• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.2542-2547
• /
• 2005

In this paper, we propose a support vector machine (SVM) hyper and kernel parameter optimization method which is based on minimizing radius/margin bound which is a kind of estimation of leave-one-error. This method uses dynamic encoding algorithm for search (DEAS) and gradient information for better optimization performance. DEAS is a recently proposed optimization algorithm which is based on variable length binary encoding method. This method has less computation time than genetic algorithm (GA) based and grid search based methods and better performance on finding global optimal value than gradient based methods. It is very efficient in practical applications. Hand-written letter data of MNI steel are used to evaluate the performance.

• Nuclear Engineering and Technology
• /
• v.52 no.3
• /
• pp.544-551
• /
• 2020

Reactor control is a standout amongst the most vital issues in the nuclear power plant. In this paper, the optimal design of variable structure controller (VSC) based on the lightning search algorithm (LSA) is proposed for a nuclear reactor power system. The LSA is a new optimization algorithm. It is used to find the optimal parameters of the VSC instead of the trial and error method or experts of the designer. The proposed algorithm is used for the tuning of the feedback gains and the sliding equation gains of the VSC to prove a good performance. Furthermore, the parameters of the VSC are tuned by the genetic algorithm (GA). Simulation tests are carried out to verify the performance and robustness of the proposed LSA-based VSC compared with GA-based VSC. The results prove the high performance and the superiority of VSC based on LSA compared with VSC based on GA.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.26 no.1
• /
• pp.74-81
• /
• 2017

A crane is typically used as a means to lift and load equipment or materials. A surface vessel uses a towed object for underwater activity. Such a mechanism for dropping and lifting of equipment is necessary, and is called an overboarding unit. The present study is focused on the overboarding unit used for a crane structure. This paper deals with new overboarding mechanism design and GA-based $MATLAB^{(R)}$ optimization. By using a quadrilateral link mechanism, it is possible to set the constraint function for optimizing the GA method. The optimization with $MATLAB^{(R)}$ is followed by the $SolidWorks^{(R)}$ simulation and verification. When applying the proposed mechanism, the operator is expected to have a big advantage in safety and efficiency of operations. Furthermore, the technology developed in this study will be helpful in similar circumstances and in the proposed mechanism.

• Proceedings of the KIEE Conference
• /
• 2007.04a
• /
• pp.207-209
• /
• 2007

In this paper, we introduce a genetic optimization of fuzzy set-fuzzy model using successive tuning method to carry out the model identification of complex and nonlinear systems. To identity we use genetic alrogithrt1 (GA) sand C-Means clustering. GA is used for determination the number of input, the seleced input variables, the number of membership function, and the conclusion inference type. Information Granules (IG) with the aid of C-Means clustering algorithm help determine the initial paramters of fuzzy model such as the initial apexes of the, membership functions in the premise part and the initial values of polyminial functions in the consequence part of the fuzzy rules. The overall design arises as a hybrid structural and parametric optimization. Genetic algorithms and C-Means clustering are used to generate the structurally as well as parametrically optimized fuzzy model. To identify the structure and estimate parameters of the fuzzy model we introduce the successive tuning method with variant generation-based evolution by means of GA. Numerical example is included to evaluate the performance of the proposed model.

• Journal of the Korea Institute of Building Construction
• /
• v.12 no.6
• /
• pp.664-673
• /
• 2012

Determining the layout of temporary facilities that support construction activities at a site is an important planning activity, as layout can significantly affect cost, quality of work, safety, and other aspects of the project. The construction site layout problem involves difficult combinatorial optimization. Recently, various artificial intelligence(AI)-based algorithms have been applied to solving many complex optimization problems, including neural networks(NN), genetic algorithms(GA), and swarm intelligence(SI) which relates to the collective behavior of social systems such as honey bees and birds. This study proposes a site facility layout optimization algorithm based on self-organizing maps(SOM). Computational experiments are carried out to justify the efficiency of the proposed method and compare it with particle swarm optimization(PSO). The results show that the proposed algorithm can be efficiently employed to solve the problem of site layout.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.3 no.1
• /
• pp.115-125
• /
• 1999

Simple Genetic Algorithm(SGA) proposed by J. H. Holland is a population-based optimization method based on the principle of the Darwinian natural selection. The theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. Although GA does well in many applications as an optimization method, still it does not guarantee the convergence to a global optimum in some problems. In designing intelligent systems, specially, since there is no deterministic solution, a heuristic trial-and error procedure is usually used to determine the systems' parameters. As an alternative scheme, therefore, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve. In this paper we review the existing co-evolutionary algorithms and propose co-evolutionary schemes designing intelligent systems according to the relation between the system's components.

• Journal of Electrical Engineering and Technology
• /
• v.8 no.2
• /
• pp.223-229
• /
• 2013

Restructuring and recent developments in the power system and problems arising from construction and maintenance of large power plants, increasing amount of interest in distributed generation (DG) source. Distributed generation units due to specifications, technology and location network connectivity can improve system and load point reliability indices. In this paper, the allocation and sizing of DG in distribution networks are determined using optimization. The objective function of the proposed method is to improve customer-based reliability indices at lowest cost. The placement and size of DGs are optimized using a Genetic Algorithm (GA). To evaluate the proposed algorithm, 34-bus IEEE test system, is used. The results illustrate efficiency of the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.10a
• /
• pp.553-559
• /
• 1998

Simple Genetic Algorithm(SGA) proposed by J. H. Holland is a population-based optimization method based on the principle of the Darwinian natural selection. The theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. Although GA goes well in many applications as an optimization method, still it does not guarantee the convergence to a global optimum in some problems. In designing intelligent systems, specially, since there is no deterministic solution, a heuristic trial-and error procedure is usually used to determine the systems' parameters. As an alternative scheme, therefore, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve. In this paper we review the existing co-evolutionary algorithms and propose co-evolutionary schemes designing intelligent systems according to the relation between the system's components.

• Proceedings of the KIEE Conference
• /
• 2003.07d
• /
• pp.2040-2042
• /
• 2003

In this paper, we propose a new method about wavelet-based fuzzy modeling using a DNA coding method. DNA coding techniques is known that expression of knowledge is various than Genetic Algorithm(GA) usually by made optimization technique because done base in structure of biologic DNA and optimization performance is superior. The reposed method make fuzzy system model in wavelet transform and equivalence relation after identification with coefficient of wavelet transform using a DNA coding techniques. Also, can get fuzzy model effectively of nonlinear system using advantage of strong wavelet transform about function that have sudden change. In this paper, in order to demonstrate the superiority of the proposed method compared with GA.

• Journal of Microbiology and Biotechnology
• /
• v.16 no.6
• /
• pp.863-869
• /
• 2006

The behavior of submerged membrane bioreactor (SMBR) filtration systems utilizing rapid air backpulsing as a cleaning technique to remove reversible foulants was investigated using a genetic algorithm (GA). A customized genetic algorithm with suitable genetic operators was used to generate optimal time profiles. From experiments utilizing short and long periods of forward and reverse filtration, various experimental process parameters were determined. The GA indicated that the optimal values for the net flux fell between 263-270 LMH when the forward filtration time ($t_f$) was 30-37 s and the backward filtration time ($t_b$) was 0.19-0.27 s. The experimental data confirmed the optimal backpulse duration and frequency that maximized the net flux, which represented a four-fold improvement in 24-h backpulsing experiments compared with the absence of backpulsing. Consequently, the identification of a region of feasible parameters and nonlinear flux optimization were both successfully performed by the genetic algorithm, meaning the genetic algorithm-based optimization proved to be useful for solving SMBR flux optimization problems.