• 해양환경안전학회지
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• 제3권2호
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• pp.93-103
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• 1997

This paper develops a new heuristic, the Excursion Algorithm(EA), for constructing optimal designs for the experiments related with marine environment. The proposed EA consists of three parts: 1) construction of an initial feasible solution, 2) excursions over a bounded region, and 3) stopping rules. It is the second part that distinguishes the EA from the other existing heuristic methods. It turns out that excursions over a bounded feasible and/or infeasible region is effective in alleviating the risks of being trapped at a local optimum. Since this problem is formulated for the first time thesis, other heuristic algorithms do not exist. Therefore, global optimal solutions are obtained by complete enumeration for some cases, and the performance of the EA is evaluated in terms of solution quality. Computational results show that the proposed EA is effective in finding good(or, in many cases, global) solutions to the constrained optimal experimental design problems.

• 산업경영시스템학회지
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• 제34권3호
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• pp.90-96
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• 2011

Finding the critical path (or the longest path) on acyclic directed graphs, which is well-known as PERT/CPM, the ambiguity of each acr's length can be modeled as a range or an interval, in which the actual length of arc may realize. In this case, the min-max regret criterion, which is widely used in the decision making under uncertainty, can be applied to find the critical path minimizing the maximum regret in the worst case. Since the min-max regret critical path problem with the interval arc's lengths is known as NP-hard, this paper proposes a heuristic algorithm to diminish the maximum regret. Then the computational experiments shows the proposed algorithm contributes to the improvement of solution compared with the existing heuristic algorithms.

• Smart Structures and Systems
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• 제21권3호
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• pp.321-333
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• 2018

Simplified Dolphin Echolocation (SDE) algorithm is a recently developed meta-heuristic algorithm. This algorithm is an improved and simplified version of the Dolphin Echolocation Optimization (DEO) method, based on the baiting behavior of the dolphins. The main advantage of the SDE algorithm is that it needs no empirical parameter. In this paper, the SDE algorithm is applied for optimization of three well-studied frame structures. The designs are then compared with those of other meta-heuristic methods from the literature. Numerical results show the efficiency of the SDE algorithm and its competitive ability with other well-established meta-heuristics methods.

• 한국통신학회논문지
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• 제16권9호
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• pp.877-882
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• 1991

전문가 제어는 궤환세어 시스템에 규칙기반 전문가 시스템을 도입한 기법이다. 본 논문에서는 직류 서보전동기 속도 제어를 위한 입력 조절, 감독, 동조에 관한 경험적 규치기 제시되었다. 경험적 규칙이 궤횐제어 시스템의 한 요소로서 사용된 전문가 자동동조 PID 제어기는 수치 알고리즘과 경험적 논리로 구성된다. 견실하고 정밀한 제어기능은 컴퓨터 시뮬레이션에 의하여 확인 하였다

• 한국해양공학회지
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• 제17권6호
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• pp.53-57
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• 2003

Optimum design of most structural system requires that design variables are regarded as discrete quantities. This paper presents the use of Genetic Algorithm for determining the optimum design for truss with discrete variables. Genetic Algorithm are know as heuristic search algorithms, and are effective global search methods for discrete optimization. In this paper, Elitism and the method of conferring penalty parameters in the design variables, in order to achieve improved fitness in the reproduction process, is used in the Genetic Algorithm. A 10-Bar plane truss and a 25-Bar space truss are used for discrete optimization. These structures are designed for stress and displacement constraints, but buckling is not considered. In particular, we obtain continuous solution using Genetic Algorithms for a 10-bar truss, compared with other results. The effectiveness of Genetic Algorithms for global optimization is demonstrated through two truss examples.

• 한국경영과학회지
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• 제34권3호
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• pp.125-136
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• 2009

The container packing problem Is one of the traditional optimization problems, which is very related to the knapsack problem and the bin packing problem. In this paper, we deal with the quadratic multiple container picking problem (QMCPP) and it Is known as a NP-hard problem. Thus, It seems to be natural to use a heuristic approach such as evolutionary algorithms for solving the QMCPP. Until now, only a few researchers have studied on this problem and some evolutionary algorithms have been proposed. This paper introduces a new efficient evolutionary algorithm for the QMCPP. The proposed algorithm is devised by improving the original network random key method, which is employed as an encoding method in evolutionary algorithms. And we also propose local search algorithms and incorporate them with the proposed evolutionary algorithm. Finally we compare the proposed algorithm with the previous algorithms and show the proposed algorithm finds the new best results in most of the benchmark instances.

• 한국경영과학회지
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• 제26권2호
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• pp.47-58
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• 2001

The purpose of this study is to present methods for determining the k most vital arcs (k-MVAs) in shortest path problem (SPP) using evolutionary algorithms. The problem of finding the k-MVAs in SPP is to find a set of k arcs whose simultaneous removal from the network causes the greatest increase in the shortest distance between two specified nodes. Generally, the problem of determining the k-MVAs in SPP has been known as NP-hard. Therefore, to deal with problems of the real world, heuristic algorithms are needed. In this study we present three kinds of evolutionary algorithms for finding the k-MVAs in SPP, and then to evaluate the performance of proposed algorithms.

• Advances in Computational Design
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• 제1권1호
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• pp.79-86
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• 2016

In this study, the three different meta-heuristics namely the Grey Wolf Optimizer (GWO), Stochastic Fractal Search (SFS), and Adaptive Differential Evolution with Optional External Archive (JADE) algorithms are examined. This study considers optimization of the planer frame to minimize its weight subjected to the strength and displacement constraints as per the American Institute of Steel and Construction - Load and Resistance Factor Design (AISC-LRFD). The GWO algorithm is associated with grey wolves' activities in the social hierarchy. The SFS algorithm works on the natural phenomenon of growth. JADE on the other hand is a powerful self-adaptive version of a differential evolution algorithm. A one-bay ten-story planar steel frame problem is examined in the present work to investigate the design ability of the proposed algorithms. The frame design is produced by optimizing the W-shaped cross sections of beam and column members as per AISC-LRFD standard steel sections. The results of the algorithms are compared. In addition, these results are also mapped with other state-of-art algorithms.

• 대한산업공학회지
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• 제37권4호
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• pp.408-414
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• 2011

The objective of this paper is to define local optimization algorithms (LOA) to solve Resource-Constrained Project Scheduling Problem (RCPSP) and analyze the performance of these algorithms. By representing solutions with activity list, three primitive LOAs, i.e. forward and backward improvement-based, exchange-based, and relocation-based LOAs are defined. Also, combined LOAs integrating two primitive LOAs are developed. From the experiments with standard test set J120 generated using ProGen, the FBI-based LOA demonstrates to be an efficient algorithm. Moreover, algorithms combined with FBI-based LOA and other LOA generate good solutions in general. Among the considered algorithms, the combined algorithm of FBI-based and exchangebased shows best performance in terms of solution quality and computation time.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2000년도 춘계학술대회논문집A
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• pp.725-730
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• 2000

Genetic algorithms based on the theory of natural selection, have been applied to many different fields, and have proven to be relatively robust means to search for global optimum and handle discontinuous or even discrete data. Genetic algorithms are widely used for unconstrained optimization problems. However, their application to constrained optimization problems remains unsettled. The most prevalent technique for coping with infeasible solutions is to penalize a population member for constraint violation. But, the weighting of a penalty for a particular problem constraint is usually determined in the heuristic way. Therefore this paper proposes, the effective technique for handling constraints, the ranking penalty method and hybrid genetic algorithms. And this paper proposes dynamic mutation tate to maintain the diversity in population. The effectiveness of the proposed algorithm is tested on several test problems and results are discussed.