• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2343-2348
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• 2008

Ant 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. In this paper, we propose the improved $AS_{rank}$ algorithms. The original $AS_{rank}$ algorithm accomplishes a pheromone updating about only the paths which will be composed of the optimal path is higher, but, the paths which will be composed the optimal path is lower does not considered. In this paper, The proposed method evaporate the pheromone of the paths which will be composed of the optimal path is lowest(worst tour path), it is reducing the probability of the edges selection during next search cycle. Simulation results of proposed method show lower average search time and average iteration than original ACS.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.10
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• pp.849-858
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• 2021

It is difficult to establish a quantitative standard because there are many factors to consider, such as environmental conditions, airworthiness, and maintainability, in determining the installation location of equipment in an aircraft. In addition, as the number of equipment increases, the design proposal increases exponentially, so the design is proceeding depending on the experience of the designer much in order to review it within a limited time schedule. In this paper, a method of calculating the length and weight of the wiring harness according to the location of the equipment and a method of optimizing the weight of the wiring harness and the CG of the equipment using genetic algorithms are described in order to create a quantitative standard useful by comparing the optimal design and the actual design.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.10a
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• pp.57-78
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• 2005

The vehicle routing problem (VRP) is to determine a set of feasible vehicle routes, one for each vehicle, such that each customer is visited exactly once and the total distance travelled by the vehicles is minimized. A feasible route is defined as a simple circuit including the depot such that the total demand of the customers in the route does not exceed the vehicle capacity. While there have been significant advances recently in exact solution methodology, the VRP is not a well solved problem. We find most approaches still relying on the branch and bound method. These approaches employ various methodologies to compute a lower bound on the optimal value. We introduce a new modelling approach, termed route-splitting, for the VRP that allows us to address problems whose size is beyond the current computational range of set-partitioning models. The route-splitting model splits each vehicle route into segments, and results in more tractable subproblems. Lifting much of the burden of solving combinatorially hard subproblems, the route-splitting approach puts more weight on the LP master problem, Recent breakthroughs in solving LP problems (Nemhauser, 1994) bode well for our approach. Lower bounds are computed on five symmetric VRPs with up to 199 customers, and eight asymmetric VRPs with up to 70 customers. while it is said that the exact methods developed for asymmetric instances have in general a poor performance when applied to symmetric ones (Toth and Vigo, 2002), the route splitting approach shows a competent performance of 93.5% on average in the symmetric VRPs. For the asymmetric ones, the approach comes up with lower bounds of 97.6% on average. The route-splitting model can deal with asymmetric cost matrices and non-identical vehicles. Given the ability of the route-splitting model to address a wider range of applications and its good performance on asymmetric instances, we find the model promising and valuable for further research.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.100-107
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• 2011

One of the important fields for heuristic algorithm is how to balance between Intensification and Diversification. In this paper, we deal with the performance improvement techniques through balance the intensification and diversification in Ant Colony System(ACS) which is one of Ant Colony Optimization(ACO). In this paper, we propose the hybrid searching method between intensification strategy and diversification strategy. First, the length of the global optimal path does not improved within the limited iterations, we evaluates this state that fall into the local optimum and selects the next node using changed parameters in the state transition rule. And then we consider the overlapping edge of the global best path of the previous and the current, and, to enhance the pheromone for the overlapping edges increases the probability that the optimal path is configured. Finally, the performance of Best and Average-Best of proposed algorithm outperforms ACS-3-opt, ACS-Subpath, ACS-Iter and ACS-Global-Ovelap algorithms.