• Structural Engineering and Mechanics
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• 제54권6호
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• pp.1097-1109
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• 2015

A novel optimization approach for reinforced concrete (RC) biaxially loaded columns is proposed. Since there are several design constraints and influences, a new computation methodology using iterative analyses for several stages is proposed. In the proposed methodology random iterations are combined with music inspired metaheuristic algorithm called harmony search by modifying the classical rules of the employed algorithm for the problem. Differently from previous approaches, a detailed and practical optimum reinforcement design is done in addition to optimization of dimensions. The main objective of the optimization is the total material cost and the optimization is important for RC members since steel and concrete are very different materials in cost and properties. The methodology was applied for 12 cases of flexural moment combinations. Also, the optimum results are found by using 3 different axial forces for all cases. According to the results, the proposed method is effective to find a detailed optimum result with different number of bars and various sizes which can be only found by 2000 trial of an engineer. Thus, the cost economy is provided by using optimum bars with different sizes.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2005년도 춘계종합학술대회
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• pp.671-674
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• 2005

본 논문은 웹기반 게임 컨텐츠로써의 최적 미로 게임 개발에 대한 논문이다. 웹을 이용한 클라이언트 접속자는 자바 애플릿을 이용하여 웹상에서 접근 가능하고 Java Bytecode의 다운로드에 의해 각 클라이언트 접속자의 하드웨어시스템에 무관하게 작동가능하다. 본 논문에서 개발된 최적 미로 게임은 랜덤 미로 생성기, 미로 내 경로 입력기, 가중 최적 경로 탐색기, 및 비교기 등으로 구성되어 있다. 최적 미로 탐색 알고리즘으로 탐색된 경로와 사용자가 선택한 경로의 cost 비교를 통하여 사용자의 지리적 인지력을 정량적으로 평가 및 도시함으로써 사용자의 지리적 인지력 향상을 유도할 수 있다.

• 한국지능시스템학회논문지
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• 제18권3호
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• pp.291-296
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• 2008

본 논문에서는 군집로봇시스템에서 목표물 추적을 위하여 SVM을 이용한 12각형 기반의 Q-learning 알고리즘을 제안한다. 제안한 알고리즘의 유효성을 보이기 위해 본 논문에서는 여러 대의 로봇과 장애물 그리고 하나의 목표물로 정하고, 각각의 로봇이 숨겨진 목표물을 찾아내는 실험을 가정하여 무작위, DBAM과 AMAB의 융합 모델, 마지막으로는 본 논문에서 제안한 SVM과 12각형 기반의 Q-learning 알고리즘을 이용하여 실험을 수행하고, 이 3가지 방법을 비교하여 본 논문의 유효성을 검증하였다.

• Nuclear Engineering and Technology
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• 제51권3호
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• pp.825-836
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• 2019

One of the most challenging safety precautions for workers in dynamic, radioactive environments is avoiding radiation sources and sustaining low exposure. This paper presents a sampling-based algorithm, DL-RRT*, for minimum dose walk-path re-planning in radioactive environments, expedient for occupational workers in nuclear facilities to avoid unnecessary radiation exposure. The method combines the principle of random tree star ($RRT^*$) and $D^*$ Lite, and uses the expansion strength of grid search strategy from $D^*$ Lite to quickly find a high-quality initial path to accelerate convergence rate in $RRT^*$. The algorithm inherits probabilistic completeness and asymptotic optimality from $RRT^*$ to refine the existing paths continually by sampling the search-graph obtained from the grid search process. It can not only be applied to continuous cost spaces, but also make full use of the last planning information to avoid global re-planning, so as to improve the efficiency of path planning in frequently changing environments. The effectiveness and superiority of the proposed method was verified by simulating radiation field under varying obstacles and radioactive environments, and the results were compared with $RRT^*$ algorithm output.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1995년도 춘계공동학술대회논문집; 전남대학교; 28-29 Apr. 1995
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• pp.47-53
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• 1995

The Generalized Assignment (GAP) determines the minimum assignment of n tasks to m workstations such that each task is assigned to exactly one workstation, subject to the capacity of a workstation. In this paper, we presented a new heuristic search algorithm for GAPs. Then we tested it on 4 different benchmark sample sets of random problems generated according to uniform distribution on a microcomputer.

• Journal of Multimedia Information System
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• 제4권4호
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• pp.317-320
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• 2017

This paper examines approaches to the development of algorithms of parametric identification of models of complex systems from discrete observations. A modification of a known algorithm Kaczmarz which is designed for closed systems with perturbations, based on the methods of random search and investigates their statistical properties.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.110.2-110
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• 2002

Evolutionary Algorithm is the general method that can search the optimum value for the various problems. Evolutionary method consists of random selection, crossover, mutation, etc. Since the next generation is selected based on the fitness values, the crossover between chromosomes does not have any restrictions. Not only normal marriage but also consanguineous marriage will take place. In human world, consanguineous marriage was reported to cause various genetic defects, such as poor immunity about new diseases and new environment disaster, These problems translate into searching for the local optimum, not the global optimum. So, a new evolutionary algorithm is needed that prevents traps to...

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 춘계학술대회논문집
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• pp.400-403
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• 2004

This paper presents an optimum design of journal bearings using a hybrid method to find the solutions of optimization problem. The present hybrid algorithm, namely Enhanced Artificial Life Algorithm(EALA), is a synthesis of an artificial life algorithm(ALA) and the random tabu search(R-tabu) method. EALA is applied to the optimum design of journal bearings supporting simple rotor. The applicability of EALA to optimum design of rotor-bearing system is exemplified through this study.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.255-255
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• 2000

Technical advance of electrical chart and cruising equipment make it possible to sail without a man. It is important to decide the cruising route in view of effectiveness and stability of a ship. So we need to study on the optimal trajectory planning. Genetic algorithm is a strong optimization algorithm with adaptational random search. It is a good choice to apply genetic algorithm to the trajectory planning of a ship. We modify a genetic algorithm to solve this problem. The effectiveness of the revised genetic algorithm is assured through computer simulations.

• 한국게임학회 논문지
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• 제16권6호
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• pp.79-90
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• 2016

최근 게임분야에서 수준 높은 인공지능 에이전트의 구현은 많은 주목을 받고 있다. 그 중 Monte-Carlo Tree Search(MCTS)는 완전 정보를 가진 게임에서 무작위 탐색을 통해 최적의 해를 구할 수 있는 알고리즘으로, 수식으로 표현되지 않는 경우에 근사치를 계산하는 용도로 적합하다. 하스스톤과 같은 Trading Card Game(TCG) 장르의 게임은 상대방의 카드와 플레이를 예측할 수 없기 때문에 불완전 정보를 가지고 있다. 본 논문에서는 불완전 정보 카드 게임에서 인공지능 에이전트를 생성하기 위해 MCTS 알고리즘을 응용하는 방법을 제안하고, 현재 서비스되는 하스스톤 게임에 적용하여 봄으로써 MCTS 알고리즘의 실용성을 검증한다.