• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.64-66
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• 2003

DNA(Deoxyribo Nucleic Acid)컴퓨팅은 생체분자를 계산의 도구로 이용하는 새로운 계산 방법으로 DNA 정보 저장능력과 DNA의 상보적인 관계를 이용하여 연산을 수행하는 방법이다. 최근에는 DNA 분자들이 갖는 강력한 병렬성을 이용하여 NP-Complete 문제에 적용하는 연구가 많이 시도되고 있다. Adleman이 DNA 컴퓨팅을 이용해 해결한 HPP(Hamilton Path Problem)와는 달리 TSP(Traveling Salesman Problem)는 간선에 가중치가 추가되었기 때문에 DNA 염기배열로 표현하기가 어렵고 또한 염기배열의 길이를 줄이기 위해 고정길이 염기배열을 사용할 경우 가중치가 커지면 효율적이지 못하다. 본 논문에서는 스무딩 알고리즘(smoothing algorithm)을 사용하여 간선의 가중치를 일정한 비율로 줄인 다음 유전자 알고리즘을 사용하여 최적의 염기배열을 찾는 방법을 제안하였다.

• Journal of Drive and Control
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• v.19 no.4
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• pp.19-28
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• 2022

This paper introduces an automatic steering system for straight traveling capable of being mounted on drivable agricultural machinery which user can handle it such as a tractor, a transplant, etc. The modular automatic steering device proposed in the paper is composed of RTK GNSS, IMU, HMI, hydraulic valve, and wheel sensor. The path generation method of the automatic steering system is obtained from two location information(latitude and longitude on each point) measured by GNSS in advance. From HMI, a straight path(AB line) can be created by connecting latitude and longitude on each point and the device makes the machine able to follow the path. During traveling along the reference path, it acquires the real time position data every sample time(0.1s), compares the reference with them and calculates the lateral deviation. The values of deviation are used to control the steering angle of the machine using hydraulic valve mounted on the axle of front wheel. In this paper, Pure Pursuit algorithm is applied used in autonomous vehicles frequently. For the analysis of traveling characteristics, field tests were executed about these conditions: velocity of 2, 3, 4km/h which is applied to general agricultural work and ground surface of solid(asphalt) and weak condition(soil) such as farmland. In the case of weak ground state, two experiments were executed about no-load(without work) and load(with work such as plowing). The maximum average deviations were presented 2.44cm, 7.32cm, and 11.34cm during traveling on three ground conditions : asphalt, soil without load and with load(plowing).

• Journal of Computing Science and Engineering
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• v.8 no.4
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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.

• Tunnel and Underground Space
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• v.24 no.1
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• pp.11-20
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• 2014

This study analyzed the optimal routes of auxiliary vehicles in an open-pit mine that need to traverse the entire mine through many working points. Unlike previous studies which usually used the Dijkstra's algorithm, this study utilized a heuristic algorithm for the Traveling Salesman Problem(TSP). Thus, the optimal routes of auxiliary vehicles could be determined by considering the visiting order of multiple working points. A case study at the Pasir open-pit coal mine, Indonesia was conducted to analyze the travel route of an auxiliary vehicle that monitors the working condition by traversing the entire mine without stopping. As a result, we could know that the heuristic TSP algorithm is more efficient than intuitive judgment in determining the optimal travel route; 20 minutes can be shortened when the auxiliary vehicle traverses the entire mine through 25 working points according to the route determined by the heuristic TSP algorithm. It is expected that the results of this study can be utilized as a basis to set the direction of future research for the system optimization of auxiliary vehicles in open-pit mines.

• Journal of Korea Game Society
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• v.9 no.4
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• pp.57-66
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• 2009

A quest system is one of the important parts in the MMORPG (Massive Multiplayer Online Role Playing Game) contents. Because of its complexity in combining various content components, quest reward design belongs to a complicated work in estimating quest reward levels correctly in the initial development stage. In this paper, we suggest a new quest reward design technique by considering optimal quest play paths. We model a quest reward problem as the TSP (Traveling Salesman Problem) and solve that by adopting genetic algorithms. With our system, game designers easily estimate the optimal quest play path and it can be useful in reducing the trial-errors in the initial quest design process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.622-627
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• 2006

TSP(Traveling Salesman Problem) is a problem finding out the shortest distance out of many courses where given cities of the number of N, one starts a certain city and turns back to a starting city, visiting every city only once. As the number of cities having visited increases, the calculation rate increases geometrically. This problem makes TSP classified in NP-Hard Problem and genetic algorithm is used representatively. To obtain a better result in TSP, various operators have been developed and studied. This paper suggests new method of population initialization and of sequential transformation, and then proves the improvement of capability by comparing them with existing methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.447-448
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• 2013

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.283-286
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• 1997

일반적으로 직진 경로를 주행하는 이동로봇의 오차 보정을 위해서는 PI 제어기의 계수 보정이 필요하다. 본 논문에서는 직진 경로를 주행하는 이동로봇의 양쪽 바퀴에서 얻어진 엔코더의 값으로 측정하고, 측정되어진 엔코더의 값을 이용하여 방향과 움직임의 오차를 보정해주는 알고리즘을 PI제어기와 유전알고리즘을 사용하여 최적의 파라미터를 구할 수 있는 방법을 제안하였다.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.2
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• pp.156-161
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• 1997

In this thesis, based on Genetic Algorithm, a new route search algorithm is presented to search an optimal route between the origin and the destination in intelligent route guidance systems in order to minimize the route traveling time. The proposed algorithm is effectively employed to complex road networks which have diverse turn constrains, time-delay constraints due to cross signals, and stochastic traffic volume. The algorithm is also shown to significantly promote search efficiency by changing the population size of path individuals that exist in each generation through the concept of age and lifetime to each path individual. A virtual road-traffic network with various turn constraints and traffic volume is simulated, where the suggested algorithm promptly produces not only an optimal route to minimize the route cost but also the estimated travel time for any pair of the origin and the destination, while effectively avoiding turn constraints and traffic jam.

• The KIPS Transactions:PartA
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• v.10A no.4
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• pp.369-374
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• 2003

Ant Algorithm is used to find the solution of Combinatorial Optimization Problems. Real ants are capable of finding the shortest path from a food source to their nest without using visual informations. This behavior of real ants has inspired ant algorithm. There are various versions of Ant Algorithm. Ant Colony System (ACS) is introduced lately. ACS is applied to the Traveling Salesman Problem (TSP) for verifying the availability of ACS and evaluating the performance of ACS. ACS find a good solution for TSP When ACS is applied to different Combinatorial Optimization Problems, ACS uses the same parameters and strategies that were used for TSP. In this paper, ACS is applied to the Multicast Routing Problem. This Problem is to find the paths from a source to all destination nodes. This definition differs from that of TSP and differs from finding paths which are the shortest paths from source node to each destination nodes. We introduce parameters and strategies of ACS for Multicasting Routing Problem.