• Industrial Engineering and Management Systems
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• 제6권1호
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• pp.64-71
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• 2007

J. Kennedy and R. Eberhart first introduced the concept called as Particle Swarm Optimization (PSO). They applied it to optimize continuous nonlinear functions and demonstrated the effectiveness of the algorithm. Since then a considerable number of researchers have attempted to apply this concept to a variety of optimization problems and obtained reasonable results. In PSO, individuals communicate and exchange simple information with each other. The information among individuals is communicated in the swarm and the information between individuals and their swarm is also shared. Finally, the swarm approaches the optimal behavior. It is reported that reasonable approximate solutions of various types of test functions are obtained by employing PSO. However, if more precise solutions are required, additional algorithms and/or hybrid algorithms would be necessary. For example, the heading vector of the swarm can be slightly adjusted under some conditions. In this paper, we propose a hybrid algorithm to obtain more precise solutions. In the algorithm, when a better solution in the swarm is found, the neighborhood of a certain distance from the solution is searched. Then, the algorithm returns to the original PSO search. By this hybrid method, we can obtain considerably better solutions in less iterations than by the standard PSO method.

• 한국산업정보학회논문지
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• 제27권1호
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• pp.37-48
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• 2022

본 연구에서는 공학설계 최적화 문제 해결을 위한 혼합 메타휴리스틱(Hybrid Meta-heuristic) 접근법을 제안된다. 공학 설계 최적화 문제는 다양한 형태의 변수를 가지며, 복잡한 제약조건들하에서 그 최적해를 구하는 문제로 이미 많은 기존 연구들을 통해 다양한 접근법들이 개발되어져 왔다. 하지만 그 효율성은 아직까지 크게 개선되지 못하고 있는 실정이다. 따라서 본 연구에서는 이러한 효율성을 개선하기 위한 새로운 접근법을 제안한다. 제안된 혼합 메타휴리스틱 접근법은 탐색 공간에 대한 전역적 탐색을 위해 유전알고리즘(Genetic Algorithm: GA) 접근법, 지역적 탐색을 위해 변동이웃탐색(Variable Neighborhood Search: VNS) 접근법과 언덕오르기(Hill Climbing: HC) 접근법을 혼합(GA-VNS-HC)하였다. 사례 연구에서는 다양한 형태의 공학설계 최적화 문제를 이용하여 본 연구에서 제안한 GA-VNS-HC 접근법의 우수성을 입증하였다.

• 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.

• Journal of Information Processing Systems
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• 제16권4호
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• pp.959-974
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• 2020

High efficiency video coding (HEVC) employs quadtree coding tree unit (CTU) structure to improve its coding efficiency, but at the same time, it also requires a very high computational complexity due to its exhaustive search processes for an optimal coding unit (CU) partition. With the aim of solving the problem, a fast CU size decision optimal algorithm based on neighborhood prediction is presented for HEVC in this paper. The contribution of this paper lies in the fact that we successfully use the partition information of neighborhood CUs in different depth to quickly determine the optimal partition mode for the current CU by neighborhood prediction technology, which can save much computational complexity for HEVC with negligible RD-rate (rate-distortion rate) performance loss. Specifically, in our scheme, we use the partition information of left, up, and left-up CUs to quickly predict the optimal partition mode for the current CU by neighborhood prediction technology, as a result, our proposed algorithm can effectively solve the problem above by reducing many unnecessary prediction and partition operations for HEVC. The simulation results show that our proposed fast CU size decision algorithm based on neighborhood prediction in this paper can reduce about 19.0% coding time, and only increase 0.102% BD-rate (Bjontegaard delta rate) compared with the standard reference software of HM16.1, thus improving the coding performance of HEVC.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 1998년도 추계학술대회논문집:21세기에 대비한 지능형 통합항만관리
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• pp.315-322
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• 1998

It is discussed how to route yard-side equipment during the loading operation in port container terminals. The number of containers to be picked up at each yard-bay, as well as the route of a yard-side equipment (for example, transfer crane or straddle carrier) in a yard, are determined. The objective of the problem is to minimize the total container handling time in the yard. An encoding method to represent nodes in the search space is introduced utilizing inherent properties of the optimal solution by which the search space is greatly reduced. A beam search algorithm is suggested. A numerical experiment is carried out to compared the performance of the beam search algorithm with those of other approaches.

• 경영과학
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• 제12권3호
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• pp.177-185
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• 1995

This paper presents a Tabu Search method to minimize a number of tardy jobs in the nonidentical parallel machine scheduling. The Tabu Search method employs a restricted neighborhood for the reduction of computation time. In this paper, we use two different types of method for a single machine scheduling. One is Moore's algorithm and the other is insertion method. We discuss computational experiments on more than 1000 test problems.

• 한국정보통신학회논문지
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• 제19권5호
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• pp.1084-1090
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• 2015

본 논문에서는 무선 센서 네트워크에서 네트워크의 감시영역을 최대화하기 위해 센서 노드를 효과적으로 배치하는 타부 서치 알고리즘을 제안한다. 무선 센서 네트워크에서 센서 노드의 수가 증가하게 되면 네트워크의 감시영역을 최대화하기 위한 계산량은 급격히 늘어나게 된다. 본 논문에서는 센서 배치 밀도가 높은 네트워크에서 적정한 실행 시간 내에 네트워크의 감시영역을 최대화하는 타부 서치 알고리즘을 제안하며, 효율적인 검색을 위해 타부 서치 알고리즘의 새로운 이웃해 생성 동작을 제안한다. 제안된 알고리즘은 네트워크의 최대 감시영역과 실행속도 관점에서 성능을 평가하며, 평가 결과에서 제안된 알고리즘이 기존의 알고리즘에 비해 성능이 우수함을 보인다.

• Steel and Composite Structures
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• 제45권4호
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• pp.579-590
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• 2022

One of the biggest problems in structural steel calculation is the design of structures using the lowest possible material weight, making this a slow and costly process. To achieve this objective, several optimization methods have been developed and tested. Nevertheless, a method that performs very efficiently when applied to different problems is not yet available. Based on this assumption, this work proposes a hybrid metaheuristic algorithm for geometric and dimensional optimization of space trusses, called Simulated Squirrel Search Algorithm, which consists of an association of the well-established neighborhood shifting algorithm (Simulated Annealing) with a recently developed promising population algorithm (Squirrel Search Algorithm, or SSA). In this study, two models are tried, being respectively, a classical model from the literature (25-bar space truss) and a roof system composed of space trusses. The structures are subjected to resistance and displacement constraints. A penalty function using Fuzzy Logic (FL) is investigated. Comparative analyses are performed between the Squirrel Search Algorithm (SSSA) and other optimization methods present in the literature. The results obtained indicate that the proposed method can be competitive with other heuristics.

• Structural Engineering and Mechanics
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• 제77권6호
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• pp.779-795
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• 2021

In the recent decades, various optimization algorithms have been considered for the optimization of structures. In this research, a new enhanced algorithm is used for the size and topology optimization of truss structures. This algorithm, which is obtained from the combination of Crow Search Algorithm (CSA) and the Cellular Automata (CA) method, is called CA-CSA method. In the first iteration of the CA-CSA method, some of the best designs of the crow's memory are first selected and then located in the cells of CA. Then, a random cell is selected from CA, and the best design is chosen from the selected cell and its neighborhood; it is considered as a "local superior design" (LSD). In the optimization process, the LSD design is used to modify the CSA method. Numerical examples show that the CA-CSA method is more effective than CSA in the size and topology optimization of the truss structures.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2005년도 춘계 학술발표회 논문집
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• pp.359-366
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• 2005

Most engineering optimization are based on numerical linear and nonlinear programming methods that require substantial gradient information and usually seek to improve the solution in the neighborhood of a starting point. These algorithm, however, reveal a limited approach to complicated real-world optimization problems. If there is more than one local optimum in the problem, the result may depend on the selection of an initial point, and the obtained optimal solution may not necessarily be the global optimum. This paper describes a new harmony search(HS) meta-heuristic algorithm-based approach for structural size optimization problems with continuous design variables. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. Two classical space truss optimization problems are presented to demonstrate the effectiveness and robustness of the HS algorithm. The results indicate that the proposed approach is a powerful search and optimization technique that may yield better solutions to structural engineering problems than those obtained using current algorithms.