• Journal of Ocean Engineering and Technology
• /
• v.17 no.6
• /
• pp.38-46
• /
• 2003

Aim of this article is to propose Micro-Genetic Simulated Annealing (${\mu}GSA$) as a hybrid metaheuristics approach to find the global optimum of nonlinear optimisation problems. This approach combines the features of modern metaheuristics such as micro-genetic algorithm (${\mu}GAs$) and simulated annealing (SA) with the general robustness of parallel exploration and asymptotic convergence, respectively. Therefore, ${\mu}GSA$ approach can help in avoiding the premature convergence and can search for better global solution, because of its wide spread applicability, global perspective and inherent parallelism. For the superior performance of the ${\mu}GSA$, the five well-know benchmark test functions that were tested and compared with the two global optimisation approaches: scatter search (SS) and hybrid scatter genetic tabu (HSGT) approach. A practical application to structural sandwich panel is also examined by optimism the weight function. From the simulation results, it has been concluded that the proposed ${\mu}GSA$ approach is an effective optimisation tool for soloing continuous nonlinear global optimisation problems in suitable computational time frame.

• Journal of Korean Association for Spatial Structures
• /
• v.21 no.2
• /
• pp.89-97
• /
• 2021

This paper describes an adaptive hybrid evolutionary firefly algorithm for a topology optimization of truss structures. The truss topology optimization problems begins with a ground structure which is composed of all possible nodes and members. The optimization process aims to find the optimum layout of the truss members. The hybrid metaheuristics are then used to minimize the objective functions subjected to static or dynamic constraints. Several numerical examples are examined for the validity of the present method. The performance results are compared with those of other metaheuristic algorithms.

• IE interfaces
• /
• v.24 no.4
• /
• pp.281-294
• /
• 2011

This paper considers the rail crane scheduling problem which is defined as determining the sequence of loading/unloading container on/from a freight train. The objective is to minimize the weighted sum of the range of order completion time and makespan. The range of order completion time implies the difference between the maximum of completion time and minimum of start time of each customer order consisting of jobs. Makespan refers to the time when all the jobs are completed. In a rail freight terminal, logistics firms as a customer wish to reduce the range of their order completion time. To develop a methodology for the crane scheduling, we formulate the problem as a mixed integer program and develop three metaheuristics, namely, genetic algorithm, simulated annealing, and tabu search. To validate the effectiveness of heuristic algorithms, computational experiments are done based on a set of real life data. Results of the experiments show that heuristic algorithms give good solutions for small-size and large-size problems in terms of solution quality and computation time.

• Structural Engineering and Mechanics
• /
• v.77 no.3
• /
• pp.417-439
• /
• 2021

The geometric nonlinearity has been successfully integrated with the design of steel structural system. Thus, the tubular lattice girder, one application of steel structural systems have already been optimized to obtain an economic design following the completion of computationally expensive design procedure. In order to decrease its computing cost, this study proposes to employ five multi-objective metaheuristics for the design optimization of geometrically nonlinear tubular lattice girder. Then, the employed multi-objective optimization algorithms (MOAs), NSGAII, PESAII, SPEAII, AbYSS and MoCell are evaluated considering their computing performances. For an unbiased evaluation of their computing performance, a tubular lattice girder with varying size-shape-topology and a benchmark truss design with 17 members are not only optimized considering the geometrically nonlinear behavior, but three benchmark mathematical functions along with the four benchmark linear design problems are also included for the comparison purpose. The proposed experimental study is carried out by use of an intelligent optimization tool named JMetal v5.10. According to the quantitative results of employed quality indicators with respect to a statistical analysis test, MoCell is resulted with an achievement of showing better computing performance compared to other four MOAs. Consequently, MoCell is suggested as an optimization tool for the design of geometrically nonlinear tubular lattice girder than the other employed MOAs.

• Proceedings of the Korea Contents Association Conference
• /
• 2014.06a
• /
• pp.35-36
• /
• 2014

• The KIPS Transactions:PartB
• /
• v.16B no.5
• /
• pp.411-426
• /
• 2009

Most of the metaheuristics are made by imitating the action of the animals. In this paper, we proposed Mine Algorithm. The Mine Algorithm is a metaheuristic that imitates the action of the human being. Speaking of search, the field in which the know-how and the heuristics of the human being are melted best is the mining industry. In the Mine Algorithm we formalize the action pattern of the human being by focusing the mine business. The Mine Algorithm uses various searching techniques fluently and shows equally good performance for broad problems. That is, it has good generality. We show the improved generality of the Mine Algorithm by the comparing experiments with the conventional metaheuristics.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2003.11a
• /
• pp.119-122
• /
• 2003

본 논문에서는 분산메모리 멀티프로세서 시스템에서 태스크와 프로세서 노드간의 매핑에 관한 최적화 문제를 메타휴리스틱스(metatheuristics)의 장점을 효과적으로 결합한 새로운 방안을 소개한다. 태스크-프로세서 할당에 있어 부하균형을 고려한 MFA-GA 하이브리드 알고리즘을 제안하고 기존의 할당 방안들과 성능실험을 통해 비교 분석한다. 우리의 합성 휴리스틱을 이용하면 각 방법을 단독으로 사용하는 것 보다 매핑 품질과 수행시간 면에서 개선된 성능결과를 얻을 수 있음을 보여주었다.

• Industrial Engineering and Management Systems
• /
• v.7 no.3
• /
• pp.204-213
• /
• 2008

Companies that produce capital goods need to schedule the production of products that have complex product structures with components that require many operations on different machines. A feasible schedule must satisfy operation and assembly precedence constraints. It is also important to avoid deadlock situations. In this paper a Genetic Algorithm (GA) has been developed that includes a new repair process that rectifies infeasible schedules that are produced during the evolution process. The algorithm was designed to minimise the combination of earliness and tardiness penalties and took into account finite capacity constraints. Three different sized problems were obtained from a collaborating capital goods company. A design of experimental approach was used to systematically identify that the best genetic operators and GA parameters for each size of problem.

• Journal of Advanced Marine Engineering and Technology
• /
• v.38 no.10
• /
• pp.1340-1346
• /
• 2014

Previous studies of reliable server assignment considered only to assign the same cost of server, that is, homogeneous servers. In this paper, we generally deal with reliable server assignment with different server costs, i.e., heterogeneous servers. We formulate this problem as a nonlinear integer programming mathematically. Our problem is defined as determining a deployment of heterogeneous servers to maximize a measure of service availability. We propose two metaheuristic algorithms (tabu search and particle swarm optimization) for solving the problem of reliable server assignment. From the computational results, we notice that our tabu search outstandingly outperforms particle swarm optimization for all test problems. In terms of solution quality and computing time, the proposed method is recommended as a promising metaheuristic for a kind of reliability optimization problems including reliable sever assignment and e-Navigation system.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.20 no.3
• /
• pp.441-453
• /
• 2017

In this paper, a new type of weapon-target assignment(WTA) problem has been suggested that reflects realistic constraints for sharing target with other weapons and shooting double rapid fire. To utilize in rapidly changing actual battle field, the computation time is of great importance. Several metaheuristic methods such as Simulated Annealing, Tabu Search, Genetic Algorithm, Ant Colony Optimization, and Particle Swarm Optimization have been applied to the real-time WTA in order to find a near optimal solution. A case study with a large number of targets in consideration of the practical cases has been analyzed by the objective value of each algorithm.