• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.783-786
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• 2008

외판원문제(Traveling Salesman problem: TSP)는 전형적인 조합최적화 문제로 위치하는 n개의 모든 지점을 오직 한번씩만 방문하는 순회경로를 결정하는 과정에서 순회비용 또는 순회거리를 최소화한다. 따라서 본 논문에서는 종래의 NP-hard문제로 널리 알려진 TSP를 해결하기 위해서 메타 휴리스틱기법 중에서 가장 널리 이용되고 있는 유전 알고리즘(Genetic Algorithm: GA)을 이용한다. 마지막으로, 유전 알고리즘을 이용해 외판원문제에 적합한 성능을 보이는 유전 연산자를 찾아내기 위해 수치 실험을 통해 그 성능에 대한 평가를 한다.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.751-756
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• 2003

Ant Colony Optimization (ACO) is new meta heuristics method to solve hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was firstly proposed for tackling the well known Traveling Salesman Problem (TSP) . In this paper, we introduce Multi Colony Ant Model that achieve positive interaction and negative interaction through Intensification and Diversification to improve original ACS performance. This algorithm is a method to solve problem through interaction between ACS groups that consist of some agent colonies to solve TSP problem. In this paper, we apply this proposed method to TSP problem and evaluates previous method and comparison for the performance and we wish to certify that qualitative level of problem solution is excellent.

• Journal of Korean Society of Transportation
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• v.23 no.3 s.81
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• pp.35-47
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• 2005

This paper is to review a literature concerning Bus Transit Route Network Design(BTRNDP), to describe a future study direction for a systematic application for the BTRNDP. Since a bus transit uses a fixed route, schedule, stop, therefore an approach methodology is different from that of auto network design problem. An approach methodology for BTRNDP is classified by 8 categories: manual & guideline, market analysis, system analytic model. heuristic model. hybrid model. experienced-based model. simulation-based model. mathematical optimization model. In most previous BTRNDP, objective function is to minimize user and operator costs, and constraints on the total operator cost, fleet size and service frequency are common to several previous approach. Transit trip assignment mostly use multi-path trip assignment. Since the search for optimal solution from a large search space of BTRNDP made up by all possible solutions, the mixed combinatorial problem are usually NP-hard. Therefore, previous researches for the BTRNDP use a sequential design process, which is composed of several design steps as follows: the generation of a candidate route set, the route analysis and evaluation process, the selection process of a optimal route set Future study will focus on a development of detailed OD trip table based on bus stop, systematic transit route network evaluation model. updated transit trip assignment technique and advanced solution search algorithm for BTRNDP.

• Journal of the Korea Safety Management & Science
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• v.13 no.1
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• pp.175-182
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• 2011

고객의 요구에 대한 빠른 대응과 유연하고 효율적으로 새로운 제품을 적기에 개발하기 위해서는 제품 플랫폼에 기초한 대량 맞춤이 절실히 요구된다. 이러한 목적을 달성하기 위하여 기업들은 상대적으로 생산비용을 낮게 유지하면서 대량생산의 이점을 유지하고 동시에 고객의 요구사항을 만족시키기 위해, product family를 도입하고 가능하면 작은 변화를 통하여 제품의 다양성을 유지하고자 한다. Product family를 설계할 때 중요한 이슈 중에 하나는 제품의 공통성과 차별성간의 절충점을 찾아내는 것인데, 본 연구에서는 설계자들이 product family 재설계를 용이하게 하기 위한 방법론을 제안한다. 이를 위하여 본 연구에서는 ant colony 알고리즘과 product family의 공통성 평가지수를 이용하여 product family 재설계 방법론을 개발한다. 제안한 방법론은 복잡하고 반복적인 많은 계산과정을 가지고 있는 다른 방법과 달리 메타 휴리스틱 알고리즘을 적용하여 인간의 간섭을 줄이고, 실험결과의 정확도, 반복성 및 강건성을 향상시킨다. 본 연구에서는 컴퓨터 마우스 제품군을 대상으로 제안한 방법의 타당성을 검증하였고, 추가적으로 product family 레벨과 부품 레벨의 product family 재설계 추천방안도 제시하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.572-580
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• 2021

In order to reduce transmission delay and increase reliability in networks, mainly high-performance and high-power network equipment is used to guarantee network quality. In this paper, we propose an optimization algorithm to minimize the energy consumed when transmitting traffic in networks with a bundle link composed of multiple physical cables. The proposed optimization algorithm is a meta-heuristic method, which uses tabu search algorithm. In addition, it is designed to minimize transmission energy by minimizing the cables on the paths of the source and destination nodes for each traffic. In the proposed optimization algorithm, performance evaluation was performed in terms of the number of cables used in the transmission and the link utilization for all traffic on networks, and the performance evaluation result confirmed the superior performance than the previously proposed method.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.6
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• pp.301-308
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• 2018

This paper presents an optimal design method of a hybrid structural control system considering multi-hazard. Unlike a typical structural control system in which one system is designed for one specific type of hazard, a simultaneous optimal design method for both active and passive control systems is proposed for the mitigation of seismic and wind induced vibration responses of structures. As a numerical example, an optimal design problem is illustrated for a hybrid mass damper(HMD) and 30 viscous dampers which are installed on a 30 story building structure. In order to solve the optimization problem, a self-adaptive Harmony Search(HS) algorithm is adopted. Harmony Search algorithm is one of the meta-heuristic evolutionary methods for the global optimization, which mimics the human player's tuning process of musical instruments. A self-adaptive, dynamic parameter adjustment algorithm is also utilized for the purpose of broad search and fast convergence. The optimization results shows that the performance and effectiveness of the proposed system is superior with respect to a reference hybrid system in which the active and passive systems are independently optimized.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.69-76
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• 2022

The traveling salesman problem(TSP) is one of the most famous combinatorial optimization problem. So far, many metaheuristic search algorithms have been proposed to solve the problem, and one of them is local search. One of the very important factors in local search is neighbor generation method, and random-based neighbor generation methods such as inversion have been mainly used. This paper proposes 4 new greedy-based neighbor generation methods. Three of them are based on greedy insertion heuristic which insert selected cities one by one into the current best position. The other one is based on greedy rotation. The proposed methods are applied to first-choice hill-climbing search and simulated annealing which are representative local search algorithms. Through the experiment, we confirmed that the proposed greedy-based methods outperform the existing random-based methods. In addition, we confirmed that some greedy-based methods are superior to the existing local search methods.

• Journal of KIISE:Software and Applications
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• v.29 no.9
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• pp.645-653
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• 2002

The unit commitment problem in a power system involves determining the start-up and shut-down schedules of many dynamos for a day or a week while satisfying the power demands and diverse constraints of the individual units in the system. It is very difficult to derive an economically optimal schedule due to its huge search space when the number of dynamos involved is large. Tabu search is a popular solution method used for various optimization problems because it is equipped with effective means of searching beyond local optima and also it can naturally incorporate and exploit domain knowledge specific to the target problem. When given a large-scaled problem with a number of complicated constraints, however, tabu search cannot easily find a good solution within a reasonable time. This paper shows that a large- scaled optimization problem such as the unit commitment problem can be solved efficiently by using a parallel tabu search. The parallel tabu search not only reduces the search time significantly but also finds a solution of better quality.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.157-164
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• 2023

This paper suggests O(n²) polynomial time heuristic algorithm for corporate tax structure optimization problem that has been classified as NP-complete problem. The proposed algorithm constructs tax tree levels that the target holding company is located at root node of Level 1, and the tax code categories(T_e) 1,4,3,2 are located in each level 2,3,4,5 sequentially. To find the maximum tax-relief path from source(S) to target(T), firstly we connect the minimum witholding tax rate _minr_w(u, v) arc of node u point of view for transfer the profit from u to v node. As a result we construct the spanning tree from all of the source nodes to a target node, and find the initial feasible solution. Nextly, we find the alternate path with minimum foreign tax rate _minr_fi(u, v) of v point of view. Finally we choose the minimum tax-relief path from of this two paths. The proposed heuristic algorithm performs better optimal results than linear programming and Tabu search method that is a kind of metaheuristic method.