• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.447-456
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• 2007

The ant colony optimization (ACO) is a probabilistic Meta-heuristic algorithm which has been developed in recent years. Originally ACO was used for solving the well-known Traveling Salesperson Problem. More recently, ACO has been used to solve many difficult problems. In this paper, we develop an ant colony optimization method to solve the maximum independent set problem, which is known to be NP-hard. In this paper, we suggest a new method for local information of ACO. Parameters of the ACO algorithm are tuned by evolutionary operations which have been used in forecasting and time series analysis. To show the performance of the ACO algorithm, the set of instances from discrete mathematics and computer science (DIMACS)benchmark graphs are tested, and computational results are compared with a previously developed ACO algorithm and other heuristic algorithms.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.769-776
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• 2003

DNA computing has used to solve MCP (Maximal Clique Problem). However, when current DNA computing is applied to MCP. it can't efficiently express vertices and edges and it has a problem that can't look for solutions, by misusing wrong restriction enzyme. In this paper we proposed ACO (Algorithm for Code Optimization) that applies DNA coding method to DNA computing to solve MCP's problem. We applied ACO to MCP and as a result ACO could express DNA codes of variable lengths and generate codes without unnecessary vertices than Adleman's DNA computing algorithm could. In addition, compared to Adleman's DNA computing algorithm, ACO could get about four times as many as Adleman's final solutions by reducing search time and biological error rate by 15%.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.105-111
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• 2004

DNA computing has been using to solve TSP (Traveling Salesman Problems). However, when the typical DNA computing is applied to TSP, it can`t efficiently express vertices and weights of between vertices. In this paper, we proposed ACO (Algorithm for Code Optimization) that applies DNA coding method to DNA computing to efficiently express vertices and weights of between vertices for TSP. We applied ACO to TSP and as a result ACO could express DNA codes which have variable lengths and weights of between vertices more efficiently than Adleman`s DNA computing algorithm could. In addition, compared to Adleman`s DNA computing algorithm, ACO could reduce search time and biological error rate by 50% and could search for a shortest path in a short time.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.2
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• pp.33-40
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• 2018

There are lots of literature about connected car system from industry and academia. The connected car is a smart car integrated with IT technology that is connected to people, vehicles and traffic management systems. It is important to V2I (vehicle to infrastructure) communication which is the connection between the vehicle and the infrastructure. CIM (cooperative intersection management) is a device to manage the communication between vehicle and infrastructure. In this paper, we analyze two intelligent vehicle control methods using CIM at non-signalized intersections. In the first method, a vehicle to pass through intersection needs to reserve a resource of intersection. In the second method, trajectory patterns on pre-planned vehicles are classified to pass through intersection. We analyze case studies of two methods to be implemented by DP(dynamic programming) and ACO(ant colony optimization) algorithms. The methods can be reasonably improved by placing importance on vehicles or controlling speeds of vehicles.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.243-246
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• 2004

배낭 문제는 단순한 것 같지만 조합형 특성을 가진 NP-hard 문제이다 이 문제를 해결하기 위해 기존에는 GA(Genetic algorithms)를 이용하였으나 지역해에 빠질 수 있어 잘못된 해를 찾거나 찾지 못하는 문제점을 갖고 있다. 본 논문에서는 이러한 문제점들을 해결하기 위해 막대한 병렬성과 저장능력을 가진 DNA 컴퓨팅 기법에 DNA에 기반한 변형된 GA인 DNA 코딩 방법을 적용한 ACO(Algorithm for Code Optmization)를 제안한다. ACO는 배낭 문제 중 (0,1)-배낭 문제에 적용하였고, 그 결과 기존의 GA를 이용한 것 보다 초기 문제 표현에서 우수한 적합도를 생성했으며, 빠른 시간내에 우수한 해를 찾을 수 있었다.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.9
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• pp.884-891
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• 2009

This study investigates some of swarm intelligence algorithms to tackle a traditional damage detection problem having stiffness degradation or damage in mechanical structures. Particle swarm(PSO) and ant colony optimization(ACO) methods have been exploited for localizing and estimating the location and extent damages in a structure. Both PSO and ACO are population-based, stochastic algorithms that have been developed from the underlying concept of swarm intelligence and search heuristic. A finite element (FE) model updating is implemented to minimize the difference in a set of natural frequencies between measured and baseline vibration data. Stiffness loss of certain elements is considered to simulate structural damages in the FE model. It is numerically shown that PSO and ACO algorithms successfully completed the optimization process of model updating in locating unknown damages in a truss structure.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.512-515
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• 2010

온톨로지는 시맨틱 웹의 핵심 기술로써 시맨틱 웹이 소개된 이후 다양한 온톨로지 생성 방법의 연구가 이루어져 왔다. 그러나 온톨로지는 작성이 어렵고 난해한 면이 있어 소수의 전문가 집단에 의해서만 만들어지고 있는 것이 현실이다. 본 논문에서는 웹 2.0 기반 환경에서 사용자들이 생성한 온톨로지를 수집하여 대중 온톨로지를 완성하는 새로운 온톨로지 생성 방법을 제안한다. 본 논문에서는 집단지성을 이용한 최적화 기법 중 한가지인 ACO 를 온톨로지 생성 분야에 적용시켜 전문가가 아닌 일반 사용자들이 작성한 낮은 수준의 온톨로지를 모아 완성된 형태의 온톨로지를 생성한다. 그리고 본 논문에서 제안한 방법을 통해 만들어진 온톨로지의 신뢰성을 검증하기 위하여 전문가 집단이 만든 온톨로지에 대해 정확도와 재현율을 계산하여 보인다. 본 논문에서 제시하는 방법은 복잡하고 난해한 기존 온톨로지의 제작 방법의 단점을 효과적으로 해결하며 대중적으로 시맨틱 웹이 활용될 수 있는 환경을 구축할 수 있다.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.730-735
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• 2005

Though Knapsack Problem appears to be simple, it is a NP-hard problem that is not solved in polynomial time as combinational optimization problems. To solve this problem, GA(Genetic Algorithms) was used in the past. However, there were difficulties in real experiments because the conventional method didn't reflect the precise characteristics of DNA. In this paper we proposed ACO (Algorithm for Code Optimization) that applies DNA coding method to DNA computing to solve problems of Knapsack Problem. ACO was applied to (0,1) Knapsack Problem; as a result, it reduced experimental errors as compared with conventional methods, and found accurate solutions more rapidly.

• The Journal of the Korea Contents Association
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• v.10 no.5
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• pp.1-9
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• 2010

This paper propose a novel scheme called selective evaluation to improve convergence of ACO (ant colony optimization) for feature selection. The scheme cutdown the computational load by excluding the evaluation of unnecessary or less promising candidate solutions. The scheme is realizable in ACO due to the valuable information, pheromone trail which helps identify those solutions. With the aim of checking applicability of algorithms according to problem size, we analyze the timing requirements of three popular feature selection algorithms, greedy algorithm, genetic algorithm, and ant colony optimization. For a rigorous timing analysis, we adopt the concept of atomic operation. Experimental results showed that the ACO with selective evaluation was promising both in timing requirement and recognition performance.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.6
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• pp.87-94
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• 2003

One of the important fields for heuristic algorithm is how to balance between Intensification and Diversification. Ant Colony Optimization(ACO) is a new meta heuristic algorithm to solve hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as Breedy search It was first Proposed for tackling the well known Traveling Salesman Problem(TSP). In this paper, we deal with the performance improvement techniques through balance the Intensification and Diversification in Ant Colony System(ACS). First State Transition considering the number of times that agents visit about each edge makes agents search more variously and widen search area. After setting up criteria which divide elite tour that receive Positive Intensification about each tour, we propose a method to do addition Intensification by the criteria. Implemetation of the algorithm to solve TSP and the performance results under various conditions are conducted, and the comparision between the original An and the proposed method is shown. It turns out that our proposed method can compete with the original ACS in terms of solution quality and computation speed to these problem.