• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.19 no.1 s.71
• /
• pp.27-37
• /
• 2006

A Metropolis genetic algorithm (MGA) is developed and applied for the structural design optimization. In MGA, favorable features of Metropolis criterion of simulated annealing (SA) are incorporated in the reproduction operations of simple genetic algorithm (SGA). This way, the MGA maintains the wide varieties of individuals and preserves the potential genetic information of early generations. Consequently, the proposed MGA alleviates the disadvantages of premature convergence to a local optimum in SGA and time consuming computation for the precise global optimum in SA. Performances and applicability of MGA are compared with those of conventional algorithms such as Holland's SGA, Krishnakumar's micro GA, and Kirkpatrick's SA. Typical numerical examples are used to evaluate the computational performances, the favorable features and applicability of MGA. The effects of population sizes and maximum generations are also evaluated for the performance reliability and robustness of MGA. From the theoretical evaluation and numerical experience, it is concluded that the proposed MGA Is a reliable and efficient tool for structural design optimization.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2009.05a
• /
• pp.292-296
• /
• 2009

우리나라의 용수공급체계는 주로 여름에 집중된 강우를 댐이나 저수지에 저류하였다가 연중고르게 사용자들에게 공급하는 방법을 취하고 있다. 이때 도시지역의 생활용수는 갈수기에도 가장먼저 공급되어져야하는 우선순위를 가지는 반면, 농업용수는 가장 우선적으로 그 사용을 제한받는다. 하지만, 도시화로 인해 도시의 하수처리장 배출수량은 지속적으로 증가하고 있으며, 그 처리수의 대부분은 하천으로 방류되어 유실되고 있다. 방류수 수질기준에 맞추어 방류되는 처리수는 대량방류로 인해 자주 하천에 수질문제를 야기하기도 한다. 그러나 실제로 하수처리장의 처리수는 갈수기에 매우 유용하게 사용될 수 있는 수원 중 하나이다. 그러므로 그 처리수의 재이용에 관한 연구를 적절히 수행한다면, 갈수기에도 농업용수사용의 제한을 최소화할 수 있을 것이다. 그러므로 본 연구에서는 처리수 재이용 모형을 혼합정수계획법으로 개발하고, 하수처리장 처리수의 농업용수나 공공용수로의 사용을 제안하였다. 개발된 처리수 재이용 모형에서는 하천으로 방류되던 처리수를 농업용수나 공공용수로 재사용하는 시스템을 구축한다는 가정 하에, 그 공사비를 최소화하는 것을 목적으로 하였다. 또한 관말의 최소유지압력 등 수리학적인 제약조건을 만족시키도록 설계하였으며, 이 때문에 야기되는 비선형성을 선형화하여 계산시간이 짧은 동시에 전역해에 가까운 해를 찾을 수 있도록 하였다. 개발된 모형은 가상의 처리수 재이용 모형에 적용되어 시스템의 설계자에게 갈수기 용수공급의 최적 대안을 제시해 줄 수 있는 가능성을 보였다.

• The Journal of Information Technology
• /
• v.5 no.4
• /
• pp.73-79
• /
• 2002

In this paper, I will propose the Niche-Meta Genetic Algorithm that has a multi-mutation operator for design of fuzzy controller. The gene in the proposed algorithm is formed by several parameters that represent the crossover rate, mutation rate and input-output membership functions. The optimization of fuzzy membership function is performed with local search on sub-population and the optimal structure is constructed with global search on total-population. The multi-mutation is selected under basis of the result of local evolution. A simulation for 2 D.O.F wheeled-mobile robot is showed to prove the efficiency of the proposed algorithm

• 한국전산유체공학회:학술대회논문집
• /
• 2009.04a
• /
• pp.57-65
• /
• 2009

An efficient and high-fidelity design approach for wing-body shape optimization is presented. Depending on the size of design space and the number of design of variable, aerodynamic shape optimization process is carried out via different optimization strategies at each design stage. In the first stage, global optimization techniques are applied to planform design with a few geometric design variables. In the second stage, local optimization techniques are used for wing surface design with a lot of design variables to maintain a sufficient design space with a high DOF (Degree of Freedom) geometric change. For global optimization, Kriging method in conjunction with Genetic Algorithm (GA) is used. Asearching algorithm of EI (Expected Improvement) points is introduced to enhance the quality of global optimization for the wing-planform design. For local optimization, a discrete adjoint method is adopted. By the successive combination of global and local optimization techniques, drag minimization is performed for a multi-body aircraft configuration while maintaining the baseline lift and the wing weight at the same time. Through the design process, performances of the test models are remarkably improved in comparison with the single stage design approach. The performance of the proposed design framework including wing planform design variables can be efficiently evaluated by the drag decomposition method, which can examine the improvement of various drag components, such as induced drag, wave drag, viscous drag and profile drag.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.32 no.6A
• /
• pp.369-378
• /
• 2012

In this study, direct-search based optimization methods are applied for blade shape optimization of wind turbines and the optimization performances of several methods including conventional genetic algorithm, micro genetic algorithm and pattern search method are compared to propose a more efficient method. For this purpose, the currently available version of HARP_Opt (Horizontal Axis Rotor Performance Optimizer) code is enhanced to rationally evaluate the annual energy production value according to control strategies and to optimize the blade shape using pattern search method as well as genetic algorithm. The enhanced HARP_Opt code is applied to obtain the optimal turbine blade shape for 1MW class wind turbines. The results from pattern search method are compared with the results from conventional genetic algorithm and also micro genetic algorithm and it is found that the pattern search method has a better performance in achieving higher annual energy production and consistent optimal shapes and the micro genetic algorithm is better for reducing the calculation time.

• Journal of Korea Society of Industrial Information Systems
• /
• v.27 no.1
• /
• pp.37-48
• /
• 2022

In this study, a hybrid meta-heuristic approach is proposed for solving engineering design optimization problems. Various approaches in many literatures have been proposed to solve engineering optimization problems with various types of decision variables and complex constraints. Unfortunately, however, their efficiencies for locating optimal solution do not be highly improved. Therefore, we propose a hybrid meta-heuristic approach for improving their weaknesses. the proposed GA-VNS-HC approach is combining genetic algorithm (GA) for global search with variable neighborhood search (VNS) and hill climbing (HC) for local search. In case study, various types of engineering design optimization problems are used for proving the efficiency of the proposed GA-VNS-HC approach

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.1
• /
• pp.29-37
• /
• 1999

In general, the information processing capability of a neural network is determined by its architecture and efficient training patterns. However, there is no systematic method for designing neural network and selecting effective training patterns. Evolutionary Algorithms(EAs) are referred to as the methods of population-based optimization. Therefore, EAs are considered as very efficient methods of optimal system design because they can provide much opportunity for obtaining the global optimal solution. In this paper, we propose a new method for finding the optimal structure of neural networks based on competitive co-evolution, which has two different populations. Each population is called the primary population and the secondary population respectively. The former is composed of the architecture of neural network and the latter is composed of training patterns. These two populations co-evolve competitively each other, that is, the training patterns will evolve to become more difficult for learning of neural networks and the architecture of neural networks will evolve to learn this patterns. This method prevents the system from the limitation of the performance by random design of neural networks and inadequate selection of training patterns. In co-evolutionary method, it is difficult to monitor the progress of co-evolution because the fitness of individuals varies dynamically. So, we also introduce the measurement method. The validity and effectiveness of the proposed method are inspected by applying it to the visual servoing of robot manipulators.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.16 no.6 s.111
• /
• pp.665-673
• /
• 2006

This paper proposes a RSM-based hybrid evolutionary Algorithm (RHEA) which combines the merits of the popular programs such as genetic algorithm (GA), tabu search method and response surface methodology (RSM). This algorithm, for improving the convergent speed that is thought to be the demerit of genetic algorithm, uses response surface methodology and simplex method. The mutation of GA offers random variety to finding the optimum solution. In this study, however, systematic variety can be secured through the use of tabu list. Efficiency of this method has been proven by applying traditional left functions and comparing the results to GA. It was also proved that the newly suggested algorithm is very effective to find the global optimum solution to minimize the weight for avoiding the resonance of fresh water tank that is placed in the after body area of ship. According to the study, GA's convergent speed in initial stages is improved by using RSM method. An optimized solution is calculated without the evaluation of additional actual objective function. In a summary, it is concluded that RHEA is a very powerful global optimization algorithm from the view point of convergent speed and global search ability.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.11
• /
• pp.166-174
• /
• 2016

In this paper, we developed a mixed-integer linear programming model for transportation planning of athletes in sports events. The transportation planning of athletes involves finding the lowest-cost and fastest-time plan for distributing athletes from multiple accommodation to stadium. The decision variables associate with the number of driving events, and the total transportation cost is the objective function that needs to be minimized. The proposed method uses mixed integer linear programming to solve transportation problem, thus the global optimality is guaranteed. In order to verify the effectiveness of proposed method, we performed simulation and built the sports event management service platform (SEMSP) for transportation planning.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.24 no.6 s.177
• /
• pp.1529-1539
• /
• 2000

A newly developed optimization method which uses the genetic algorithm combined with the sensitivity analysis is presented in this paper. The genetic algorithm is a probabilistic method, searching the optimum at several points simultaneously, requiring only the values of the object and constraint functions. It has therefore more chances to find global solution and can be applied various problems. Nevertheless, it has such shortcomings that even it approaches the optimum rapidly in the early stage, it slows down afterward and it can't consider the constraints explicitly. It is only because it can't search the local area near the current points. The traditional method, on the other hand, using sensitivity analysis is of great advantage in searching the near optimum. Thus the combination of the two techniques makes use of the individual advantages, that is, the superiority both in global searching by the genetic algorithm and in local searching by the sensitivity analysis. Application of the method to the several test functions verifies that the method suggested is very efficient and powerful to find the global solutions, and that the constraints can be considered properly.