• 한국CDE학회논문집
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• 제19권4호
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• pp.410-422
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• 2014

Unlike other weapon systems, a naval vessel has unique characteristics in that the vessel itself is a naval unit. In limited space, compartments with various objectives and characteristics need to be arranged, so that vessel performance is maximized. This paper studied a compartment arrangement algorithm that considers activity relationships among compartments and survivability of a vessel. Based on the study, a compartment arrangement application is developed that can generate various layout alternatives swiftly. The application developed in this study aims at automating a two dimensional compartment layout problem. A combinatorial optimization is performed with the differential evolution algorithm to achieve the optimized layout.

• 한국산학기술학회논문지
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• 제13권12호
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• pp.5704-5708
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• 2012

본 논문에서는 DEA(Differential Evolution Algorithm)기법을 이용하여 트래버스 연삭의 최적 조건을 선정하기 위한 알고리즘을 제안하였다. 최적 연삭 조건 선정을 위한 가격함수는 가공 경비, 생산율 및 표면 거칠기 등의 다중 함수식을 이용하였다. 또한 연삭 조건에 대한 구속 조건으로 열 손상 효과, 가공 툴의 강성, 연삭 휠 마모 상수 및 표면 거칠기 등을 고려하였다. 알고리즘의 구현은 산업현장에서 널리 사용되는 LabView소프트웨어를 사용하였다. 제안된 알고리즘의 성능은 컴퓨터 시뮬레이션을 통해 GA알고리즘의 결과와 비교하여 검증하였다.

• Advances in Computational Design
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• 제1권4호
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• pp.315-327
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• 2016

Damage detection and localisation in structures is essential since it can be a means for preventive maintenance of those structures under service conditions. The use of structural modal data for detecting the damage is one of the most efficient methods. This paper presents comparative performance of various state-of-the-art meta-heuristics for use in structural damage detection based on changes in modal data. The metaheuristics include differential evolution (DE), artificial bee colony algorithm (ABC), real-code ant colony optimisation (ACOR), charged system search (ChSS), league championship algorithm (LCA), simulated annealing (SA), particle swarm optimisation (PSO), evolution strategies (ES), teaching-learning-based optimisation (TLBO), adaptive differential evolution (JADE), evolution strategy with covariance matrix adaptation (CMAES), success-history based adaptive differential evolution (SHADE) and SHADE with linear population size reduction (L-SHADE). Three truss structures are used to pose several test problems for structural damage detection. The meta-heuristics are then used to solve the test problems treated as optimisation problems. Comparative performance is carried out where the statistically best algorithms are identified.

• 한국경영과학회지
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• 제31권4호
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• pp.139-155
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• 2006

The facility location problem of this paper is distinguished from the maximal covering location problem and the flxed-charge facility location problem. We propose the maximal profiting location problem (MPLP) that is the facility location problem maximizing profit with multi-product. We apply to the simulated annealing algorithm, the stochastic evolution algorithm and the accelerated simulated annealing algorithm to solve this problem. Through a scale-down and extension experiment, the MPLP was validated and all the three algorithm enable the near optimal solution to produce. As the computational complexity is increased, it is shown that the simulated annealing algorithm' is able to find the best solution than the other two algorithms in a relatively short computational time.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권4호
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• pp.265-276
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• 2004

Genetic Algorithm(GA), that is shown stable performance to find an optimal solution, has been used as a method of solving large-scaled optimization problems with complex constraints in various applications. Since it takes so much time to execute a long computation process for iterative evolution and adaptation. In this paper, a hardware-based adaptive GA was proposed to reduce the serious computation time of the evolutionary process and to improve the accuracy of convergence to optimal solution. The proposed GA, based on steady-state model among continuos generation model, performs an adaptive mutation process with consideration of the evolution flow and the population diversity. The drawback of the GA, premature convergence, was solved by the proposed adaptation. The Performance improvement of convergence accuracy for some kinds of problem and condition reached to 5-100% with equivalent convergence speed to high-speed algorithm. The proposed adaptive GAP(Genetic Algorithm Processor) was implemented on FPGA device Xilinx XCV2000E of EHW board for face recognition.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권3호
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• pp.185-190
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• 2002

In distributed autonomous robotic systems (DARS), each robot must behave by itself according to its states ad environments, and if necessary, must cooperate with other robots in order to carry out their given tasks. Its most significant merit is that they determine their behavior independently, and cooperate with other robots in order to perform the given tasks. Especially, in DARS, it is essential for each robot to have evolution ability in order to increase the performance of system. In this paper, a schema co-evolutionary algorithm is proposed for the evolution of collective autonomous mobile robots. Each robot exchanges the information, chromosome used in this algorithm, through communication with other robots. Each robot diffuses its chromosome to two or more robots, receives other robot's chromosome and creates new species. Therefore if one robot receives another robot's chromosome, the robot creates new chromosome. We verify the effectiveness of the proposed algorithm by applying it to cooperative search problem.

• 한국산학기술학회논문지
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• 제14권1호
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• pp.7-12
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• 2013

본 논문은 연삭 공정의 최적화 알고리즘과 이를 구현하기 위한 방안을 제안하였다. 최적의 연삭 공정 설계를 위해서 최적화 함수를 제안하고 선정된 최적 함수의 해를 구하기 위해 DE(Differential Evolution)알고리즘을 이용하였다. 알고리즘의 구현은 산업현장에서 널리 사용되고 있는 LabView소프트웨어를 통해 구현하였고 컴퓨터 시뮬레이션을 통해 제안된 알고리즘을 검증하였다. 본 논문에서 획득한 최적화 기법은 연삭공정의 가이드라인으로 활용 될 수 있을 것으로 사료된다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권4호
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• pp.302-309
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• 2005

We have studied a model and application that evolves the behavioral knowledge of a virtual robot. The knowledge is represented in classification rules and a neural network, and is learned by a genetic algorithm. The model consists of a virtual robot with behavior knowledge, an environment that it moves in, and an evolution performer that includes a genetic algorithm. We have also applied our model to an environment where the robots gather food into a nest. When comparing our model with the conventional method on various test cases, our model showed superior overall learning.

• 한국지능시스템학회논문지
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• 제17권5호
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• pp.591-597
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• 2007

군집 로봇시스템에서 개개의 로봇은 스스로 주위의 환경과 자신의 상태를 스스로 판단하여 행동하고, 필요에 따라서는 다른 로봇과 협조를 통하여 어떤 주어진 일을 수행할 수 있어야 한다. 따라서 개개의 로봇은 동적으로 변화하는 환경에 잘 적응할 수 있는 학습과 진화능력을 갖는 것이 필수적이다 이를 위하여 본 논문에서는 새로운 Polygon 기반의 Q-learning 알고리즘과 분산유전알고리즘을 이용한 새로운 자율이동로봇의 행동학습 및 진화방법을 제안한다. 또한 개개의 로봇이 통신을 통하여 염색체를 교환하는 분산유전알고리즘은 각기 다른 환경에서 학습한 우수한 염색체로부터 자신의 능력을 향상시킨다. 특히 본 논문에서는 진화의 성능을 향상시키기 위하여 강화학습의 특성을 이용한 선택 교배방법을 채택하였다. 제안된 방법은 협조탐색 문제에 적용하여 컴퓨터 모의실험을 통하여 그 유효성을 검증한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.82-85
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• 2003

In this paper, in order to deduce the deep structure of a set of fuzzy rules from the surface structure, we use co-evolutionary algorithm based on modified Nash GA. This algorithm coevolves membership functions in antecedents and parameters in consequents of fuzzy rules. We demonstrate this co-evolutionary algorithm and apply to the mobile robot control. From the result of simulation, we compare modified Nash GA with the other co-evolution algorithms and verify the efficacy of this algorithm through application to fuzzy systems.