• Structural Engineering and Mechanics
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• v.46 no.3
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• pp.403-416
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• 2013

This paper describes new optimization strategy that offers significant improvements in performance over existing methods for geometry design of frame structures. In this study, an imperialist competitive algorithm (ICA) and ant colony optimization (ACO) are combined to reach to an efficient algorithm, called Imperialist Competitive Ant Colony Optimization (ICACO). The ICACO applies the ICA for global optimization and the ACO for local search. The results of optimal geometry for three benchmark examples of frame structures, demonstrate the effectiveness and robustness of the new method presented in this work. The results indicate that the new technique has a powerful search strategies due to the modifications made in search module of ICACO. Higher rate of convergence is the superiority of the presented algorithm in comparison with the conventional mathematical methods and non hybrid heuristic methods such as ICA and particle swarm optimization (PSO).

• 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.

• 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.

• Structural Engineering and Mechanics
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• v.45 no.3
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• pp.391-410
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• 2013

A numerical method is presented here to detect and assess structural damages from changes in natural frequencies using Ant Colony Optimization (ACO) algorithm. It is possible to formulate the inverse problem in terms of optimization and then to utilize a solution technique employing ACO to assess the damage/damages of structures using natural frequencies. The laboratory tested data has been used to verify the proposed algorithm. The study indicates the potentiality of the developed code to solve a wide range of inverse identification problems in a systematic manner. The developed code is used to assess damages of beam like structures using a first few natural frequencies. The outcomes of the simulated results show that the developed method can detect and estimate the amount of damages with satisfactory precision.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.7-13
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• 2008

This paper suggests a Path-planning algorithm for mobile agents. While there are a lot of studies on the path-planning for mobile agents, mathematical modeling of complex environment which constrained by spatio-temporally is very difficult and it is impossible to obtain the optimal solutions. In this paper, an optimal path-planning algorithm based on the graphic technique is presented. The working environment is divided into two areas, the one is free movable area and the other is not permissible area in which there exist obstacles and spatio-temporally constrained, and an optimal solution is obtained by using a new algorithm which is based on the well known ACO algorithm.

• Journal of Korean Society of Water and Wastewater
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• v.27 no.4
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• pp.413-428
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• 2013

In this study, Ant Colony Algorithm(ACO) was used for optimal model. ACO which are metaheuristic algorithm for combinatorial optimization problem are inspired by the fact that ants are able to find the shortest route between their nest and food source. For applying the model to water distribution systems, pipes, tanks(reservoirs), pump construction and pump operation cost were considered as object function and pressure at each node and reservoir level were considered as constraints. Modified model from Ostfeld and Tubaltzev(2008) was verified by applying 2-Looped, Hanoi and Ostfeld's networks. And sensitivity analysis about ant number, number of ants in a best group and pheromone decrease rate was accomplished. After the verification, it was applied to real water network from S water treatment plant. As a result of the analysis, in the Two-looped network, the best design cost was found to $419,000 and in the Hanoi network, the best design cost was calculated to $6,164,384, and in the Ostfeld's network, the best design cost was found to $3,525,096. These are almost equal or better result compared with previous researches. Last, the cost of optimal design for real network, was found for 66 billion dollar that is 8.8 % lower than before. In addition, optimal diameter for aged pipes was found in this study and the 5 of 8 aged pipes were changed the diameter. Through this result, pipe construction cost reduction was found to 11 percent lower than before. And to conclusion, The least cost design model on water distribution system was developed and verified successfully in this study and it will be very useful not only optimal pipe change plan but optimization plan for whole water distribution system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.13-21
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• 2017

Airline schedules are highly dependent on various factors of uncertainties such as unfavorable weather conditions, mechanical problems, natural disaster, airport congestion, and strikes. If the schedules are not properly managed to cope with such disturbances, the operational cost and performance are severely affected by the delays, cancelations, and so forth. This is described as a disruption. When the disruption occurs, the airline requires the feasible recovery plan returning to the normal operations in a timely manner so as to minimize the cost and impact of disruptions. In this research, an Ant Colony Optimization (ACO) algorithm with re-timing strategy is developed to solve the recovery problem for both aircraft and passenger. The problem consists of creating new aircraft routes and passenger itineraries to produce a feasible schedule during a recovery period. The suggested algorithm is based on an existing ACO algorithm that aims to reflect all the downstream effects by considering the passenger recovery cost as a part of the objective function value. This algorithm is complemented by re-timing strategy to effectively manage the disrupted passengers by allowing delays even on some of undisrupted flights. The delays no more than 15 minutes are accepted, which does not influence on the on-time performance of the airlines. The suggested method is tested on the real data sets from 2009 ROADEF Challenge, and the computational results are compared with the existing ones on the same data sets. The method generates the solution for most of problem set in 10 minutes, and the result generated by re-timing strategy is discussed for its impact.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.241-243
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• 2002

DNA 컴퓨팅은 생체 분자들이 갖는 막대한 병렬성을 정보 처리 기술에 적용한 기술이다. Adleman의 DNA 컴퓨팅은 랜덤한 고정길이의 형태로 문제를 표현하기 때문에 해를 찾지 못하거나 시간이 많이 걸리는 단점을 갖고 있다. 본 논문은 DNA 컴퓨팅에 DNA 코딩 방법을 적용하여 DNA 서열을 효율적으로 표현하고 반응횟수 만큼 합성과 분리 과정을 거쳐 최적의 코드를 생성하는 ACO(Algorithm for Code Optimization)를 제안한다. DNA 코딩 방법은 변형된 유전자 알고리즘으로 DNA 기능을 유지하며, 서열의 길이를 줄일 수 있으므로 최적의 서열을 생성할 수 있는 특징을 갖는다. ACO를 NP-complete 문제 중 Hamiltonian path problem에 적용하여 실험한 결과, Adleman의 DNA 컴퓨팅 보다 초기 문제 표현에서 높은 적합도 값을 갖는 서열을 생성했으며, 경로의 변화에도 능동적으로 대처하여 최적의 결과를 빠르게 탐색할 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10d
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• pp.621-624
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• 2006

무선 센서 네트워크는 기존의 무선 통신 기술로는 구현 불가능했던 다양한 응용 기술의 실현을 가능케 할 것으로 기대되고 있다. 이를 위해 제한된 자원의 효율적인 사용을 통한 무선 센서 네트워크의 성능 향상에 대한 연구가 지속되고 있으며 네트워크 계층에 있어서는 에너지 효율적인 라우팅 알고리즘에 대한 연구가 활발히 진행되고 있다. 본 논문에서는 데이터 중심(data-centric) 멀티 홉(multi-hop) 평면 라우팅 알고리즘에 최적화 알고리즘의 하나인 Ant Colony Optimization을 적용한 에너지 효율적인 라우팅 알고리즘을 제안한다. 시뮬레이션 결과, 제안한 알고리즘은 기존의 알고리즘에 비해 데이터 전송 지연 시간을 줄였을 뿐만 아니라, 경로 선택 및 유지에 필요한 제어 메시지 최소화를 통해 에너지 소모를 줄여 데이터 전송량의 증가를 가능케 했다.

• Annual Conference of KIPS
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• 2002.11a
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• pp.323-326
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• 2002

DNA 컴퓨팅은 생체 분자들이 갖는 막대한 병렬성을 이용하여 조합 최적화 문제에 적용하는 연구가 많이 시도되고 있다. 특히 TSP(Travelling Salesman Problem)는 간선에 대한 가중치 정보가 추가되어 있기 때문에 가중치를 DNA 염기 배열로 표현하기 위한 효율저인 방법들이 제시되지 않았다. 따라서 본 논문에서는 DNA 컴퓨팅에 DNA 코딩 방법을 적용하여 정점과 간선을 효율적으로 생성하고 표현된 DNA 염기 배열의 간선에 실제간을 적용하여 가중치 정보를 계산하는 ACO(Algorithm for Code Optimization)를 제안한다. DNA 코딩 방법은 변형된 유전자 알고리즘으로 DNA 기능을 유지하며, 서열의 길이를 줄일 수 있으므로 최적의 서열을 생성할 수 있는 특징을 갖는다. 실험에서 ACO를 TSP에 적용하여 Adleman의 DNA 컴퓨팅 알고리즘과 비교하였다. 그 결과 초기 문제 표현에서 우수한 적합도 값을 생성했으며, 경로의 변화에도 능동적으로 대처하여 최적의 결과를 빠르게 탐색할 수 있었다.