• Journal of KIISE:Software and Applications
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• v.30 no.9
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• pp.862-866
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• 2003

Traveling Salesman Problem(TSP) is a combinational optimization problem, Genetic Algorithm(GA) and Lin-Kernighan(LK) Heuristic［1］that is Local Search Heuristic are one of the most commonly used methods to resolve TSP. In this paper, we introduce ACS(Ant Colony System) Algorithm as another approach to solve TSP and propose a new pheromone updating method. ACS uses pheromone information between cities in the Process where many ants make a tour, and is a method to find a optimal solution through recursive tour creation process. At the stage of Global Updating of ACS method, it updates pheromone of edges belonging to global best tour of created all edge. But we perform once more pheromone update about created all edges before global updating rule of original ACS is applied. At this process, we use the frequency of occurrence of each edges to update pheromone. We could offer stochastic value by pheromone about each edges, giving all edges' occurrence frequency as weight about Pheromone. This finds an optimal solution faster than existing ACS algorithm and prevent a local optima using more edges in next time search.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.237-242
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• 2003

Ant Colony System (ACS) Algorithm is new meta heuristic for hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem (TSP). In this paper, we introduce ACS of new method that adds reinforcement value for each edge that visit to Local/Global updating rule. and the performance results under various conditions are conducted, and the comparision between the original ACS and the proposed method is shown. It turns out that our proposed method can compete with tile original ACS in terms of solution quality and computation speed to these problem.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.443-448
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• 2013

Ant Colony System(ACS) is a meta heuristic approach based on biology in order to solve combinatorial optimization problem. It is based on the tracing action of real ants which accumulate pheromone on the passed path and uses as communication medium. In order to search the optimal path, ACS requires to explore various edges. In existing ACS, the local updating rule assigns the same pheromone to visited edge. In this paper, our local updating rule gives the pheromone according to the total frequency of visits of the currently selected node in the previous iteration. I used the ACS algoritm using subpath for search. Our approach can have less local optima than existing ACS and find better solution by taking advantage of more informations during searching.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.221-226
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• 2004

Ant Colony System(ACS) Algorithm is new meta-heuristic for hard combinational optimization problem. It is a population-based approach that uses exploitation of positive feedback as well as greedy search. Recently, various methods and solutions are proposed to solve optimal solution of graph coloring problem that assign to color for adjacency node($v_i, v_j$) that they has not same color. In this paper introducing ANTCOL Algorithm that is method to solve solution by Ant Colony System algorithm that is not method that it is known well as solution of existent graph coloring problem. After introducing ACS algorithm and Assignment Type Problem, show the wav how to apply ACS to solve ATP And compare graph coloring result and execution time when use existent generating functions(ANT_Random, ANT_LF, ANT_SL, ANT_DSATUR, ANT_RLF method) with ANT_XRLF method that use XRLF that apply Randomize to RLF to solve ANTCOL. Also compare graph coloring result and execution time when use method to add re-search to ANT_XRLF(ANT_XRLF_R) with existent generating functions.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.1-8
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• 2022

In this paper, we design and implement a GPU-based parallel algorithm to effectively solve the traveling salesman problem through an ant color system. The repetition process of generating hundreds or thousands of tours simultaneously in TSP utilizes GPU's task-level parallelism, and the update process of pheromone trails data actively exploits data parallelism by 32x32 thread blocks. In particular, through simultaneous memory access of multiple threads, the coalesced accesses on continuous memory addresses and concurrent accesses on shared memory are supported. This experiment used 127 to 1002 city data provided by TSPLIB, and compared the performance of sequential and parallel algorithms by using Intel Core i9-9900K CPU and Nvidia Titan RTX system. Performance improvement by GPU parallelization shows speedup of about 10.13 to 11.37 times.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.9-12
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• 2000

컨테이너 터미널에서 효율적인 적하작업 계획을 자동으로 생성하는 알고리즘을 연구하였다. 실제 터미널에서 계획자들이 적하작업 계획시에 고려하는 제약소건 및 효율적인 계획을 위한 고려사항을 조사하였다. 이를 바탕으로 1단계에서는 개미시스템(ant system)이라는 인공지능기법을 적용하여 제약조건을 만족시키면서 원활한 적하작업이 진행될 수 있도록 컨테이너 크레인과 트랜스퍼 크레인의 이동순서와 위치를 결정하고, 2단계에서는 1단계에서의 결과를 바탕으로 빔탐색법(beam search)을 사용하여 컨테이너 개개의 작업순서를 결정하는 알고리즘을 개발하였다. 또한 개발된 시스템의 성능을 검증하기 위하여 최근의 대형선반에 대한 실제 현장자료를 바탕으로 실험을 수행하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.2
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• pp.704-710
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• 2017

Our research team is developing a 6-DOF manipulator that is adequate for the narrow workspace of press forming processes. This paper addresses the task sequence optimization methods for the manipulator to minimize the task-finishing time. First, a kinematic model of the manipulator is presented, and the anticipated times for moving among the task locations are computed. Then, a mathematical model of the task sequence optimization problem is presented, followed by a comparison of three meta-heuristic methods to solve the optimization problem: an ant colony system, simulated annealing, and a genetic algorithm. The simulation shows that the genetic algorithm is robust to the parameter settings and has the best performance in both minimizing the task-finishing time and the computing time compared to the other methods. Finally, the algorithms were implemented and validated through a simulation using Mathworks' Matlab and Coppelia Robotics' V-REP (virtual robot experimentation platform).

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.234-235
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• 2012

본 논문에서는 개미 집단 최적화(Ant Colony Optimization, ACO) 알고리즘을 적용한 감속률에 따른 동적 상황인식 경로 선정 방법을 제안한다. 최근 ITS(Intelligent Transportation Systems)의 개발과 함께 차량용 내비게이션의 실시간 교통 정보를 이용하는 수요가 급증하면서, 경로탐색의 중요성이 더욱 가속화되고 있다. 현재 차량용 내비게이션은 멀티미디어 및 정보통신 기술의 결합과 함께 다양한 기능 및 정보를 사용자에게 제공하고 있으며, 이러한 경로탐색 알고리즘은 교통시스템, 통신 네트워크, 운송 시스템 등 다양한 분야에 적용되고 있다. 본 논문에서는 감속률에 따른 동적 상황인식 경로 선정 방법을 제안함으로써, 최단 시간 및 최소 비용의 정보를 제공해 줄 뿐만 아니라 교통정체로 인한 사회적 비용 감소의 효과를 가져다 줄 것으로 기대한다.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.5
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• pp.191-198
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• 2013

It is required to transmit data through shorter path between sensor and base node for real time intrusion detection in wireless sensor networks (WSN) with a mobile base node. Because minimum Wiener index spanning tree (MWST) based routing approach guarantees lower average hop count than that of minimum spanning tree (MST) based routing method in WSN, it is known that MWST based routing is appropriate for real time intrusion detection. However, the minimum Wiener index spanning tree problem which aims to find a spanning tree which has the minimum Wiener index from a given weighted graph was proved to be a NP-hard. And owing to its high dependency on certain nodes, minimum Wiener index tree based routing method has a shorter network lifetime than that of minimum spanning tree based routing method. In this paper, we propose a multi-objective ant colony optimization algorithm to tackle these problems, so that it can be used to detect intrusion in real time in wireless sensor networks with a mobile base node. And we compare the results of our proposed method with MST based routing and MWST based routing in respect to average hop count, network energy consumption and network lifetime by simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.401-406
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• 2011

In order to solve a complex optimization problem more efficiently than traditional approaches, various meta-heuristic algorithms such as genetic algorithm, ant-colony algorithm, and harmony search algorithm have been extensively researched. Compared with other meta-heuristic algorithm, harmony search algorithm shows a better result to resolve the complex optimization issues. Harmony search algorithm is inspired by the improvision process of musician for most suitable harmony. In general, the performance of harmony search algorithm is determined by the value of harmony memory considering rate, and pitch adjust rate. In this paper, modified harmony search algorithm is proposed in order to derive best harmony. If the optimal solution of a specific problem can not be found for a certain period of time, a part of original harmony memory is updated as the selected suitable harmonies. Experimental results using test function demonstrate that the updated harmony memory can induce the approximation of reliable optimal solution in the short iteration, because of a few change of fitness.