• 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).

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.462-466
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• 2008

In this paper, we propose a Data Dependant Swarm Intelligence Routing Algorithm(DSRA) based on "ant colony optimization" to improve routing performance in Mobile Ad hoc Network(MANET). DSRA generates a different routing path depending on data's characteristics: Realtime and Non-Realtime. DSRA achieves a reduced delay for Realtime data and an enhanced network lifetime from a decentralized path selection for Non-Realtime data. We demonstrate these results by an experimental study comparing with AODV, DSR and AntHocNet.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.6
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• pp.504-510
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• 2016

Robots are widely utilized in many fields, and various demands need customized robots. This study proposes an optimal design method based on swarm intelligence for selecting the kinematic parameter of a manipulator according to the task space trajectory desired by the user. The optimal design method is dealt with herein as an optimization problem. This study is based on swarm intelligence-based optimization algorithms (i.e., ant colony optimization (ACO) and particle swarm optimization algorithms) to determine the optimal kinematic parameters of the manipulator. The former is used to select the optimal kinematic parameter values, whereas the latter is utilized to solve the inverse kinematic problem when the ACO determines the parameter values. This study solves a design problem with the PUMA 560 when the desired task space trajectory is given and discusses its results in the simulation part to verify the performance of the proposed design.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.131-137
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• 2007

Recently, Ant Colony Optimization (ACO) is emerged as a simple yet powerful optimization algorithm for routing and load-balancing of both wired and wireless networks. However, there are few researches trying to adopt ACO to enhance routing performance in WSN owing to difficulties in applying ACO to WSN because of stagnation effect. In this paper, we propose an energy-efficient path selection algorithm based on ACO for WSN. The algorithm is not by simply applying ACO to routing algorithm but by introducing a mechanism to alleviate the influence of stagnation. By the simulation result, the proposed algorithm shows better performance in data propagation delay and energy efficiency over Directed Diffusion which is one of the outstanding schemes in multi-hop flat routing protocols for WSN. Moreover, we checked that the proposed algorithm is able to mitigate stagnation effect than simple ACO adoption to WSN.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.15-22
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• 2023

An Ant Colony Optimization Algorithm(ACO) is one of the frequently used algorithms to solve the Traveling Salesman Problem(TSP). Since the ACO searches for the optimal value by updating the pheromone, it is difficult to consider the distance between the nodes and other variables other than the amount of the pheromone. In this study, fuzzy logic is added to ACO, which can help in making decision with multiple variables. The improved algorithm improves computation complexity and increases computation time when other variables besides distance and pheromone are added. Therefore, using the algorithm improved by the fuzzy logic, it is possible to solve TSP with many variables accurately and quickly. Existing ACO have been applied only to pheromone as a criterion for decision making, and other variables are excluded. However, when applying the fuzzy logic, it is possible to apply the algorithm to various situations because it is easy to judge which way is safe and fast by not only searching for the road but also adding other variables such as accident risk and road congestion. Adding a variable to an existing algorithm, it takes a long time to calculate each corresponding variable. However, when the improved algorithm is used, the result of calculating the fuzzy logic reduces the computation time to obtain the optimum value.

• Journal of Korean Society for Quality Management
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• v.36 no.2
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• pp.28-35
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• 2008

This study deals with an Excel-based scheduling system for a small and medium sized manufacturing factory without sufficient capability for managing full-scale information systems. The factory has the bottleneck with identical machines and unique batching characteristics. The scheduling problem is formulated as a variation of the parallel-machine scheduling system. It can be solved by a two-phase method: the first phase with an ant colony optimization (ACO) heuristic for order grouping and the second phase with a mixed integer programming (MIP) algorithm for scheduling groups on machines.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.166-169
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• 2003

일정한 시간이 지나면 제품으로서의 가치가 사라지게 되는 단일기간 재고품목들은 생산된 직후 전량 각 고객들에게 주어진 납기에 맞추어 효율적인 분배가 요구된다. 본 연구에서는 고객들은 다수 종류의 제품을 주문할 수 있으며 제품종류별 분리배송을 허용하는 상황에서 생산비, 수송비, 납기위반비, 차량고정비를 최소화하기 위한 생산순서 및 차량경로를 수립함을 목적으로 한다. 이에 대한 해법으로써 진화개미해법을 개발하였다. 개발된 해법의 성능평가를 위해 각 고객의 위치, 주문 제품 종류, 주문량들을 다르게 하여 구축한 실험문제에 대하여 유전알고리듬해법과 비교실험을 수행하였다.

• IE interfaces
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• v.17 no.spc
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• pp.103-110
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• 2004

This paper presents a two-phase method for the vehicle routing problems with time windows(VRPTW). In a supply chain management(SCM) environment, timely distribution is very important problem faced by most industries. The VRPTW is associated with SCM for each customer to be constrained the time of service. In the VRPTW, the objective is to design the least total travel time routes for a fleet of identical capacitated vehicles to service geographically scattered customers with pre-specified service time windows. The proposed approach is based on ant colony optimization(ACO) and improvement heuristic. In the first phase, an insertion based ACO is introduced for the route construction and its solutions is improved by an iterative random local search in the second phase. Experimental results show that the proposed two-phase method obtains very good solutions with respect to total travel time minimization.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.207-213
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• 2021

Networks in Mobile ad hoc contain distribution and do not have a predefined structure which practically means that network modes can play the role of being clients or servers. The routing protocols used in mobile Ad-hoc networks (MANETs) are characterized by limited bandwidth, mobility, limited power supply, and routing protocols. Hybrid routing protocols solve the delay problem of reactive routing protocols and the routing overhead of proactive routing protocols. The Ant Colony Optimization (ACO) algorithm is used to solve other real-life problems such as the travelling salesman problem, capacity planning, and the vehicle routing challenge. Bio-inspired methods have probed lethal in helping to solve the problem domains in these networks. Hybrid routing protocols combine the distance vector routing protocol (DVRP) and the link-state routing protocol (LSRP) to solve the routing problem.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.314-317
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• 2010

본 연구는 차량 경로 스케줄링 문제(VRSPTW, the Vehicle Routing and Scheduling Problem with Time Window)를 해결하기 위하여, 멀티 비용 함수(Multi Cost Function)를 갖는 개미 군집 최적화(Ant Colony Optimization)을 이용한 휴리스틱을 제안하였다. 멀티 비용 함수는 각 개미가 다음 고객 노드로 이동하기 위해 비용을 평가할 때 거리, 요구량, 각도, 시간제약에 대해 서로 다른 가중치를 반영하여 우수한 초기 경로를 구할 수 있도록 한다. 본 연구의 실험결과에서 제안된 휴리스틱이 Solomon I1 휴리스틱과 기회시간이 반영된 하이브리드 휴리스틱보다 효율적으로 최근사 해를 얻을 수 있음을 보였다.