• 산업경영시스템학회지
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• 제33권3호
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• pp.30-39
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• 2010

This paper considers a problem of optimizing torch paths to cut stock plates nested with open contours. For each contour, one of the two ending points is to be selected as a starting point of cutting with the other being the exit point. A torch path is composed of a single depot and a series of starting and ending points of contours to be cut. The torch path optimization problem is shown to be formulated as an extended version of the standard travelling salesman problem. To solve the problem, a hybrid genetic algorithm with the local search of torch paths is proposed. The genetic algorithm is tested for hypothetical problems whose optimal solutions are known in advance due to the special structure of them. The computational results show that the algorithm generates very near optimal solutions for most cases of the test problems, which verifies the validity of the algorithms.

• 한국인터넷방송통신학회논문지
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• 제9권1호
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• pp.75-87
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• 2009

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.

• 제어로봇시스템학회논문지
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• 제8권12호
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• pp.1048-1055
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• 2002

The problem of generating torch paths for 2D laser cutting of a stock plate nested with a set of free-formed parts is investigated. The objective is to minimize the total length of the torch path starting from a blown depot, then visiting all the given Parts, and retuning back to the depot. A torch Path consists of the depot and Piercing Points each of which is to be specified for cutting a part. The torch path optimization problem is shown to be formulated as an extended version of the standard travelling salesman problem To solve the problem, a hybrid genetic algorithm is proposed. In order to improve the speed of evolution convergence, the algorithm employs a genetic algorithm for global search and a combination of an optimization technique and a genetic algorithm for local optimization. Traditional genetic operators developed for continuous optimization problems are used to effectively deal with the continuous nature of piercing point positions. Computational results are provided to illustrate the validity of the proposed algorithm.

• 제어로봇시스템학회논문지
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• 제11권9호
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• pp.802-809
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• 2005

A hybrid genetic algorithm is proposed for the problem of generating laser torch paths to cut a stock plate nested with free-formed parts each having a set of sub-parts. In the problem, the total unproductive travel distance of the torch is minimized. The problem is shown to be formulated as a special case of the standard travelling salesman problem. The hybrid genetic algorithm for solving the problem is hierarchically structured: First, it uses a genetic algorithm to find the cutting path f3r the parts and then, based on the obtained cutting path, sequence of sub-parts and their piercing locations are optimally determined by using a combined genetic and heuristic algorithms. This process is repeated until any progress in the total unproductive travel distance is not achieved. Computational results are provided to illustrate the validity of the proposed algorithm.

• ETRI Journal
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• 제35권3호
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• pp.550-553
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• 2013

Due to limited spectrum resources and differences in link loads, network congestion is one of the key issues in cognitive radio wireless mesh networks. In this letter, a congestion avoidance model with power control, channel allocation, and routing under the signal-to-interference-and-noise ratio is presented. As a contribution, a nested optimization scheme combined with a genetic algorithm and linear programming solver is proposed. Extensive simulation results are presented to demonstrate the effectiveness of our algorithm.