• 정보처리학회논문지B
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• 제10B권3호
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• pp.237-242
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• 2003

Ant Colony System(ACS) 알고리즘은 조합 최적화 문제를 해결하기 위한 메타 휴리스틱 탐색 방법이다. 이것은 greedy search뿐만 아니라 exploitation of positive feedback을 사용한 모집단에 근거한 접근법으로 Traveling Salesman Problem(TSP)를 풀기 위해 제안되었다. 본 논문에서는 전통적 전역갱신과 지역갱신 방법에 개미들이 방문한 각 간선에 대한 방문 횟수를 강화값으로 추가한 새로운 방법의 ACS를 제안한다. 그리고 여러 조건 하에서 TCS 문제를 풀어보고 그 성능에 대해 기존의 ACS 방법과 제안된 ACS 방법을 비교 평가해, 최적해에 더 빨리 수렴함을 실험을 통해 알 수 있었다.

• 대한안전경영과학회지
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• 제13권1호
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• pp.175-182
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• 2011

고객의 요구에 대한 빠른 대응과 유연하고 효율적으로 새로운 제품을 적기에 개발하기 위해서는 제품 플랫폼에 기초한 대량 맞춤이 절실히 요구된다. 이러한 목적을 달성하기 위하여 기업들은 상대적으로 생산비용을 낮게 유지하면서 대량생산의 이점을 유지하고 동시에 고객의 요구사항을 만족시키기 위해, product family를 도입하고 가능하면 작은 변화를 통하여 제품의 다양성을 유지하고자 한다. Product family를 설계할 때 중요한 이슈 중에 하나는 제품의 공통성과 차별성간의 절충점을 찾아내는 것인데, 본 연구에서는 설계자들이 product family 재설계를 용이하게 하기 위한 방법론을 제안한다. 이를 위하여 본 연구에서는 ant colony 알고리즘과 product family의 공통성 평가지수를 이용하여 product family 재설계 방법론을 개발한다. 제안한 방법론은 복잡하고 반복적인 많은 계산과정을 가지고 있는 다른 방법과 달리 메타 휴리스틱 알고리즘을 적용하여 인간의 간섭을 줄이고, 실험결과의 정확도, 반복성 및 강건성을 향상시킨다. 본 연구에서는 컴퓨터 마우스 제품군을 대상으로 제안한 방법의 타당성을 검증하였고, 추가적으로 product family 레벨과 부품 레벨의 product family 재설계 추천방안도 제시하였다.

• 정보처리학회논문지B
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• 제18B권3호
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• pp.165-172
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• 2011

본 논문에서는 생물학의 개미들이 학습을 통해 목표를 획득하는 방법을 응용한 Ant-Q 알고리즘(Ant Q learning System)[1]을 Gale-Shapley[2]알고리즘을 통해 제시되었던 안정된 결혼문제(SMP: Stable Marriage Problem)[3]의 새로운 해법을 찾기 위해 적용 하였다. SMP는 남성($m_i$)들과 여성($w_j$)들은 각자 자신이 좋아하는 이상형에 대한 선호도(PL: preference list)를 바탕으로 안정이면서도 최선의 짝을 찾는 것을 목표로 하고 있다. Gale-Shapley 알고리즘은 남성(혹은 여성) 위주로 안정적(stability)인 짝(Matching)을 성사시키므로 다양한 조건을 수용하지 못한다. 본 논문에 적용된 Ant-Q는 개미(Ant)의 페로몬을 활용한 학습인 ACS(Ant colony system)에 강화학습의 일종인 Q-학습[9]을 추가한 방법으로, SMP의 새로운 해법을 찾을 수 있었다.

• 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.

• 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.

• 한국정밀공학회지
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• 제29권4호
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• pp.373-379
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• 2012

Disruptions in manufacturing systems caused by system changes and disturbances such as the tool wear, machine breakdown, malfunction of transporter, and so on, reduce the productivity and the increase of downtime and manufacturing cost. In order to cope with these challenges, a new method to build an intelligent manufacturing system with biological principles, namely an ant colony inspired manufacturing system, is presented. In the developed system, the manufacturing system is considered as a swarm of cognitive agents where work-pieces, machines and transporters are controlled by the corresponding cognitive agent. The system reacts to disturbances autonomously based on the algorithm of each autonomous entity or the cooperation with them. To develop the ant colony inspired manufacturing system, the disturbances happened in the machining shop of a transmission case were analyzed to classify them and to find out the corresponding management methods. The system architecture with the autonomous characteristics was generated with the cognitive agent and the ant colony technology. A test bed was implemented to prove the functionality of the developed system.

• Journal of Power Electronics
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• 제11권6호
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• pp.856-863
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• 2011

In this paper a novel method of the Ant Colony Optimization algorithm for rotor position estimation in Switched Reluctance Motors is presented. The data provided by the initial assumptions is one of the important aspects used to solve the problems relative to an Ant Colony algorithm. Considering the nature of a real ant colony, it was found that the ants have no primary data for deducing which is the shortest path in their initial iteration. They also do not have the ability to see the food sources at a distance. According to this point of view, a novel method is presented in which the rotor pole position relative to the corresponding stator pole in a switched reluctance motor is estimated with high accuracy using the active and inactive phase parameters. This new method gives acceptable results such as a desirable convergence together with an optimized and stable response. To the best knowledge of the authors, such an analysis has not been carried out previously.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권9호
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• pp.3496-3514
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• 2015

One of the challenging tasks in Mobile Ad hoc Network is to discover precise optimal routing solution due to the infrastructure-less dynamic behavior of wireless mobile nodes. Ant Colony Optimization, a swarm Intelligence technique, inspired by the foraging behaviour of ants in colonies was used in the past research works to compute the optimal path. In this paper, we propose a Recurrent Ant Colony Optimization (RECACO) that executes the actual Ant Colony Optimization iteratively based on recurrent value in order to obtain an optimal path convergence. Each iteration involves three steps: Pheromone tracking, Pheromone renewal and Node selection based on the residual energy in the mobile nodes. The novelty of our approach is the inclusion of new pheromone updating strategy in both online step-by-step pheromone renewal mode and online delayed pheromone renewal mode with the use of newly proposed metric named ELD (Energy Load Delay) based on energy, Load balancing and end-to-end delay metrics to measure the performance. RECACO is implemented using network simulator NS2.34. The implementation results show that the proposed algorithm outperforms the existing algorithms like AODV, ACO, LBE-ARAMA in terms of Energy, Delay, Packet Delivery Ratio and Network life time.

• International Journal of Computer Science & Network Security
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• 제23권2호
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• pp.55-64
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• 2023

The travelling salesman problem (TSP) is an important combinatorial optimization problem that is used in several engineering science branches and has drawn interest to several researchers and scientists. In this problem, a salesman from an arbitrary node, called the warehouse, starts moving and returns to the warehouse after visiting n clients, given that each client is visited only once. The objective in this problem is to find the route with the least cost to the salesman. In this study, a meta-based ant colony system algorithm (ACSA) is suggested to find solution to the TSP that does not use local pheromone update. This algorithm uses the global pheromone update and new heuristic information. Further, pheromone evaporation coefficients are used in search space of the problem as diversification. This modification allows the algorithm to escape local optimization points as much as possible. In addition, 3-opt local search is used as an intensification mechanism for more quality. The effectiveness of the suggested algorithm is assessed on a several standard problem instances. The results show the power of the suggested algorithm which could find quality solutions with a small gap, between obtained solution and optimal solution, of 1%. Additionally, the results in contrast with other algorithms show the appropriate quality of competitiveness of our proposed ACSA.

• Structural Engineering and Mechanics
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• 제45권3호
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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.