• Structural Engineering and Mechanics
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• 제62권6호
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• pp.749-758
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• 2017

In structural mechanics, traditional analyses methods usually employ matrix operations for obtaining displacement and internal forces of the structure under the external effects, such as distributed loads, earthquake or wind excitations, and temperature changing inter alia. These matrices are derived from the well-known principle of mechanics called minimum potential energy. According to this principle, a system can be in the equilibrium state only in case when the total potential energy of system is minimum. A close examination of the expression of the well-known equilibrium condition for linear problems, $P=K{\Delta}$, where P is the load vector, K is the stiffness matrix and ${\Delta}$ is the displacement vector, it is seen that, basically this principle searches the displacement set (or deformed shape) for a system that minimizes the total potential energy of it. Instead of using mathematical operations used in the conventional methods, with a different formulation, meta-heuristic algorithms can also be used for solving this minimization problem by defining total potential energy as objective function and displacements as design variables. Based on this idea the technique called Total Potential Optimization using Meta-heuristic Algorithms (TPO/MA) is proposed. The method has been successfully applied for linear and non-linear analyses of trusses and truss-like structures, and the results have shown that the approach is much more successful than conventional methods, especially for analyses of non-linear systems. In this study, the application of TPO/MA, with Harmony Search as the selected meta-heuristic algorithm, to cables net system is presented. The results have shown that the method is robust, powerful and accurate.

• 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.

• 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.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권12호
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• pp.3096-3117
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• 2013

To execute the performance driven Grid applications, an effective and scalable workflow scheduling is seen as an essential. To optimize cost & makespan, in this paper, we propose a Scalable Cost-Time Trade-off (SCTT) model for scheduling workflow tasks. We have developed a heuristic algorithm known as Scalable Cost-Time Trade-off Scheduling (SCTTS) with a lower runtime complexity based on the proposed SCTT model. We have compared the performance of our proposed approach with other heuristic and meta-heuristic based scheduling strategies using simulations. The results show that the proposed approach improves performance and scalability with different workflow sizes, task parallelism and heterogeneous resources. This method, therefore, outperforms other methods.

• 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.

• 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.

• 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.

• Industrial Engineering and Management Systems
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• 제4권1호
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• pp.94-101
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• 2005

This paper presents the first known application of a meta-heuristic algorithm, variable neighbourhood descent (VND), to the redundancy allocation problem (RAP). The RAP, a well-known NP-hard problem, has been the subject of much prior work, generally in a restricted form where each subsystem must consist of identical components. The newer meta-heuristic methods overcome this limitation and offer a practical way to solve large instances of the relaxed RAP where different components can be used in parallel. The variable neighbourhood descent method has not yet been used in reliability design, yet it is a method that fits perfectly in those combinatorial problems with potential neighbourhood structures, as in the case of the RAP. A variable neighbourhood descent algorithm for the RAP is developed and tested on a set of well-known benchmark problems from the literature. Results on 33 test problems ranging from less to severely constrained conditions show that the variable neighbourhood descent method provides comparable solution quality at a very moderate computational cost in comparison with the best-known heuristics. Results also indicate that the VND method performs with little variability over random number seeds.

• 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.