GA를 이용한 도시계획 변경에 따른 지가 분포의 시뮬레이션

문태헌,박광용 대한국토·도시계획학회 2002 國土計劃 Vol.37 No.1

Analyzing the present and future spatial land price distribution in a city is essential to enhance the understanding of urban spatial structure. However, the neoclassicist's land rent theory considering only accessibility to civic center as a major variable is too simple to reflect the complicated mechanism of land price. Moreover, calibration model is required to deal with several variables and their mutual interactions. Thus, this study introduces Genetic Algorithm(GA) which is a searching algorithm based on genetic mechanism of natural phenomena. The basic idea of GA that a creature that fits itself to the given circumstances survives with higher possibility is thought to be applicable to solve complicated real urban problems, especially nonlinear problem. This study presents analyzing, simulating and forecasting the future land price distribution using Genetic Algorithm. And the change of land price distribution when urban planning would be changed in Jinju City is simulated as a case study. For the spatial calibration, firstly, grid data about land use, road and distance to urban facilities was built on GIS base. And a hypothesis that neighboring lands give impact on each other were set. Next, GA model was constructed and simulated with Delphi 5.0. As a result, GA was proved to be an applicable model to forecast the land price distribution in the city caused by urban planning change.

Parallel Genetic Algorithm-Tabu Search Using PC Cluster System for Optimal Reconfiguration of Distribution Systems

Mun Kyeong-Jun,Lee Hwa-Seok,Park June-Ho The Korean Institute of Electrical Engineers 2005 KIEE International Transactions on Power Engineeri Vol.a5 No.2

This paper presents an application of the parallel Genetic Algorithm-Tabu Search (GA- TS) algorithm, and that is to search for an optimal solution of a reconfiguration in distribution systems. The aim of the reconfiguration of distribution systems is to determine the appropriate switch position to be opened for loss minimization in radial distribution systems, which is a discrete optimization problem. This problem has many constraints and it is very difficult to solve the optimal switch position because of its numerous local minima. This paper develops a parallel GA- TS algorithm for the reconfiguration of distribution systems. In parallel GA-TS, GA operators are executed for each processor. To prevent solution of low fitness from appearing in the next generation, strings below the average fitness are saved in the tabu list. If best fitness of the GA is not changed for several generations, TS operators are executed for the upper 10$\%$ of the population to enhance the local searching capabilities. With migration operation, the best string of each node is transferred to the neighboring node after predetermined iterations are executed. For parallel computing, we developed a PC-cluster system consisting of 8 PCs. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through switch based rapid Ethernet. To demonstrate the usefulness of the proposed method, the developed algorithm was tested and is compared to a distribution system in the reference paper From the simulation results, we can find that the proposed algorithm is efficient and robust for the reconfiguration of distribution system in terms of the solution quality, speedup, efficiency, and computation time.

USING FUZZY LOGIC CONTROLLER AND EVOLUTIONARY GENETIC ALGORITHM FOR AUTOMOTIVE ACTIVE SUSPENSION SYSTEM

J.-S. CHIOU,M.-T. LIU 한국자동차공학회 2009 International journal of automotive technology Vol.10 No.6

This study designs a fuzzy logic controller (FLC) for an active automobile suspension system in which the membership functions and control rules are optimized using a genetic algorithm (GA). The objective of the FLC is to strike an optimal balance between the ride comfort and the vehicle stability. The values of the crossover and mutation parameters in the GA are adapted dynamically during the convergence procedure using a fuzzy control scheme. The convergence state of the GA is determined by using a support vector machine (SVM) method to identify the variation in each of the genes of the best-fit GA chromosome following each iteration loop. The feasibility of the proposed GA-assisted FLC scheme is verified by performing a series of numerical simulations in which the characteristics of the controlled plant are compared with those observed in a passive suspension system and obtained under an optimal linear feedback controller. The results demonstrate that the GA-assisted FLC results in a lower suspension deflection, a reduced sprung mass acceleration and a lower bouncing distance between the tire and the ground. This study designs a fuzzy logic controller (FLC) for an active automobile suspension system in which the membership functions and control rules are optimized using a genetic algorithm (GA). The objective of the FLC is to strike an optimal balance between the ride comfort and the vehicle stability. The values of the crossover and mutation parameters in the GA are adapted dynamically during the convergence procedure using a fuzzy control scheme. The convergence state of the GA is determined by using a support vector machine (SVM) method to identify the variation in each of the genes of the best-fit GA chromosome following each iteration loop. The feasibility of the proposed GA-assisted FLC scheme is verified by performing a series of numerical simulations in which the characteristics of the controlled plant are compared with those observed in a passive suspension system and obtained under an optimal linear feedback controller. The results demonstrate that the GA-assisted FLC results in a lower suspension deflection, a reduced sprung mass acceleration and a lower bouncing distance between the tire and the ground.

Optimization Design Based On Self-Adapted Ant Colony and Genetic Mix Algorithm for Parameters of PID Controller

Wang Xiao-Yu 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.7

This paper presents a method of optimized PID parameter self-adapted ant colony algorithm with aberrance gene, based on ant colony algorithm. This method overcomes genetic algorithm’s defects of repeated iteration, slower solving efficiency, ordinary ant colony algorithm’s defects of slow convergence speed, easy to get stagnate, and low ability of full search. For a given system, the results of simulation experiments which compare to the result of Z-N optimization and evolution of genetic algorithm optimization and evolution of ant colony system optimization, it has more excellent performance in finding best solution and convergence, the PID parameters also have optimality, system possesses dynamic controlling and performance. The experiments show that this method has its practical value on controlling other objection and process.

재능 유전인자를 갖는 네스티드 유전자 알고리듬을 이용한 새로운 다중 초점 이미지 융합 기범

박대철,론넬 아톨레 한국인터넷방송통신학회 2009 한국인터넷방송통신학회 논문지 Vol.9 No.1

본 논문에서 이미지 선명도 함수의 최적화에 의해 융합 법칙이 유도되는 새로운 이미지 융합 접근법을 제안한다. 선명도 함수에 비교하여 소스 이미지로부터 최적 블록을 통계적으로 선택하기 위하여 유전자 알고리듬이 사용되었다. 변이 연산에 의해 만들어진 유전인자들의 포격을 통해서 찾아진 재능 유전 인자를 갖는 새로운 네스티드 유전자 알고리듬을 설계하였고 구현하였다. 알고리듬의 수렴은 해석적으로, 실험적으로 그리고 통계적으로 3개의 테스트 함수를 사용하여 표준 GA와 비교하였다. 결과의 GA는 변수와 집단 크기에 불변이며, 최소 20 개체이면 시험에 충분하다는 것을 알 수 있었다. 융합 응용에서 모집단내의 각 개체는 입력 블록을 나타내는 유한한 이산 값을 갖는 개체이다. 이미지 융합 실험에 제안한 기법의 성능은 출력 품질 척도로 상호 정보량(MI)으로 특징지워진다. 제안한 방법은 C=2 입력 이미지에 대해 테스트되었다. 제안한 방법의 실험 결과는 현재의 다중 초점 이미지 융합 기법에 대한 실제적이고 매력적인 대안이 됨을 보여준다.

정량적, 정성적 문제가 혼재한 건축디자인 문제에서 IGA와 GA를 이용한 최적안 선정방법에 관한 연구

진경일 대한건축학회지회연합회 2005 대한건축학회연합논문집 Vol.7 No.3

A genetic algorithm with the concept of viral infections to solve hard constraints in workflow scheduling

Jeremie Sublime,Sonia Yassa,Geun-Sik Jo 한국지능정보시스템학회 2012 한국지능정보시스템학회 학술대회논문집 Vol.2012 No.12

Cloud computing is an emerging technology that allows users to utilize on-demand computation, storage, data and services from around the world. The main contribution of this work is to propose a new multi-objective genetic algorithm coupled with the viral infection capable of handling hard constraints, such as restrictions on task scheduling, which are not handled by current algorithms. Furthermore, our algorithm can optimize any number of parameters such as execution time, cost, reliability, and availability; In addition, it can handle restrictions such as deadlines and requirements on the different variables. Using data of the Amazon EC2 cloud resources and workflows from London e-Science Center, we have been investigating the problem of scheduling workflow applications and have compared our algorithm with other scheduling algorithms. Our experimentations have shown the efficiency of our algorithm and have confirmed that the viral infection operator is a powerful tool when solving hard constraints.

복수물류센터에 대한 VRP 및 GA-TSP의 개선모델개발

이상철(Lee, Sang-Cheol),류정철(Yu, Jeong-Cheol) 한국산학기술학회 2007 한국산학기술학회논문지 Vol.8 No.5

시간제한을 가지는 차량경로문제는 배송 및 물류에서 가장 중요한 문제 중의 하나이다. 실제적으로 고객의 서비스를 위하여 주어진 시간 안에 출발해서 배송을 끝마쳐야 한다. 본 연구는 복수 물류센터의 최적차량경로문제를 위하여 유전자 알고리즘을 이용한 2단계 접근방법을 사용한 VRP(Vehicle Routing Problem)모델의 개발이다. 1단계로 구역별로 Clustering한 것은 복수 물류센터의 문제를 쉽게 해결하기 위해 단일 물류센터의 문제로 전환하여 모델을 개발하였다. 2단계로 시간제한을 가지는 최적차량경로를 찾을 수 있는 개선된 유전자 알고리즘을 이용하여 GA-TSP(Genetic Algorithm-Traveling Salesman Problem)모델을 개발하였다. 따라서 본 연구에서 개발한 Network VRP 는 ActiveX와 분산객체기술을 이용한 VRP문제의 해를 구하기 위한 전산프로그램을 개발한다. A vehicle routing problem with time constraint is one of the must important problem in distribution and logistics. In practice, the service for a customer must start and finish within a given delivery time. This study is concerned about the development of a model to optimize vehicle routing problem under the multi-logistics center problem. And we used a two-step approach with an improved genetic algorithm. In step one, a sector clustering model is developed by transfer the multi-logistics center problem to a single logistics center problem which is more easy to be solved. In step two, we developed a GA-TSP model with an improved genetic algorithm which can search a optimize vehicle routing with given time constraints. As a result, we developed a Network VRP computer programs according to the proposed solution VRP used ActiveX and distributed object technology.

Genetic Algorithm과 Expert System의 결합 알고리즘을 이용한 직구동형 풍력발전기 최적설계

김상훈(Shang-Hoon Kim),정상용(Sang-Yong Jung) 한국조명·전기설비학회 2010 조명·전기설비학회논문지 Vol.24 No.10

In this paper, the optimal design of a wind generator, implemented with the hybridized GA(Genetic Algorithm) and ES(Expert System), has been performed to maximize the AEP(Annual Energy Production) over the whole wind speed characterized by the statistical model of wind speed distribution. In particular, to solve the problem of calculation iterate, ES finds the superior individual and apply to initial generation of GA and it makes reduction of search domain. Meanwhile, for effective searching in reduced search domain, it propose Intelligent GA algorithm. Also, it shows the results of optimized model 500[㎾] wind generator using hybridized algorithm and benchmark result of compare with GA.

GA-FSMC를 이용한 이중탱크의 정밀한 수위 제어

박현철,권용범,이준탁 동아대학교 생산기술연구소 2002 生産技術硏究所硏究論文集 Vol.7 No.2

Even though, tanks are used at the many industry plants, it is very difficult to control precisely the tank level without any overflow and shortage; moreover, because of its complicatedynamics and nonlinearity, it's impossible to realize the accurate control using the mathematical model, which can be applied to the various operation modes.this paper, Genetic Algorithm based Fuzzy Sliding Mode Controller (GA-FSMC) for the precise control of the double tank level was proposed. The sliding mode controller (SMC) is known as having the robust variable structures for the nonlinear control systems with the parametric perturbations and with the sudden disturbances. It's difficult to find SMC's parameters, and SMC brings the chattering that injures actuator and increases error. Genetic Algorithm and Fuzzy logic are adapted to find SMC's parameters and reduce the chattering. The simulation results are shown that the tank level could be satisfactorily controlled with less overshoot and steady-state error by the Proposed GA-FSMC.