• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2000.11a
• /
• pp.683-686
• /
• 2000

A new approach to the task of aligning a robot using camera is presented in this paper. We apply an elitist GA to find the joints angles of manipulator to reach target position instead of using nonlinear least error method. Since it employs parallel search and have good performance in solving optimization problems. In order to improve convergence speed, the floating coding method and geometry constraint conditions are used. Experiments are carried out to exhibit the effectiveness of vision-based control using elitist genetic algorithm.

• Proceedings of the Korean Information Science Society Conference
• /
• 2008.06c
• /
• pp.365-370
• /
• 2008

본 논문에서는 개미 집단 최적화(Ant Colony Optimization, ACO)의 시간적 공간적 효율성을 향상시키기 위해 ACO에 엘리트 콤팩트 유전 알고리즘(Elitist compact Genetic Algorithms, elitist cGAs)의 아이디어를 적용한 콤팩트 개미 최적화(Compact elitist Ant Optimization, CAO)를 제안한다. CAO는 elitist cGAs에서 각 세대마다 염색체의 수를 둘로 고정하고 우월한 염색체를 유지하여 최적의 해를 찾는 방식을 적용하여 개미의 수를 하나로 고정하고 전이 확률식과 페로몬 갱신 규칙을 변형하고 특정 문제에 적용할 수 있는 타부 규칙을 추가한 알고리즘이다. 이 알고리즘의 공간 효율성이 ACO보다 좋다는 것을 증명하고 스테이너 트리 문제(Steiner Tree Problem)에 적용하여 제안된 알고리즘의 시간 효율성이 ACO보다 좋다는 것을 보인다.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.15 no.2
• /
• pp.305-313
• /
• 2002

The genetic algorithm(GA), an optimization technique based on the theory of natural selection, has proven to be relatively robust means to search for global optimum. It is converged near to the global optimum point without auxiliary information such as differentiation of function. When studying some optimization problems with continuous variables, it was found that premature saturation was reached that is no further improvement in the object function could be found over a set of iterations. Also, the general GA oscillates in the region of the new global optimum point so that the speed of convergence is decreased. This paper is to propose the concept of restarting and elitist preserving strategy as a measure to overcome this difficulty. Some benchmark examples are studied involving 3-bar truss and cantilever beam with plane stress elements. The modifications to GA improve the speed of convergence.

• Journal of the Korean Society for Precision Engineering
• /
• v.19 no.1
• /
• pp.119-126
• /
• 2002

In this paper, we present a new approach based on an elitist genetic algorithm for the task of aligning the position of a robot gripper using CCD cameras. The vision-based control scheme for the task of aligning the gripper with the desired position is implemented by image information. The relationship between the camera space location and the robot joint coordinates is estimated using a camera-space parameter modal that generalizes known manipulator kinematics to accommodate unknown relative camera position and orientation. To find the joint angles of a robot manipulator for reaching the target position in the image space, we apply an elitist genetic algorithm instead of a nonlinear least square error method. Since GA employs parallel search, it has good performance in solving optimization problems. In order to improve convergence speed, the real coding method and geometry constraint conditions are used. Experiments are carried out to exhibit the effectiveness of vision-based control using an elitist genetic algorithm with a real coding method.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.39 no.4
• /
• pp.10-18
• /
• 2002

Network topology design is a multiobjective problem with various design components. The components such as cost, message delay and reliability are important to gain the best performance. Recently, Genetic Algorithms(GAs) have been widely used as an optimization method for real-world problems such as combinatorial optimization, network topology design, and so on. This paper proposed a method of Multi-objective GA for Design of the network topology which is to minimize connection cost and message delay time. A common difficulty in multiobjective optimization is the existence of an objective conflict. We used the prufer number and cluster string for encoding, parato elimination method and niche-formation method for the fitness sharing method, and reformation elitism for the prevention of pre-convergence. From the simulation, the proposed method shows that the better candidates of network architecture can be found.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.4
• /
• pp.959-968
• /
• 1999

several papers have reported that parallel heuristics or hybrid approaches combining several heuristics can get better results. However, the parallelization and hybridization of any search methods on the single CPU type computer need enormous computation time. that case, we need more elegant combination method. For this purpose, we propose Fast Model of Parallel Heuristics(FMPH). FMPH is based on the island model of parallel genetic algorithms and takes local search to the elite solution obtained form each island(sub group). In this paper we introduce how can we adapt FMPH to the job-shop scheduling problem notorious as the most difficult NP-hard problem and report the excellent results of several famous benchmark problems.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.11 no.1
• /
• pp.115-123
• /
• 2011

Distribution system loss minimization re-configuration is 0-1 planning problem, and the number of combinations requiring searches is extremely large when dealing with typical system scales. For this reason, the application of a genetic algorithm (GA) seems a tractive to solve this problem. Although Genetic algorithms are a type of random number search method, they incorporate a multi-point search feature and are therefore superior to one-point search techniques. The incorporate of GAs for solving large combinational problem has received wide attention. Further, parallel searching can be performed and the optimal solution is more easily reach ed. In this paper, for improving GA convergence characteristics in the distribution system loss minimization re-configuration problem, a chromosome "Limited Life" concept is introduced. Briefly, considering the population homogenization and genetic drift problems, natural selection is achieved by providing this new concept, in addition to natural selection by fitness. This is possible because individuals in a population have an age value. Simulation were carried out using a model system to check this method's validity.