. A design formulation was established considering a specific aerodynamic, stability and control design features of a tailless aircraft configuration with a UCAV specific mission profile. Design optimization problems with low-fidelity and variable fidelity analyses were successfully solved. The objective function improvement is 14.5% and 15.9% with low and variable fidelity optimization respectively. Results also indicate that low-fidelity analysis overestimates the value of lift-to-drag ratio by 3-5%, while the variable fidelity results are equal to the high-fidelity analysis results by algorithm definition.