• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.101-110
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• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• Journal of Electrical Engineering and Technology
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• 제5권1호
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• pp.70-78
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• 2010

Elitist nondominated sorting genetic algorithm (NSGA-II) is adopted and improved for multiobjective optimal reactive power flow (ORPF) problem. Multiobjective ORPF, formulated as a multiobjective mixed integer nonlinear optimization problem, minimizes real power loss and improves voltage profile of power grid by determining reactive power control variables. NSGA-II-based ORPF is tested on standard IEEE 30-bus test system and compared with four other state-of-the-art multiobjective evolutionary algorithms (MOEAs). Pareto front and outer solutions achieved by the five MOEAs are analyzed and compared. NSGA-II obtains the best control strategy for ORPF, but it suffers from the lower convergence speed at the early stage of the optimization. Several problem-specific local search strategies (LSSs) are incorporated into NSGA-II to promote algorithm's exploiting capability and then to speed up its convergence. This enhanced version of NSGA-II (ENSGA) is examined on IEEE 30 system. Experimental results show that the use of LSSs clearly improved the performance of NSGA-II. ENSGA shows the best search efficiency and is proved to be one of the efficient potential candidates in solving reactive power optimization in the real-time operation systems.

• Smart Structures and Systems
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• 제26권6호
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• pp.721-733
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• 2020

The present study investigates the employing of piezoelectric patches in active control of a sandwich plate. Indeed, the active control and optimal patch distribution on this structure are presented together. A sandwich plate with honeycomb core and composite reinforced by carbon nanotubes in facesheet layers is considered so that the optimum position of actuator/sensor patches pair is guaranteed to suppress the vibration of sandwich structures. The sandwich panel consists of a search space which is a square of 200 × 200 mm with a numerous number of candidates for the optimum position. Also, different dimension of square and rectangular plates to obtain the optimal placement of piezoelectric actuator/senor patches pair is considered. Based on genetic algorithm and LQR, the optimum position of patches and fitness function is determined, respectively. The present study reveals that the efficiency and performance of LQR control is affected by the optimal placement of the actuator/sensor patches pair to a large extent. It is also shown that an intelligent selection of the parent, repeated genes filtering, and 80% crossover and 20% mutation would increase the convergence of the algorithm. It is noted that a fitness function is achieved by collection actuator/sensor patches pair cost functions in the same position (controllability). It is worth mentioning that the study of the optimal location of actuator/sensor patches pair is carried out for different boundary conditions of a sandwich plate such as simply supported and clamped boundary conditions.

• 대한산업공학회지
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• 제37권4호
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• pp.331-341
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• 2011

This paper considers an inbound lot-sizing and outbound dispatching problem for a single product in a thirdparty logistics (3PL) distribution center. Demands are dynamic and finite over the discrete time horizon, and moreover, each demand has a delivery time window which is the time interval with the dates between the earliest and the latest delivery dates All the product amounts must be delivered to the customer in the time window. Ordered products are shipped by multiple vehicle types and the freight cost is proportional to the vehicle-types and the number of vehicles used. First, we formulate a mixed integer programming model. Since it is difficult to solve the model as the size of real problem being very large, we design a conventional genetic algorithm with a local search heuristic (HGA) and an improved genetic algorithm called adaptive genetic algorithm (AGA). AGA spontaneously adjusts crossover and mutation rate depending upon the status of current population. Finally, we conduct some computational experiments to evaluate the performance of AGA with HGA.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2016년도 추계학술대회
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• pp.729-731
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• 2016

본 논문에서는 제안한 알고리즘은 이전 유전 알고리즘의 분산처리를 위해 라우터 그룹 단위인 셀을 도입하였다. 기존 최적경로 알고리즘인 Dijkstra 알고리즘에서 네트워크가 손상되었을 경우 제안한 알고리즘에는 대체 경로 설정의 연산시간이 단축되었으며 손상된 네트워크의 셀 안에서 2순위의 경로를 가지고 있으므로 Dijkstra 알고리즘보다 신속하게 대체경로를 설정하도록 설계되었다. 이는 제안한 알고리즘이 네트워크상에서 Dijkstra 알고리즘이 손상되었을 경우 대체 경로설정을 보완할 수 있음을 확인하였다.

• 전자공학회논문지CI
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• 제44권2호
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• pp.11-18
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• 2007

신경회로망의 학습에 널리 사용되고 있는 오차역전파 알고리즘은 최급하강법을 기초로 하고 있기 때문에 초기값에 따라서는 극소값에 떨어지거나, 신경회로망을 학습시킬 때 중간층 유닛수를 얼마로 설정하는 등의 문제점이 있다. 따라서 이러한 문제점을 해결하기 위하여, 본 논문에서는 3비트 패리티 판별을 위하여 신경회로망의 학습에 교차법, 돌연변이법에 새로운 기법을 도입한 개량형 유전적 알고리즘을 제안한다. 본 논문에서는 세대차이, 중간층 유닛수의 차이, 집단의 개체수의 차이에 대하여 실험을 실시하여, 본 방식이 학습 속도의 면에서 유효하다는 것을 나타낸다.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.55-64
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• 2023

The Internet of Things (IoT) is the combination of the internet and various sensing devices. IoT security has increasingly attracted extensive attention. However, significant losses appears due to malicious attacks. Therefore, intrusion detection, which detects malicious attacks and their behaviors in IoT devices plays a crucial role in IoT security. The intrusion detection system, namely IDS should be executed efficiently by conducting classification and efficient feature extraction techniques. To effectively perform Intrusion detection in IoT applications, a novel method based on a Conventional Neural Network (CNN) for classification and an improved Genetic Algorithm (GA) for extraction is proposed and implemented. Existing issues like failing to detect the few attacks from smaller samples are focused, and hence the proposed novel CNN is applied to detect almost all attacks from small to large samples. For that purpose, the feature selection is essential. Thus, the genetic algorithm is improved to identify the best fitness values to perform accurate feature selection. To evaluate the performance, the NSL-KDDCUP dataset is used, and two datasets such as KDDTEST²¹ and KDDTEST⁺ are chosen. The performance and results are compared and analyzed with other existing models. The experimental results show that the proposed algorithm has superior intrusion detection rates to existing models, where the accuracy and true positive rate improve and the false positive rate decrease. In addition, the proposed algorithm indicates better performance on KDDTEST⁺ than KDDTEST²¹ because there are few attacks from minor samples in KDDTEST⁺. Therefore, the results demonstrate that the novel proposed CNN with the improved GA can identify almost every intrusion.

• 대한인간공학회지
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• 제20권1호
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• pp.45-62
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• 2001

This study suggests a genetic algorithm-based partial least squares (GA-based PLS) method to select the design variables for building a usability model. The GA-based PLS uses a genetic algorithm to minimize the root-mean-squared error of a partial least square regression model. A multiple linear regression method is applied to build a usability model that contains the variables seleded by the GA-based PLS. The performance of the usability model turned out to be generally better than that of the previous usability models using other variable selection methods such as expert rating, principal component analysis, cluster analysis, and partial least squares. Furthermore, the model performance was drastically improved by supplementing the category type variables selected by the GA-based PLS in the usability model. It is recommended that the GA-based PLS be applied to the variable selection for developing a usability model.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권12호
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• pp.667-674
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• 2000

In this paper, a modified real-valued genetic algorithm is developed by using the meiosis for human's chromosome. Unlike common crossover methods adapted in the conventional genetic algorithms, our suggested modified real-valued genetic algorithm makes gametes by conducting the meiosis for individuals composed of chromosomes, and then generates a new individual through crossovers among those. Ultimately, when appling it for the gas data of Box-Jenkin, model and parameter identifications can be concurrently done to construct the optimal model of a neural network in terms of minimizing with the structure and the error.

• 한국경영과학회지
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• 제32권4호
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• pp.1-18
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• 2007

In this paper, we develop an adaptive genetic algorithm (aGA). The aGA has an adaptive scheme which can automatically determine the use of local search technique and adaptively regulate the rates of crossover and mutation operations during its search process. For the adaptive scheme, the ratio of degree of dispersion resulting from the various fitness values of the populations at continuous two generations is considered. For the local search technique, an improved iterative hill climbing method is used and incorporated into genetic algorithm (GA) loop. In order to demonstrate the efficiency of the aGA, i) a canonical GA without any adaptive scheme and ii) several conventional aGAs with various adaptive schemes are also presented. These algorithms, including the aGA, are tested and analyzed each other using various test problems. Numerical results by various measures of performance show that the proposed aGA outperforms the conventional algorithms.