• 한국정보과학회논문지:시스템및이론
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• 제32권8호
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• pp.399-410
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• 2005

본 논문에서는 개미 군 집단 알고리즘을 융합한 새로운 적응형 유전 알고리즘을 제안하고, 제안된 알고리즘이 확률적으로 최적 해에 수렴함을 증명한다. 실험을 통해서, 제안된 알고리즘은 최적 해로의 수렴이 어려운 여러 가지 대표적인 함수들에 대하여 elitist 전략을 사용한 유전 알고리즘보다 더 빠른 속도로 최적 해에 수렴하고 한 군집 내의 모든 해들이 최적 해로 수렴하며 파라미터 값에 따라 새로운 탐색이나 현 상태로의 귀착의 정도를 조절할 수 있는 유연성 있는 알고리즘인 것을 보인다.

• Industrial Engineering and Management Systems
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• 제4권2호
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• pp.184-191
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• 2005

In this article we study the feasibility of the Ant Colony Optimisation (ACO) algorithm for finding optimal Kanban allocations in Kanban systems represented by Stochastic Petri Net (SPN) models. Like other optimisation algorithms inspired by nature, such as Simulated Annealing/Genetic Algorithms, the ACO algorithm contains a large number of adjustable parameters. Thus we study the influence of the parameters on performance of ACO on the Kanban allocation problem, and identify the most important parameters.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2008년도 추계학술대회 및 정기총회
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• pp.308-311
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• 2008

The ant colony optimization (ACO) is a metaheuristic inspired by the behavior of real ants. Recently, ACO has been widely used to solve the difficult combinatorial optimization problems. In this paper, we propose an ACO algorithm to solve the two disjoint paths problem with dual link cost structure (TDPDCP). We propose a dual pheromone structure and a procedure for solution construction which is appropriate for the TDPDCP. Computational comparisons with the state-of-the-arts algorithms are also provided.

• 대한전자공학회논문지TC
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• 제40권12호
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• pp.71-82
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• 2003

본 논문은 real-time 어플리케이션들을 위한 보나 효과적이고 효율적으로 ant-like mobile agent들이 QoS metrics를 고려하여 네트워크상에서 목적지까지 가장 최적화된 route을 찾는 Ant 알고리듬을 바탕으로 한 QoS 라우팅 알고리듬에서의 route 선택 강화 계산방법을 제시한다. 시뮬레이션 결과 본 논문에서 제시하는 방법이 기존의 방법보다 delay jitter와 bandwidth를 우선으로 하는 real-time application에 대한 가장 최적화된 route을 보다 효과적이고 보다 네트워크 환경에 적응적으로 찾아내는 것을 확인하였다.

• 대한산업공학회지
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• 제35권2호
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• pp.160-170
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• 2009

This paper studies a vehicle routing problem variant which considers customers to require simultaneous delivery and pick-up under time windows(VRPSDP-TW). The objective of this paper is to minimize the total travel distance of routes that satisfy both the delivery and pick-up demand. We propose a heuristic algorithm for solving the VRPSDP-TW, based on the ant colony system(ACS). In route construction, an insertion algorithm based ACS is applied and the interim solution is improved by local search. Through iterative processes, the heuristic algorithm drives the best solution. Experiments are implemented to evaluate a performance of the algorithm on some test instances from literature.

• 한국산업정보학회논문지
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• 제13권5호
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• pp.142-148
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• 2008

Facility Layout Planning is concerned with how to arrange facilities necessary for production in a given space. Its objective is often to minimize the total sum of all material flows multiplied by the distance among facilities. FLP belongs to NP complete problem; i.e., the number of possible layout solutions increases with the increase of the number of facilities. Thus, meta heuristics such as Genetic Algorithm (GA) and Simulated Annealing have been investigated to solve the FLP problems. However, one of the biggest problems which lie in the existing meta heuristics including GA is hard to find an appropriate combinations of parameters which result in optimal solutions for the specific problem. The Ant System algorithm with elitist and ranking strategies is used to solve the FLP problem as an another good alternative. Experimental results show that the AS algorithm is able to produce the same level of solution quality with less sensitive parameters selection comparing to the ones obtained by applying other existing meta heuristic algorithms.

• 한국콘텐츠학회논문지
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• 제10권5호
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• pp.1-9
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• 2010

이 논문은 특징 선택에 사용되는 개미 군락 최적화의 수렴 특성을 개선하기 위해 선택적 평가라는 새로운 기법을 제시한다. 이 방법은 불필요하거나 가능성이 덜한 후보 해를 배제함으로써 계산량을 줄인다. 이 방법은, 그런 해를 찾아내는데 사용할 수 있는 페로몬 정보 때문에 구현이 가능하다. 문제 크기에 따른 알고리즘의 적용가능성을 판단할 목적으로, 특징 선택에 사용되는 세 가지 알고리즘인 탐욕 알고리즘, 유전 알고리즘, 그리고 개미 군락 최적화의 계산 시간을 분석한다. 엄밀한 분석을 위해 원자 연산이라는 개념을 사용한다. 실험 결과는 선택적 평가를 채택한 개미 군락 최적화가 계산 시간과 인식 성능 모두에서 우수함을 보여준다.

• 제어로봇시스템학회논문지
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• 제10권12호
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• pp.1127-1136
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• 2004

This paper proposes a novel multicasting algorithm, called ANMAS (Ad hoc Network Multicasting with Ant System), for Mobile Ad hoc Network (MANET). The algorithm utilizes the indirect communication method of the ants via 'pheromone' to effectively obtain dynamical topology change information, generating safer multicasting paths, and adapts the well-known CBT (Core Based Tree) multicasting algorithm into the ANMAS framework with proper modificiations to make 'tolerable' multicasting group in the MANET environment. We show the efficiency and the effectiveness of ANMAS via simulation studies.

• ETRI Journal
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• 제30권1호
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• pp.129-140
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• 2008

In this paper, we propose a novel ant colony optimization (ACO)-based test scheduling method for testing network-on-chip (NoC)-based systems-on-chip (SoCs), on the assumption that the test platform, including specific methods and configurations such as test packet routing, generation, and absorption, is installed. The ACO metaheuristic model, inspired by the ant's foraging behavior, can autonomously find better results by exploring more solution space. The proposed method efficiently combines the rectangle packing method with ACO and improves the scheduling results by dynamically choosing the test-access-mechanism widths for cores and changing the testing orders. The power dissipation and variable test clock mode are also considered. Experimental results using ITC'02 benchmark circuits show that the proposed algorithm can efficiently reduce overall test time. Moreover, the computation time of the algorithm is less than a few seconds in most cases.

• Nuclear Engineering and Technology
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• 제55권5호
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• pp.1838-1854
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• 2023

This paper presents a hybrid algorithm to solve the multi-objective path planning (MOPP) problem for mobile robots in a static nuclear accident environment. The proposed algorithm mimics a real nuclear accident site by modeling the environment with a two-layer cost grid map based on geometric modeling and Monte Carlo calculations. The proposed algorithm consists of two steps. The first step optimizes a path by the hybridization of improved ant colony optimization algorithm-modified A^* (IACO-A^*) that minimizes path length, cumulative radiation dose and energy consumption. The second module is the high radiation dose rate avoidance strategy integrated with the IACO-A^* algorithm, which will work when the mobile robots sense the lethal radiation dose rate, avoiding radioactive sources with high dose levels. Simulations have been performed under environments of different complexity to evaluate the efficiency of the proposed algorithm, and the results show that IACO-A^* has better path quality than ACO and IACO. In addition, a study comparing the proposed IACO-A^* algorithm and recent path planning (PP) methods in three scenarios has been performed. The simulation results show that the proposed IACO-A^* IACO-A^* algorithm is obviously superior in terms of stability and minimization the total cost of MOPP.