• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2008년도 추계 공동 국제학술대회
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• pp.463-467
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• 2008

Heuristic optimization using hybrid algorithms have provided a robust and efficient approach for solving many optimization problems. In this paper, a new hybrid algorithm using adaptive genetic algorithm (aGA) and particle swarm optimization (PSO) is proposed. The proposed hybrid algorithm is applied to solve numerical optimization functions. The results are compared with those of GA and other conventional PSOs. Finally, the proposed hybrid algorithm outperforms others.

• 한국국방경영분석학회지
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• 제33권2호
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• pp.31-47
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• 2007

본 연구는 엄격한 시간제약 차량경로문제에 대하여 유전자알고리즘과 휴리스틱 기법을 이용하여 최적해를 산출하는 것이다. 문제해결을 위해 수리적 모형을 구성하고, ILOG-CPLEX를 이용하여 최적해를 산출하였다. 임의 생성방법과 세이빙 휴리스틱을 적용한 초기해 생성, 실행불가능해의 교정과 유전자 알고리즘 종료 후 2-opt, Or-opt 등 해교정 및 해개선을 위한 과정이 추가된 하이브리드 유전자 알고리즘을 구축하여 엄격한 시간제약이 있는 차량경로 문제에 적용하여 솔로몬 예제와 비교하고, 제안한 알고리즘의 해공간탐색능력, 수렴성, 휴리스틱 기법의 효과를 확인하였다.

• 대한산업공학회지
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• 제33권1호
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• pp.86-98
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• 2007

This paper presents a heuristic algorithm for the traveling salesman problem with time windows (TSPTW). Aniterative insertion algorithm as a constructive search heuristic and a hybrid meta heuristic combining simulatedannealing and tabu search with the randomized selection of 2-interchange and a simple move operator as animproving search heuristic are proposed, Computational tests performed on 400 benchmark problem instancesshow that the proposed algorithm generates optimal or near-optimal solutions in most cases. New best knownheuristic values for many benchmark problem sets were obtained using the proposed approach.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제1권1호
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• pp.6-12
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• 2001

Evolutionary algorithms(EAs) have been successfully applied to many combinatorial optimization problems of various engineering fields. Recently, some comparative studies of EAs with other stochastic search algorithms have, however, shown that they are similar to, or even are not comparable to other heuristic search. In this paper, a new hybrid evolutionary algorithm utilizing a new local heuristic search, for combinatorial optimization problems, is presented. The new intelligent local heuristic search is described, and the behavior of the hybrid search algorithm is investigated on two well-known problems: traveling salesman problems (TSPs), and quadratic assignment problems(QAPs). The results indicate that the proposed hybrid is able to produce solutions of high quality compared with some of evolutionary and simulated annealing.

• Steel and Composite Structures
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• 제8권6호
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• pp.475-490
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• 2008

A hybrid tabu-simulated annealing algorithm is proposed for the optimum design of steel frames. The special character of the hybrid algorithm is that it exploits both tabu search and simulated annealing algorithms simultaneously to obtain near optimum. The objective of optimum design problem is to minimize the weight of steel frames under the actual design constraints of AISC-LRFD specification. The performance and reliability of the hybrid algorithm were compared with other algorithms such as tabu search, simulated annealing and genetic algorithm using benchmark examples. The comparisons showed that the hybrid algorithm results in lighter structures for the presented examples.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2010년도 추계학술대회 논문집
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• pp.473-474
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• 2010

• 해양환경안전학회지
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• 제5권2호
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• pp.87-97
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• 1999

This study is concerned with developing a hybrid heuristic algorithm for solving the redundancy optimization problem which is very important in system safety, This study develops a HH(Hybrid Heuristic) method combined with two strategies to alleviate the risks of being trapped at a local optimum. One of them is to construct the populations of the initial solutions randomly. The other is the additional search with SA(Simulated Annealing) method in final step. Computational results indicate that HH performs consistently better than the KY method proposed in Kim[8]. Therefore, the proposed HH is believed to an attractive to other heuristic methods.

• Structural Engineering and Mechanics
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• 제65권6호
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• pp.707-719
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• 2018

In this article, a new method is introduced to improve the local search capability of meta-heuristic algorithms using the projection of the path on the border of constraints. In a mathematical point of view, the Gradient Projection Method is applied through a new approach, while the imposed limitations are removed. Accordingly, the gradient vector is replaced with a new meta-heuristic based vector. Besides, the active constraint identification algorithm, and the projection method are changed into less complex approaches. As a result, if a constraint is violated by an agent, a new path will be suggested to correct the direction of the agent's movement. The presented procedure includes three main steps: (1) the identification of the active constraint, (2) the neighboring point determination, and (3) the new direction and step length. Moreover, this method can be applied to some meta-heuristic algorithms. It increases the chance of convergence in the final phase of the search process, especially when the number of the violations of the constraints increases. The method is applied jointly with the authors' newly developed meta-heuristic algorithm, entitled Star Graph. The capability of the resulted hybrid method is examined using the optimal design of truss and frame structures. Eventually, the comparison of the results with other meta-heuristics of the literature shows that the hybrid method is successful in the global as well as local search.

• Structural Engineering and Mechanics
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• 제42권6호
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• pp.783-797
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• 2012

A new hybrid meta-heuristic optimization algorithm is presented for design of structures. The algorithm is based on the concepts of the charged system search (CSS) and the particle swarm optimization (PSO) algorithms. The CSS is inspired by the Coulomb and Gauss's laws of electrostatics in physics, the governing laws of motion from the Newtonian mechanics, and the PSO is based on the swarm intelligence and utilizes the information of the best fitness historically achieved by the particles (local best) and by the best among all the particles (global best). In the new hybrid algorithm, each agent is affected by local and global best positions stored in the charged memory considering the governing laws of electrical physics. Three different types of structures are optimized as the numerical examples with the new algorithm. Comparison of the results of the hybrid algorithm with those of other meta-heuristic algorithms proves the robustness of the new algorithm.

• 한국산학기술학회논문지
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• 제7권1호
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• pp.89-95
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• 2006

공급사슬망에서 분산되어있는 제조시스템에 대한 생산 계획수립은 공급사슬관리의 주요 연구분야 중의 하나이다. 본 논문은 공급사슬망에서 자원제약을 갖는 다단계 다품목 로트사이즈 결정 문제(Multi-Level, multi-item Capacitated Lot Sizing Problem: MLCLSP)를 위한 알고리즘을 제시한다. MLCLSP는 MIP(mixed integer program) 문제에 해당한다. 제안된 알고리즘은 휴리스틱과 최적화 패키지인 LINGO를 이용해 서로 반복적인 방식으로 해를 풀어나가는 혼성적인 성격을 갖는다. 휴리스틱을 이용하여 정수형 변수를 결정한 후, 얻게 되는 LP(linear program) 문제를 LINGO를 이용하여 해를 개선해 나가는 방식을 기본으로 한다. 본 논문에서는 탐색 휴리스틱 기법으로 임의 재시작 타부탐색 알고리즘을 제시한다. 다양한 시나리오의 실험을 통해 제안된 알고리즘들의 성능을 평가한다.