• 산업경영시스템학회지
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• 제29권4호
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• pp.75-82
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• 2006

The main objective of this study is to find out the shortest path of the vehicle routing problem with time window constraints by using both genetic algorithm and heuristic. Hard time constraints were considered to the vehicle routing problem in this suggested algorithm. Four different heuristic rules, modification process for initial and infeasible solution, 2-opt process, and lag exchange process, were applied to the genetic algorithm in order to both minimize the total distance and improve the loading rate at the same time. This genetic algorithm is compared with the results of existing problems suggested by Solomon. We found better solutions concerning vehicle loading rate and number of vehicles in R-type Solomon's examples R103 and R106.

• Journal of Information Processing Systems
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• 제5권4호
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• pp.237-242
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• 2009

The classical vehicle routing problem (VRP) can be extended by including customers who want to send goods to the depot. This type of VRP is called the vehicle routing problem with pickups and deliveries (VRPPD). This study proposes a novel way to solve VRPPD by introducing a two-phase heuristic routing algorithm which consists of a clustering phase and uses the geometrical center of a cluster and route establishment phase by applying a two-way search of each route after applying the TSP algorithm on each route. Experimental results show that the suggested algorithm can generate better initial solutions for more computer-intensive meta-heuristics than other existing methods such as the giant-tour-based partitioning method or the insertion-based method.

• 대한산업공학회지
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• 제33권4호
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• pp.447-456
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• 2007

The ant colony optimization (ACO) is a probabilistic Meta-heuristic algorithm which has been developed in recent years. Originally ACO was used for solving the well-known Traveling Salesperson Problem. More recently, ACO has been used to solve many difficult problems. In this paper, we develop an ant colony optimization method to solve the maximum independent set problem, which is known to be NP-hard. In this paper, we suggest a new method for local information of ACO. Parameters of the ACO algorithm are tuned by evolutionary operations which have been used in forecasting and time series analysis. To show the performance of the ACO algorithm, the set of instances from discrete mathematics and computer science (DIMACS)benchmark graphs are tested, and computational results are compared with a previously developed ACO algorithm and other heuristic algorithms.

• 한국컴퓨터정보학회논문지
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• 제21권2호
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• pp.71-77
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• 2016

There has been unknown polynomial time algorithm for cellular formation problem (CFP) that is one of the NP-hard problem. Therefore metaheuristic method has been applied this problem to obtain approximated solution. This paper shows the existence of polynomial-time heuristic algorithm in CFP. The proposed algorithm performs coarse-grained and fine-grained cell formation process. In coarse-grained cell formation process, the cell can be formed in accordance with machine frequently used that is the number of other products use same machine with special product. As a result, the machine can be assigned to most used cell. In fine-grained process, the product and machine are moved into other cell that has a improved grouping efficiency. For 35 experimental data, this heuristic algorithm performs better grouping efficiency for 12 data than best known of meta-heuristic methods.

• Structural Engineering and Mechanics
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• 제45권2호
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• pp.183-199
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• 2013

Current methods of analysis of trusses depend on matrix formulations based on equilibrium equations which are in fact derived from energy principles, and compatibility conditions. Recently it has been shown that the minimum energy principle, by itself, in its pure and unmodified form, can well be exploited to analyze structures when coupled with an optimization algorithm, specifically with a meta-heuristic algorithm. The resulting technique that can be called Total Potential Optimization using Meta-heuristic Algorithms (TPO/MA) has already been applied to analyses of linear and nonlinear plane trusses successfully as coupled with simulated annealing and local search algorithms. In this study the technique is applied to both 2-dimensional and 3-dimensional trusses emphasizing robustness, reliability and accuracy. The trials have shown that the technique is robust in two senses: all runs result in answers, and all answers are acceptable as to the reliability and accuracy within the prescribed limits. It has also been shown that Harmony Search presents itself as an appropriate algorithm for the purpose.

• 한국인터넷방송통신학회논문지
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• 제21권6호
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• pp.33-41
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• 2021

본 논문은 NP-난제로 알려진 능력한정 최소신장트리 문제(CMST)의 해를 다항시간으로 찾을 수 있는 규칙을 가진 휴리스틱 탐욕 알고리즘을 제안하였다. CMST는 다항시간으로 해를 구하는 방법인 EW 알고리즘의 성능이 좋지 않아 컴퓨터 프로그램의 도움을 받는 메타휴리스틱 기법들을 적용하고 있다. 그러나 메타휴리스틱 기법들도 최적 해를 찾지 못하는 성능의 한계를 보였다. 본 논문에서는 컴퓨터 도움 없이 시각적으로 손으로 CMST의 해를 찾는 규칙을 제시하였다. 제안된 방법은 먼저 MST를 작도하고, MST로부터 초기 CMST의 실현 가능 해를 구하고, CMST의 해를 개선하기 위해 서브트리의 게이트들이 근 노드에 보다 근접하도록 설정하는 최적화 과정을 수행하였다. 제안된 알고리즘을 OR-LIB의 10개 데이터, Q=3,5,10의 30개 경우에 대해 적용한 결과 최상의 성능을 보였다.

• 한국콘텐츠학회논문지
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• 제10권9호
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• pp.107-116
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• 2010

시설물 입지 선정 문제(FLP)는 전통적인 최적화 문제중에 하나이다. FLP에 공급제약과 하나의 고객은 하나의 시설물에서만 제품을 공급받을 수 있다는 제약을 추가하면 단일 시설물 공급제약을 가지는 시설물 위치 설정 문제(SSFLP)가 된다. SSFLP는 NP-hard 문제로 알려져 있으며 진화 알고리즘과 같은 휴리스틱 알고리즘을 사용하여 해결하는 것이 일반적이다. 본 논문에서는 SSFLP를 위한 효율적인 진화 알고리즘을 제안한다. 제안하는 알고리즘은 적응형 링크 조절 진화 알고리즘과 3가지 휴리스틱 해 개선 방법을 조합하여 고안되었다. 제안하는 알고리즘을 벤치마크 문제에 적용하여 다른 알고리즘과 성능을 비교분석해 본 결과, 제안하는 알고리즘은 중간 크기의 문제에서 대부분 최적해를 찾았으며 큰 문제에서도 안정된 결과를 보여주었다.

• Structural Engineering and Mechanics
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• 제53권5호
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• pp.847-865
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• 2015

This paper presents the application of a recently developed meta-heuristic algorithm, called Colliding Bodies Optimization (CBO), for size and topology optimization of steel trusses. This method is based on the one-dimensional collisions between two bodies, where each agent solution is considered as a body. The performance of the proposed algorithm is investigated through four benchmark trusses for minimum weight with static and dynamic constraints. A comparison of the numerical results of the CBO with those of other available algorithms indicates that the proposed technique is capable of locating promising solutions using lesser or identical computational effort, with no need for internal parameter tuning.

• 대한산업공학회지
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• 제35권4호
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• pp.218-225
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• 2009

This paper considers a parallel-machine scheduling problem with job release times and sequence-dependent setup times. The objective of this problem is to determine the allocation policy of jobs and the scheduling policy of machines so as to minimize the weighted sum of setup times, delay times, and tardy times. A mathematical model for optimal solution is derived and a meta heuristic algorithm based on the improved ant colony system is proposed in this paper. The performance of the meta heuristic algorithm is evaluated through compare with optimal solutions using randomly generated several examples.

• Smart Structures and Systems
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• 제20권1호
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• pp.99-114
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• 2017

In cognitive science, it is illustrated how the collective opinions of a group of individuals answers to questions involving quantity estimation. One example of this approach is introduced in this article as Star Graph (SG) algorithm. This graph describes the details of communication among individuals to share their information and make a new decision. A new labyrinthine network of neighbors is defined in the decision-making process of the algorithm. In order to prevent getting trapped in local optima, the neighboring networks are regenerated in each iteration of the algorithm. In this algorithm, the normal distribution is utilized for a group of agents with the best results (guidance group) to replace the existing infeasible solutions. Here, some new functions are introduced to provide a high convergence for the method. These functions not only increase the local and global search capabilities but also require less computational effort. Various benchmark functions and engineering problems are examined and the results are compared with those of some other algorithms to show the capability and performance of the presented method.