• 한국통신학회논문지
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• 제33권11A호
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• pp.1095-1104
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• 2008

다중 사용자 OFDM 시스템에서 제한된 자원을 효율적으로 사용하기 위해서는 부반송파와 비트의 할당은 중요한 역할을 한다. 하지만 부반송파와 비트의 할당문제는 비선형적 문제로 모든 경우의 수를 계산하여 최적의 값을 얻기에는 사실상 불가능하다. 본 논문에서는 비선형적 문제의 효율적인 자원 활용을 위해서 새로운 유전자 알고리즘을 사용하였다. 논문에서 제안된 알고리즘은 기존의 정형화된 유전자 알고리즘보다 다양한 조합을 참고하여 해를 찾게 된다. 따라서 수치적 시뮬레이션 결과들을 통해서 기존의 알고리즘들과 제안된 알고리즘을 비교해 볼 때, 제안한 알고리즘이 기존의 알고리즘들보다 뛰어난 성능을 보임을 확인하였다.

• 한국지능시스템학회논문지
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• 제12권1호
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• pp.61-66
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• 2002

본 논문에서는 Flexible Manipulator의 제어를 위해 퍼지제어의 제약인 멤버쉽 함수, 퍼지규clr을 유전알고리즘으로 조정, 최적화 하는 새로운 제어기를 설계하였다. 사용된 유전알고리즘은 Steady State Genetic 알고리즘과 Adaptive 유전 알고리즘의 합성이다. 제안한 제어기는 Flexible Manipulator의 끝점 무게 0.8kmg, 최대속도 1m/s의 경우, 퍼지제어에 비해 오차가 90.8% 감소하고 신경회로망을 이용한 퍼지제어에 비하여는 31.8% 감소하였으며 진화전략과 퍼지제어합성에 의한 제어기보다는 오차가 31.3% 감소하는 통 제어성능과 그 유용성이 우수함을 확인하였다.

• International Journal of Computer Science & Network Security
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• 제24권3호
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• pp.12-22
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• 2024

The travelling salesman problem is very famous and very difficult combinatorial optimization problem that has several applications in operations research, computer science and industrial engineering. As the problem is difficult, finding its optimal solution is computationally very difficult. Thus, several researchers have developed heuristic/metaheuristic algorithms for finding heuristic solutions to the problem instances. In this present study, a new hybrid genetic algorithm (HGA) is suggested to find heuristic solution to the problem. In our HGA we used comprehensive sequential constructive crossover, adaptive mutation, 2-opt search and a new local search algorithm along with a replacement method, then executed our HGA on some standard TSPLIB problem instances, and finally, we compared our HGA with simple genetic algorithm and an existing state-of-the-art method. The experimental studies show the effectiveness of our proposed HGA for the problem.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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• pp.2714-2716
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• 2005

In this paper, we designed the adaptive fuzzy logic controller for crane system using neural network and real-coding genetic algorithm. The proposed algorithm show a good performance on convergence velocity and diversity of population among evolutionary computations. The weights of neural network is adaptively changed to tune the input/output gain of fuzzy logic controller. And the genetic algorithm was used to leam the feedforward neural network. As a result of computer simulation, the proposed adaptive fuzzy logic controller is superior to conventional controllers in moving and modifying the destination point.

• Journal of Computing Science and Engineering
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• 제8권4호
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• pp.199-206
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• 2014

The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.

• 한국전자파학회논문지
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• 제26권8호
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• pp.758-764
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• 2015

본 논문에서는 2차 함수 곡선의 회전체형 도체 곡면 위에 있는 컨포멀 배열 안테나의 패턴을 EAGA(Enhanced Adaptive Genetic Algorithm)를 이용하여 합성한 내용을 보이고 있다. 다양한 곡면의 회전체 도체를 고찰하기 위해 2차 함수의 계수를 바꿔 세 가지 유형의 회전체형 곡면을 형성시켰고, 각 유형의 컨포멀 배열 안테나 패턴을 합성하였다. 패턴 합성에 소요되는 시간의 단축을 위해 3차원 컨포멀 배열 안테나의 능동 소자 패턴 대신에 2차원 평면 배열 안테나의 능동 소자 패턴을 구한 후, 이를 오일러 변환(Euler transform)시켜 이용하였다. EAGA를 이용하여 합성된 패턴의 검증을 위해 MWS(Microwave Studio)를 통해 구한 패턴과 비교하였으며, 두 패턴은 전반적으로 유사하였다.

• Industrial Engineering and Management Systems
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• 제10권4호
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• pp.247-254
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• 2011

In this paper, we study a single-machine scheduling problem with deteriorating processing time of jobs and multiple rate-modifying activities which reset deteriorated processing time to the original processing time. In this situation, the objective function is to minimize total completion time. First, we formulate an integer programming model. Since the model is difficult to solve as the size of real problem being very large, we design an improved genetic algorithm called adaptive genetic algorithm (AGA) with spontaneously adjusting crossover and mutation rate depending upon the status of current population. Finally, we conduct some computational experiments to evaluate the performance of AGA with the conventional GAs with various combinations of crossover and mutation rates.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 B
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• pp.530-532
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• 1999

In adaptive fuzzy control, system designer develops an adaptive law for the output of the unknown plant to track a given signal. The adaptation gains of the adaptive law are critical elements in the overall system, however, they were used to be selected by the designer's experience or intuition. In this paper, genetic algorithm is used to search an optimal adaptation gain and simulation results will be presented to show the improved tracking responses.

• 전자공학회논문지S
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• 제34S권8호
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• pp.92-103
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• 1997

This paper proposes a new gray-level image segmentation method using GA(genetic algorithm) and an ACA(adaptive clustering algorithm). The solution in the general GA can be moving because of stochastic reinsertion, and suffer from the premature convergence problem owing to deficiency of individuals before finding the optimal solution. To cope with these problems and to reduce processing time, we propose the new GBR algorithm and the technique that resolves the premature convergence problem. GBR selects the individual in the child pool that has the fitness value superior to that of the individual in the parents pool. We resolvethe premature convergence problem with producing the mutation in the parents population, and propose the new method that removes the small regions in the segmented results. The experimental results show that the proposed segmentation algorithm gives better perfodrmance than the ACA ones in Gaussian noise environments.

• International Journal of Contents
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• 제2권1호
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• pp.39-44
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• 2006

Load sharing is a critical resource in computer system. In sender-initiated load sharing algorithms, the sender continues to send unnecessary request messages for load transfer until a receiver is found while the system load is heavy. Meanwhile, in the receiver initiated load sharing algorithms, the receiver continues to send an unnecessary request message for load acquisition until a sender is found while the system load is light. These unnecessary request messages result in inefficient communications, low CPU utilization, and low system throughput in distributed systems. To solve these problems, we propose a genetic algorithm based approach for improved sender-initiated and receiver-initiated load sharing in distributed systems. And we expand this algorithm to an adaptive load sharing algorithm. Compared with the conventional sender-initiated and receiver-initiated algorithms, the proposed algorithm decreases the response time and task processing time.