• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5489-5511
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• 2019

The performance of evolutionary algorithms can be seriously weakened when constraints limit the feasible region of the search space. In this paper we present a constrained multi-objective optimization algorithm based on adaptive ε-truncation (ε-T-CMOA) to further improve distribution and convergence of the obtained solutions. First of all, as a novel constraint handling technique, ε-truncation technique keeps an effective balance between feasible solutions and infeasible solutions by permitting some excellent infeasible solutions with good objective value and low constraint violation to take part in the evolution, so diversity is improved, and convergence is also coordinated. Next, an exponential variation is introduced after differential mutation and crossover to boost the local exploitation ability. At last, the improved crowding density method only selects some Pareto solutions and near solutions to join in calculation, thus it can evaluate the distribution more accurately. The comparative results with other state-of-the-art algorithms show that ε-T-CMOA is more diverse than the other algorithms and it gains better in terms of convergence in some extent.

• 제어로봇시스템학회논문지
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• 제20권7호
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• pp.730-735
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• 2014

This paper introduces a GP (Genetic Programming) based robust technique for the prediction of a heavy rain newsflash. The nature of prediction for precipitation is very complex, irregular and highly fluctuating. Especially, the prediction of heavy precipitation is very difficult. Because not only it depends on various elements, such as location, season, time and geographical features, but also the case data is rare. In order to provide a robust model for precipitation prediction, a nonlinear and symbolic regression method using GP is suggested. The remaining part of the study is to evaluate the performance of prediction for a heavy rain newsflash using a GP based nonlinear regression technique in Korean regions. Analysis of the feature selection is executed and various fitness functions are proposed to improve performances. The KLAPS data of 2006-2010 is used for training and the data of 2011 is adopted for verification.

• 대한토목학회논문집
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• 제29권1A호
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• pp.9-18
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• 2009

본 논문은 외부 긴장된 텐던의 장력추정에 대한 차분진화기법의 적용을 소개한다. 제안된 차분진화 알고리즘의 SI기법은 기존의 구배 기반의 최적화 기법과는 다르게 전역해 탐색이 가능하다. 수치실험은 인식변수에 대한 사전정보 없이도, 제안된 차분진화기법이 외부긴장 텐던의 정확한 장력 추정뿐 아니라 유효공칭직경 추정이 가능하여 1%미만의 추정 오차를 갖는 유용한 기법임을 보여준다. 또한 긴장력 손실 유무의 사용 상태를 고려한 축소실험 모델 실험을 이용하여 제안된 기법의 타당성이 실험적으로 검증되었다. 실험의 결과는 긴장력 손실과 무관하게 정확한 장력 추정과 유효공칭직경의 추정뿐 아니라 실험 모델의 감쇠비까지 추정되어 제안된 기법이 적합하고, 효과적인 방법임을 보여준다. 유효공칭 직경의 2% 추정 오차는 실제 꼬여진 단면을 갖는 텐던의 직경과 충실단면을 갖는 FE 모델의 직경의 차이 때문이다. 마지막으로, 기존이론과의 비교 분석으로 제안된 차분진화 기법의 정확성과 우월성이 검증되었다.

• 인터넷정보학회논문지
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• 제17권5호
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• pp.89-95
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• 2016

In a software testing domain, test case prioritization techniques improve the performance of regression testing, and arrange test cases in such a way that maximum available faults be detected in a shorter time. User-sessions and cookies are unique features of web applications that are useful in regression testing because they have precious information about the application state before and after making changes to software code. This approach is in fact a user-session based technique. The user session will collect from the database on the server side, and test cases are released by the small change configuration of a user session data. The main challenges are the effectiveness of Average Percentage Fault Detection rate (APFD) and time constraint in the existing techniques, so in this paper developed an intelligent framework which has three new techniques use to manage and put test cases in group by applying useful criteria for test case prioritization in web application regression testing. In dynamic weighting approach the hybrid criteria which set the initial weight to each criterion determines optimal weight of combination criteria by evolutionary algorithms. The weight of each criterion is based on the effectiveness of finding faults in the application. In this research the priority is given to test cases that are performed based on most common http requests in pages, the length of http request chains, and the dependency of http requests. To verify the new technique some fault has been seeded in subject application, then applying the prioritization criteria on test cases for comparing the effectiveness of APFD rate with existing techniques.

• 한국정보통신학회논문지
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• 제14권11호
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• pp.2514-2520
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• 2010

본 논문은 진화기법을 이용하여 최적의 보안 대응전략을 자동생성 하는 방법의 제안을 목적으로 한다. 정보통신 환경에 대한 침해에 의한 피해가 급증함에 따라 다양한 보안 기술에 대한 연구가 활발히 이루어지고 있다. 그러나 다양한 네트워크 환경에 대한 보안 기술들의 연통 상황을 고려한 최적의 대응 전략을 생성하는데 어려움이었다. 따라서 본 논문에서는 대응방법을 유전자로 표현하여 유전 알고리즘을 적용함으로써 대응방법들에 대한 최적의 조합으로서 최적 대응 전략을 생성하였다. 또한 시뮬레이션을 이용하여 다양한 상황에 대한 대용방법의 적용에 따른 취약성을 정량적으로 평가함으로써 적합도를 평가하였다. 끝으로 제안한 방법을 구현한 시스템에 대한 실험을 통하여 타당성을 검토하였다.

• Earthquakes and Structures
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• 제1권1호
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• pp.43-67
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• 2010

A gradient-based evolutionary optimization methodology is presented for finding the optimal design of both the added dampers and their supporting members to minimize an objective function of a linear multi-storey structure subjected to the critical ground acceleration. The objective function is taken as the sum of the stochastic interstorey drifts. A frequency-dependent viscoelastic damper and the supporting member are treated as a vibration control device. Due to the added stiffness by the supplemental viscoelastic damper, the variable critical excitation needs to be updated simultaneously within the evolutionary phase of the optimal damper placement. Two different models of the entire damper unit are investigated. The first model is a detailed model referred to as "the 3N model" where the relative displacement in each component (i.e., the spring and the dashpot) of the damper unit is defined. The second model is a simpler model referred to as "the N model" where the entire damper unit is converted into an equivalent frequency-dependent Kelvin-Voigt model. Numerical analyses for 3 and 10-storey building models are conducted to investigate the characters of the optimal design using these models and to examine the validity of the proposed technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권6호
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• pp.2697-2710
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• 2018

Owing to the development of Internet of Things (IoT), the fifth-generation (5G) wireless communication is going to foresee a substantial increase of mobile traffic demand. Energy efficiency and spectral efficiency are the challenges in a 5G network. Non-orthogonal multiple access (NOMA) is a promising technique to increase the system efficiency by adaptive power control (PC) in a 5G network. This paper proposes an efficient PC scheme based on evolutionary game theory (EGT) model for uplink power-domain NOMA system. The proposed PC scheme allows users to adaptively adjusts their transmit power level in order to improve their payoffs or throughput which results in an increase of the system efficiency. In order to separate the user signals, a successive interference cancellation (SIC) receiver installed at the base station (BS) site. The simulation results demonstrate that the proposed EGT-based PC scheme outperforms the traditional game theory-based PC schemes and orthogonal multiple access (OMA) in terms of energy efficiency and spectral efficiency.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.158-163
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• 1998

This paper is our first attempt to construct a information processing system such as the living creatures' brain based on artificial life technique. In this paper, we propose a method of constructing neural networks using bio-inspired emergent and evolutionary concept, Ontogeny of living things is realized by cellular automata model and Phylogeny that is living things adaptation ability themselves to given environment, are realized by evolutionary algorithms. Proposing evolving cellular automata neural systems are calledin a word ECANS. A basic component of ECANS is 'cell' which is modeled on chaotic neuron with complex characteristics, In our system, the states of cell are classified into eight by method of connection neighborhood cells. When a problem is given, ECANS adapt itself to the problem by evolutionary method. For fixed cells transition rule, the structure of neural network is adapted by change of initial cell' arrangement. This initial cell is to become a network b developmental process. The effectiveness and the capability of proposed scheme are verified by applying it to pattern classification and robot control problem.

• 대한기계학회논문집A
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• 제25권12호
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• pp.2062-2069
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• 2001

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses synthetic artificial intelligent technique, that is, Adaptive-Network-based Fuzzy Inference System(ANFIS) solved via hybrid learning algorithm(the back-propagation gradient descent and the least-squares method) are used to learn the input(the location and depth of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this ANFIS and a continuous evolutionary algorithm(CEA), it is possible to formulate the inverse problem. CEAs based on genetic algorithms work efficiently for continuous search space optimization problems like a parameter identification problem. With this ANFIS, CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집C
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• pp.182-188
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• 2001

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses synthetic artificial intelligent technique, that is, Adaptive-Network-based Fuzzy Inference System(ANFIS) solved via hybrid learning algorithm(the back-propagation gradient descent and the least-squares method) are used to learn the input(the location and depth of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this ANFIS and a continuous evolutionary algorithm(CEA), it is possible to formulate the inverse problem. CEAs based on genetic algorithms work efficiently for continuous search space optimization problems like a parameter identification problem. With this ANFIS, CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising.