• 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.

• Steel and Composite Structures
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• 제24권1호
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• pp.129-140
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• 2017

Structural optimization is one of the popular and active research areas in the field of structural engineering. In the present study, the newly developed moth-flame optimization (MFO) algorithm and its enhanced version termed as enhanced moth-flame optimization (EMFO) are employed to implement the optimization process of planar and 3D steel frame structures with discrete design variables. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. A number of benchmark steel frame optimization problems are solved by the MFO and EMFO algorithms and the results are compared with those of other meta-heuristics. The obtained numerical results indicate that the proposed EMFO algorithm possesses better computational performance compared with other existing meta-heuristics.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2003년도 춘계공동학술대회
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• pp.1065-1070
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• 2003

Facility Layout Planning is concerned with how to arrange facilities necessary for production in a given space. Its objective is often to minimize the total sum of all material flows multiplied by the distance among facilities. FLP belongs to NP complete problem; i.e., the number of possible layout solutions increases with the increase of the number of facilities. Thus, meta heuristics such as Genetic Algorithm (GA) and Simulated Annealing have been investigated to solve the FLP problems. However, one of the biggest problems which lie in the existing meta heuristics including GA is hard to find an appropriate combinations of parameters which result in optimal solutions for the specific problem. The Ant System algorithm with elitist and ranking strategies is used to solve the FLP problem as an another good alternative. Experimental results show that the AS algorithm is able to produce the same level of solution quality with less sensitive parameters selection comparing to the ones obtained by applying other existing meta heuristic algorithms.

• 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.

• Structural Engineering and Mechanics
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• 제66권4호
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• pp.465-476
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• 2018

In this paper, a new meta-heuristic optimization method is presented. This new method is named "Numbers Cup Optimization" (NCO). The NCO algorithm is inspired by the sport competitions. In this method, the objective function and the design variables are defined as the team and the team members, respectively. Similar to all cups, teams are arranged in groups and the competitions are performed in each group, separately. The best team in each group is determined by the minimum or maximum value of the objective function. The best teams would be allowed to the next round of the cup, by accomplishing minor changes. These teams get grouped again. This process continues until two teams arrive the final and the champion of the Numbers Cup would be identified. In this algorithm, the next cups (same iterations) will be repeated by the improvement of players' performance. To illustrate the capabilities of the proposed method, some standard functions were selected to optimize. Also, size optimization of three benchmark trusses is performed to test the efficiency of the NCO approach. The results obtained from this study, well illustrate the ability of the NCO in solving the optimization problems.

• Earthquakes and Structures
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• 제18권5호
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• pp.625-635
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• 2020

Suspension bridges have the extended in plan configuration which makes them prone to dynamic events like earthquake. The longer span lead to more flexibility and slender of them. So, control systems seem to be essential in order to protect them against ground motion excitation. Tuned mass damper or in brief TMD is a passive control system that its efficiency is practically proven. Moreover, its parameters i.e. mass ratio, tuning frequency and damping ratio can be optimized in a manner providing the best performance. Meta-heuristic optimization algorithm is a powerful tool to gain this aim. In this study, TMD parameters are optimized in different-length suspension bridges in three distinct cases including 3, 4 and 5 TMDs by observer-teacher-learner based algorithm under a complete set of ground motions formed from both near-field and far-field instances. The Vincent Thomas, Tacoma Narrows and Golden Gate suspension bridges are selected for case studies as short, mean and long span ones, respectively. The results indicate that All cases of used TMDs result in response reduction and case 4TMD can be more suitable for bridges in near and far-field conditions.

• 한국CDE학회논문집
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• 제15권1호
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• pp.1-10
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• 2010

Recently, a shipyard is making every effort to efficiently manage equipments of resources such as a gantry crane, transporter, and so on. So far block lifting scheduling of a gantry crane has been manually performed by a manager of the shipyard, and thus it took much time to get scheduling results and moreover the quality of them was not optimal. To improve this, a block lifting scheduling system of the gantry crane using optimization techniques was developed in this study. First, a block lifting scheduling problem was mathematically formulated as a multi-objective optimization problem, considering the minimization of travel distance at an idle state and wire replacement during block lifting. Then, to solve the problem, a meta-heuristic optimization algorithm based on the genetic algorithm was proposed. To evaluate the efficiency and applicability of the developed system, it was applied to an actual block lifting scheduling problem of the shipyard. The result shows that blocks can be efficiently lifted by the gantry crane using the developed system, compared to manual scheduling by a manager.

• 한국통신학회논문지
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• 제37권4B호
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• pp.236-244
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• 2012

무선 네트워크에서 최소 에너지 브로드캐스트 문제는 네트워크에 배치된 모든 노드가 브로드캐스팅과정에서 데이터 전송에 사용되는 에너지를 최소화하는 문제이다. 본 논문은 무선 센서 네트워크에서 최소 에너지 브로드캐스트 문제를 효과적으로 해결할 수 있는 메타휴리스틱 기법인 타부서치 알고리즘을 제안한다. 보다 효과적인 해 검색을 위해 제안된 알고리즘은 새로운 이웃해 생성방식과 복구함수를 적용한다. 제안된 알고리즘의 성능평가는 배치된 모든 노드로의 브로드캐스팅 시 전송 에너지와 알고리즘 실행시간 관점에서 기존의 알고리즘과 비교를 하였으며, 실험 결과에서 제안된 알고리즘이 최소 에너지 브로드캐스트 문제에 효과적으로 적용됨을 보여준다.

• 한국정보시스템학회지:정보시스템연구
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• 제25권1호
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• pp.159-182
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• 2016

Purpose This paper aims to develop a novel tabu search algorithm for solving the sequencing problems with precedence constraints. Due to constraints, the traditional meta heuristic methods can generate infeasible solutions during search procedure, which must be carefully dealt with. On the contrary, the candidate order based tabu search (COTS) is based on a novel neighborhood structure that guarantees the feasibility of solutions, and can dealt with a wide range of sequencing problems in flexible manner. Design/methodology/approach Candidate order scheme is a strategy for constructing a feasible sequence by iteratively appending an item at a time, and it has been successfully applied to genetic algorithm. The primary benefit of the candidate order scheme is that it can effectively deal with the additional constraints of sequencing problems and always generates the feasible solutions. In this paper, the candidate order scheme is used to design the neighborhood structure, tabu list and diversification operation of tabu search. Findings The COTS has been applied to the single machine job sequencing problems, and we can see that COTS can find the good solutions whether additional constraints exist or not. Especially, the experiment results reveal that the COTS is a promising approach for solving the sequencing problems with precedence constraints. In addition, the operations of COTS are intuitive and easy to understand, and it is expected that this paper will provide useful insights into the sequencing problems to the practitioners.

• 한국지능시스템학회논문지
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• 제14권1호
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• pp.15-19
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• 2004

개미군락시스템 (Ant Colony System, ACS)은 조합 최적화 문제를 해결하기 위한 기법으로 생물학적 기반의 메타휴리스틱 접근법이다. 지나간 경로에 대하여 페로몬을 분비하고 통신 매개물로 사용하는 실제 개미들의 추적 행위를 기반으로 한다. 최적 경로를 찾기 위해서는 보다 다양한 에지들에 대한 탐색이 필요하다. 기존 개미군락시스템의 지역 갱신 규칙에서는 지나간 에지에 대하여 고정된 페로몬 갱신 값을 부여하고 있다. 그러나 본 논문에서는 방문한 도시간의 거리와 해당 에지의 방문 횟수를 이용하여 페로몬을 부여한다. 보다 많은 정보를 탐색에 활용함으로써 기존의 방법에 비해 지역 최적화에 빠지지 않고 더 나은 해를 찾을 수 있었다.