• 지능정보연구
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• 제3권2호
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• pp.11-22
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• 1997

본 연구에서는 비선형 최적화 문제를 효율적으로 해결하기 위한 혼합유전알고리즘(Hybrid Genetic Algorthm : HGA)을 개발하였다. HGA는 기존 유전알고리즘의 적용에 있어 문제점으로 지적된 정밀도의 적용문제와 벌금함수의 사용을 배제하였으며 지역적최적점으로 빠르게 수렴하는 기존의 지역적 탐색법과 유전알고리즘 적용이후 수렴된 해 주변에 대한 정밀탐색법을 함께 고려하여 설계하였으며 이를 세가지의 비선형 최적화 문제 적용하여 본 논문에서 개발한 HGA의 유효성을 보였다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권8호
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• pp.1113-1120
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• 2004

이 논문은 특징 선택을 위한 새로운 혼합형 유전 알고리즘을 제안한다. 탐색을 미세 조정하기 위한 지역 연산을 고안하였고, 이들 연산을 유전 알고리즘에 삽입하였다. 연산의 미세 조정 강도를 조절할 수 있는 매개 변수를 설정하였으며, 이 변수에 따른 효과를 측정하였다. 다양한 표준 데이타 집합에 대해 실험한 결과, 제안한 혼합형 유전 알고리즘이 단순 유전 알고리즘과 순차 탐색 알고리즘에 비해 우수함을 확인하였다.

• 경영과학
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• 제17권1호
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• pp.135-144
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• 2000

This paper describes a scheduling problem of the panel block assembly shop in a shipbuilding industry. Because the shipbuilding is a labor intensive industry the most important consideration in a panel block assembly shop is the workload balancing. which balances man-hour weight and welding length and so on. It should be determined assembly schedule and workstation considering a daily load balancing and a workstation load balancing simultaneously. To solve the problem we develop a hybrid genetic algorithm. Hybrid genetic algorithm proposed in this paper consists of two phases. The first phase uses the heuristic method to find a initial feasible solution which provides a useful information about optimal solution. The second phase proposes the genetic algorithm to derive the optimal solution with the initial population consisting of feasible solutions based on the initial solution. Finally we carried out computational experiments for this load balancing problem which indicate that developed method is effective for finding good solutions.

• 한국산업정보학회논문지
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• 제21권4호
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• pp.31-41
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• 2016

본 연구는 폐쇄루프 공급망 모델(Closed-Loop Supply Chain Model)을 이용하여 새로운 형태의 혼합형유전알고리즘(Proposed Hybrid Genetic Algorithm: pro-HGA)접근법의 수행도를 평가하기 위한 논문이다. 제안한 폐쇄루프 공급망 모델은 물류네트워크에서 순방향물류(Forward Logistics)와 역물류(Reverse Logistics)를 함께 고려한 통합형 물류모델이며. 이 모델에서는 회수된 제품의 재사용(Reuse), 재판매(Resale) 및 폐기(Waste Disposal)를 함께 고려하고 있다. 제안된 모델의 이행을 위해 기존연구에서 제안한 유전알고리즘(Genetic Algorithm: GA), 혼합형유전알고리즘(Hybrid Genetic Algorithm: HGA)과 본 연구에서 제안한 pro-HGA를 함께 적용하여 각 접근법들의 우수성을 비교분석하였다. 분석결과 본 연구에서 제안한 pro-HGA가 기존의 GA, HGA보다 더 우수한 결과를 얻었다.

• 대한산업공학회지
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• 제25권3호
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• pp.360-368
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• 1999

We consider the problem of scheduling n jobs with sequence-dependent processing times on a set of parallel-identical machines. The processing time of each job consists of a pure processing time and a sequence-dependent setup time. The objective is to maximize the total remaining machine available time which can be used for other tasks. For the problem, a hybrid genetic algorithm is proposed. The algorithm combines a genetic algorithm for global search and a heuristic for local optimization to improve the speed of evolution convergence. The genetic operators are developed such that parallel machines can be handled in an efficient and effective way. For local optimization, the adjacent pairwise interchange method is used. The proposed hybrid genetic algorithm is compared with two heuristics, the nearest setup time method and the maximum penalty method. Computational results for a series of randomly generated problems demonstrate that the proposed algorithm outperforms the two heuristics.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 추계학술대회논문집A
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• pp.533-538
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• 2001

This paper describes a combinational method to compute the global and local solutions of optimization problems. The present hybrid algorithm uses both a genetic algorithm and a local concentrate search algorithm (e. g simplex method). The hybrid algorithm is not only faster than the standard genetic algorithm but also supplies a more accurate solution. In addition, this algorithm can find the global and local optimum solutions. The present algorithm can be supplied to minimize the resonance response (Q factor) and to yield the critical speeds as far from the operating speed as possible. These factors play very important roles in designing a rotor-bearing system under the dynamic behavior constraint. In the present work, the shaft diameter, the bearing length, and clearance are used as the design variables.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집C
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• pp.554-558
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• 2001

In this study, a hybrid method is used to search the pseudo-optimal solution for the I-dimentional nesting problem. This method is composed of the genetic algorithm for the global search and a simple heuristic one for the local search near the pseudo optimal solution. Several simulation results show that the hybrid method gives very satisfactory results.

• 대한기계학회논문집A
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• 제26권6호
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• pp.1008-1015
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• 2002

This paper proposes a combinatorial method to compute the global and local solutions of optimization problem. The present hybrid algorithm is the synthesis of a genetic algorithm and a local concentrate search algorithm (simplex method). The hybrid algorithm is not only faster than the standard genetic algorithm, but also gives a more accurate solution. In addition, this algorithm can find both the global and local optimum solutions. An optimization result is presented to demonstrate that the proposed approach successfully focuses on the advantages of global and local searches. Three numerical examples are also presented in this paper to compare with conventional methods.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.107.6-107
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• 2001

Genetic alogorithm(GA) is a well-known global optimization algorithm. However, as the searching bounds grow wider., performance of local optimization deteriorates. In this paper, we propose a hybrid algorithm which integrates the gradient algorithm and GA so as to reinforce the performance of local optimization. We apply this algorithm to the system identification of second order RLC circuit. Identification results show that the proposed algorithm gets the better and robust performance to find the exact values of RLC elements.

• Journal of Electrical Engineering and Technology
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• 제6권2호
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• pp.202-207
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• 2011

Numerous methodologies have been developed in an effort to reduce cogging torque. However, most of these methodologies have side effects that limit their applications. One approach is the optimization methodology that determines an optimized design variable within confined conditions. The response surface methodology (RSM) and the genetic algorithm (GA) are powerful instruments for such optimizations and are matters of common interest. However, they have some weaknesses. Generally, the RSM cannot accurately describe an object function, whereas the GA is time consuming. The current paper describes a novel GA and RSM hybrid algorithm that overcomes these limitations. The validity of the proposed algorithm was verified by three test functions. Its application was performed on a surface-mounted permanent magnet.