• 한국지능시스템학회논문지
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• 제15권6호
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• pp.730-735
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• 2005

배낭 문제는 단순한 것 같지만 조합 최적화 문제로서, 다항 시간(polynomial time)에 풀리지 않는 NP-hard 문제이다. 이 문제를 해결하기 위해 기존에는 GA(Genetic Algorithms)를 이용하여 해결하였다. 하지만 기존의 방법은 DNA의 정확한 특성을 고려하지 않아, 실제 실험과의 결과 차이가 발생하고 있다. 본 논문에서는 배낭 문제의 문제점을 해결하기 위해 DNA 컴퓨팅 기법에 DNA 코딩 방법을 적용한 ACO(Algorithm for Code Optimization)를 제안한다. ACO는 배낭 문제 중 (0,1)-배낭 문제에 적용하였고, 그 결과 기존의 방법보다 실험적 오류를 최소화하였으며, 또한 적합한 해를 빠른 시간내에 찾을 수 있었다.

• The Journal of Asian Finance, Economics and Business
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• 제7권2호
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• pp.309-316
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• 2020

This study aims to measure the relationship between corporate social responsibility (CSR) and affective commitment (ACO), normative commitment (NCO), and organizational performance in food processing enterprises (FPEs) in the Mekong River Delta, Vietnam. To test the initial model proposed in this paper, a total of 422 owners, directors and managers of FPEs were interviewed from some provinces in the Mekong River Delta. The method of exploratory factor analysis (EFA) is initially employed, then confirmatory factor analysis (CFA) and structure equation modelling (SEM) are used. The results of SEM showed that higher affective commitment was correlated with normative commitment. The results showed that four aspects of CSR toward employees, customers, environment and legal are significant factors. As a result, ACO and NCO act as mediators between CSR and organizational performance. This finding provides strong evidence of the important role of CSR to support positive impacts on ACO, NCO, and orgazational performance (OP). In addition, the success of the organizational performance is also found by contributions of CSR and NCO to its changes. Although ACO does not directly affect performance, it has a positive effect on the NCO. Therefore, it is necessary to enhance the implementation of CSR to promote implementation of organizational commitments.

• 한국기계가공학회지
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• 제18권9호
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• pp.36-43
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• 2019

Turning, a main machining process, is a widespread process in metal cutting industries. Many researchers have investigated the effects of process parameters on the machining process. In the turning process, input variables including cutting speed, feed, and depth of cut are generally used. Surface roughness and electric current consumption are used as output variables in this study. We construct a simulation model for the turning process using a neural network, which predicts the output values based on input values. In the neural network, obtaining the appropriate set of weights, which is called training, is crucial. In general, back propagation (BP) is widely used for training. In this study, techniques such as ant colony optimization (ACO) and particle swarm optimization (PSO) as well as BP were used to obtain the weights in the neural network. Particularly, two combined techniques of ACO_BP and PSO_BP were utilized for training the neural network. Finally, the performances of the two techniques are compared with each other.

• 대한전자공학회논문지SD
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• 제45권1호
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• pp.43-49
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• 2008

본 논문에서는 Ant Colony Optimization(ACO)을 이용하여 Single-Error Correcting & Double-Error Detecting(SEC-DED)을 제공하는 메모리 ECC 체커 회로의 소비전력을 절감하는 방안을 제시한다. H-매트릭스를 통해 구현되는 SEC-DED 코드인 Hsiao 코드의 대칭성과 H-매트릭스 구성상의 높은 자유도를 이용하여 회로의 면적, 딜레이에 영향을 주지 않고 최소의 비트 트랜지션이 일어나도록 H-매트릭스를 최적화한다. 실험을 통하여 H-매트릭스의 최적화를 위한 ACO 매핑과 파라메터의 설정을 알아보고 이의 구현 결과를 랜덤 매트릭스 구성을 통한 방식 및 기존의 GA알고리즘을 이용한 최적화 방식과 비교하여 소비 전력이 기존의 방식에 비해 절감될 수 있음을 보여준다.

• 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.

• Journal of Computing Science and Engineering
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• 제8권4호
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• pp.199-206
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• 2014

The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2004년도 춘계공동학술대회 논문집
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• pp.319-322
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• 2004

물류/택배업계의 공급사슬관리에서 차량에 의한 고객의 요구 서비스 시간대 만족은 고객의 재고수준을 낮추고 또한 서비스 수준의 향상에 매우 중요한 제약조건이다. 최근에 소개된 메타휴리스틱인 개미해법(Ant Colony Optimization: ACO)은 NP-hard 문제의 해공간 탐색에 있어서 상당한 장점을 가지고 있으나, 시간대 제약이 있는 차량경로문제(Vehicle Routing Problems with Time Windows: VRPTW)에 대한 적용은 아주 미비한 실정이다. 따라서, 본 연구에서는 ACO 를 VRPTW에 적용하여 최선의 차량경로 해를 구하기 위한 여러 변형을 제시하고, 이들의 영향을 다양한 실험문제를 이용하여 분석하고자 한다. 계산실험 결과, 기본 ACO 에 여러 설계 요소들을 추가함에 따라 계산시간이 다소 증가하지만 보다 우수한 차량경로 해를 구할 수 있었다.

• 한국소음진동공학회논문집
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• 제19권9호
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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.

• 경영과학
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• 제26권3호
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• pp.55-65
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• 2009

The objective of this paper is to propose an ant colony optimization (ACO) for clustering design in wireless sensor network problem. This proposed ACO approach is designed to deal with the dynamics of the sensor nodes which can be adaptable to topological changes to any network graph in a time. Long communication distances between sensors and a sink in a sensor network can greatly consume the energy of sensors and reduce the lifetime of a network. We can greatly minimize the total communication distance while minimizing the number of cluster heads using proposed ACO. Simulation results show that our proposed method is very efficient to find the best solutions comparing to the optimal solution using CPLEX in 100, 200, and 400 node sensor networks.

• Journal of Information Processing Systems
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• 제10권2호
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• pp.271-282
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• 2014

The advancement of knowledge society has enabled the social network community (SNC) to be perceived as another space for learning where individuals produce, share, and apply content in self-directed ways. The content generated within social networks provides information of value for the participants in real time. Thus, this study proposes the social network community activity-based content model (SoACo Model), which takes SNC-based activities and embodies them within learning objects. The SoACo Model consists of content objects, aggregation levels, and information models. Content objects are composed of relationship-building elements, including real-time, changeable activities such as making friends, and participation-activity elements such as "Liking" specific content. Aggregation levels apply one of three granularity levels considering the reusability of elements: activity assets, real-time, changeable learning objects, and content. The SoACo Model is meaningful because it transforms SNC-based activities into learning objects for learning and teaching activities and applies to learning management systems since they organize activities -- such as tweets from Twitter -- depending on the teacher's intention.