• 한국공간구조학회:학술대회논문집
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• 한국공간구조학회 2005년도 춘계학술발표회 및 정기총회 2권1호(통권2호)
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• pp.246-253
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• 2005

The design of structural engineering optimization is to minimize the cost. This problem has many objective functions formulating section and shape as a function of the included discrete variables. simulated annealing, genetic algerian and TABU algorithm are searching methods for optimum values. The object of this reserch is comparing the result of TABU algorithm, and verifying the efficiency of TABU algorithm in structural optimization design field. For the purpose, this study used a solid truss of 25 elements having 10 nodes, and size optimization for each constraint and load condition of Geodesic one, and shape optimization of Cable Dome for verifying spatial structures by the application of TABU algorithm

• Steel and Composite Structures
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• 제19권4호
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• pp.1035-1053
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• 2015

A genetic algorithm-based minimum weight design method is presented for steel frames containing composite beams, semi-rigid connections and column bases. Genetic Algorithms carry out optimum steel frames by selecting suitable profile sections from a specified list including 128 W sections taken from American Institute of Steel Construction (AISC). The displacement and stress constraints obeying AISC Allowable Stress Design (ASD) specification and geometric (size) constraints are incorporated in the optimization process. Optimum designs of three different plane frames with semi-rigid beam-to-column and column-to-base plate connections are carried out first without considering concrete slab effects on floor beams in finite element analyses. The same optimization procedures are then repeated for the case of frames with composite beams. A program is coded in MATLAB for all optimization procedures. Results obtained from the examples show the applicability and robustness of the method. Moreover, it is proved that consideration of the contribution of concrete on the behavior of the floor beams enables a lighter and more economical design for steel frames with semi-rigid connections and column bases.

• 제어로봇시스템학회논문지
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• 제6권4호
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• pp.268-275
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• 2000

The increasing technological demands of today call for complex systems, which in turn involve a series of optimization problems with some equality or inequality constraints. In this paper, we presents a real-coded genetic algorithm(RCGA) as an optimization tool which is implemented by three genetic operators based on real coding representation. Through a lot of simulation works, the optimum settings of its control parameters are obtained on the basis of global off-line robustness for use in off-line applications. Two optimization problems are Presented to illustrate the usefulness of the RCGA. In case of a constrained problem, a penalty strategy is incorporated to transform the constrained problem into an unconstrained problem by penalizing infeasible solutions.

• KSBB Journal
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• 제14권4호
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• pp.495-502
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• 1999

The optimization of fed-batch yeast fermentation process has been performed using genetic algorithm(GA). Three strategies were designed and applied to obtain the optimal feed rate profiles. Genes in the chromosome (input variables for optimization) included feed rates on fixed time intervals (strategy I), or swiching times $t_s1\;and\;t_s2$, and feed rates on singular arc (strategy II), or feed rates and the length of time interval (strategy III). Strategy III showed the best results for all initial conditions due to efficient utilization of genetic information. Simulation results using GA showed similar or better performance compared with previous results by variational caculus and singular control approach.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 추계학술대회논문집
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• pp.587-592
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• 2000

The optimization method based on an enhanced genetic algorithms is proposed for multimodal function optimization in this paper. This method is consisted of two main steps. The first step is global search step using the genetic algorithm(GA) and function assurance criterion(FAC). The belonging of an population to initial solution group is decided according to the FAC. The second step is to decide the similarity between individuals, and to research the optimum solutions by simplex method in reconstructive search space. Two numerical examples are also presented in this paper to comparing with conventional methods.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.131-138
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• 2022

A Mobile Ad-hoc Network (MANET) is a collection of moving nodes that communicate and collaborate without relying on a pre-existing infrastructure. In this type of network, nodes can freely move in any direction. Routing in this sort of network has always been problematic because of the mobility of nodes. Most existing protocols use simple routing algorithms and criteria, while another important criterion is path selection. The existing protocols should be optimized to resolve these deficiencies. 'Particle Swarm Optimization (PSO)' is an influenced method as it resembles the social behavior of a flock of birds. Genetic algorithms (GA) are search algorithms that use natural selection and genetic principles. This paper applies these optimization models to the OLSR routing protocol and compares their performances across different metrics and varying node sizes. The experimental analysis shows that the Genetic Algorithm is better compared to PSO. The comparison was carried out with the help of the simulation tool NS2, NAM (Network Animator), and xgraph, which was used to create the graphs from the trace files.

• 한국강구조학회 논문집
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• 제13권6호
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• pp.673-681
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• 2001

본 연구에서는 다설계 변수와 다제약 조건으로 구성된 단면, 형상 및 위상을 동시에 고려하는 구조물의 이산화 최적설계문제를 유전자알고리즘을 이용하여 체계화하였다. 본 연구에서는 유전자알고리즘의 적용방법을 초기화절차, 진화적 절차 그리고 유전적 절차로 구성하였다. 초기화절차에서는 한 세대의 개체 수만큼 염색체를 생성하고 진화적 절차는 구조해석의 결과를 분석하여 적합도를 계산하였다. 그리고 유전적 절차는 번식과 교배 및 돌연변이를 통하여 다음세대의 유전자를 생성하게된다. 이렇게 진화적 절차와 유전적 절차를 반복 수행하여 최적 해를 탐색한다. 본 연구에서는 설계자가 궁극적 목표로 하는 구조물의 응력 해석과 단면, 형상 및 위상최적설계를 동시에 수행할 수 있는 이산화 최적설계프로그램을 개발하고, 설계 예를 들어 비교 고찰하였다.

• Industrial Engineering and Management Systems
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• 제10권1호
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• pp.7-14
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• 2011

In solving the Job Shop Scheduling Problem, the best solution rarely is completely random; it follows one or more rules (heuristics). The Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing, and Tabu search, which belong to the Evolutionary Computations Algorithms (ECs), are not efficient enough in solving this problem as they neglect all conventional heuristics and hence they need to be hybridized with different heuristics. In this paper a new algorithm titled "Shaking Optimization Algorithm" is proposed that follows the common methodology of the Evolutionary Computations while utilizing different heuristics during the evolution process of the solution. The results show that the proposed algorithm outperforms the GA, PSO, SA, and TS algorithms, while being a good competitor to some other hybridized techniques in solving a selected number of benchmark Job Shop Scheduling problems.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.255-255
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• 2000

Technical advance of electrical chart and cruising equipment make it possible to sail without a man. It is important to decide the cruising route in view of effectiveness and stability of a ship. So we need to study on the optimal trajectory planning. Genetic algorithm is a strong optimization algorithm with adaptational random search. It is a good choice to apply genetic algorithm to the trajectory planning of a ship. We modify a genetic algorithm to solve this problem. The effectiveness of the revised genetic algorithm is assured through computer simulations.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
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• pp.241-244
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• 2000

The optimization method based on an enhanced genetic algorithms is proposed for multimodal function optimization in this paper This method is consisted of two main steps. The first step is global search step using the genetic algorithm(GA) and function assurance criterion(FAC). The belonging of an population to initial solution group is decided according to the FAC. The second step is to decide resemblance between individuals and research optimum solutions by single point method in reconstructive research space. Two numerical examples are also presented in this paper to comparing with conventional methods.