• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2000년도 추계종합학술대회 논문집(3)
• /
• pp.9-12
• /
• 2000

Genetic algorithm is a searching method which based on the law of the survival of the fittest. Multi-population Genetic Algorithm is a modified form of Genetic Algorithm, which was devised for covering the defect of general genetic algorithm. The core of multi-population genetic algorithm is said to be the migration schemes. The fitness-based migration scheme and the random migration scheme are currently used. In this paper, a new migration scheme, ‘the migration scheme between groups’, is suggested, and compared to the general two migration schemes.

• 한국산학기술학회:학술대회논문집
• /
• 한국산학기술학회 2003년도 Proceeding
• /
• pp.80-83
• /
• 2003

A Genetic algorithm is a searching algorithm that based on the law of the survival of the fittest. Multi-population Genetic Algorithms are a modified form of genetic algorithm. Therefore, experience with fuzzy logic and genetic algorithm has proven to be that a combination of them can efficiently make up for their own deficiency. The Multi-population Genetic Algorithms independently evolve subpopulations. In this paper, we suggest a new coding method that independently evolves subpopulations using the fuzzy logic controller. The fuzzy logic controller has applied two fuzzy logic controllers that are implemented to adaptively adjust the crossover rate and mutation rate during the optimization process. The migration scheme in the multi-population genetic algorithms using fuzzy logic controllers is tested for a function optimization problem, and compared with other group migration schemes, therefore the groups migration scheme is then performed. The results demonstrate that the migration scheme in the multi-population genetic algorithms using fuzzy logic controller has a much better performance.

• 정보처리학회논문지A
• /
• 제9A권4호
• /
• pp.555-562
• /
• 2002

이동 에이전트가 연속적으로 다근 노드로 이주될 때 총 작업수행 시간은 방문해야할 순서에 따라 변경될 수 있다. 효율적인 이주 알고리즘은 초기 상태에서 목적 상태로 수행되기 위해 최소한 수행 시간을 제공하는 수행 순서를 의미하며, 이동 에이전트의 효율적인 이주 알고리즘을 통해 개발자가 원하는 작업의 총 수행 시간을 최소화시킬 수 있다. 기존의 대표적인 이동 에이전트인 Aglets, Voyager, Odyssey 등은 이주할 때 네트워크 상태와 이주할 노드의 상태를 고려하지 못함으로써 효율적인 이주를 보장할 수 없다. 본 논문은 이동 에이전트에서 효율적인 이주를 위해 AMIS를 제안한다. AMIS는 이동 에이전트의 총 이주 시간을 최소화하며, 안전하고 견고한 이주를 위해 최단 수행 순서를 제공한다.

• 디지털산업정보학회논문지
• /
• 제13권4호
• /
• pp.153-162
• /
• 2017

Advances in communication technologies and the decreasing cost of computers have made distributed computer systems an attractive alternative for satisfying the information needs of large organizations. This paper presents a distributed algorithm for performance improvement through load balancing and file migration in distributed systems. We employed a sender initiated strategy for task migration and used learning automata with several internal states for file migration. A task can be migrated according to the load information of a computer. A file is migrated to the destination processor when it is in the right boundary state. We also described an analytical model for load balancing with file migration to verify the proposed algorithm. Analytical and simulation results show that our algorithm is very well-suited for distributed system environments.

• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2019년도 제60차 하계학술대회논문집 27권2호
• /
• pp.7-8
• /
• 2019

In this paper, we applied the migration technique to real-time tasks that have relatively low criticality but still important to be dropped by the mixed-criticality scheduling algorithms. The proposed drop and migrate algorithm analyzes the schedulability by calculating CPU utilization and response time of using task migration. We provide analysis to guarantee the deadline of LO-tasks, by transforming the response time equation specified with migration time. The transformed response time equation was able to analyze the migration schedulability. This algorithm can be used with various mixed-criticality schedulers as a supplementary method. We expect this algorithm will be used for scheduling LO-tasks such as communication task that requires safety guarantee especially in platooning and autonomous driving by utilizing the advantages of multiple node connectivities.

• Current Optics and Photonics
• /
• 제5권6호
• /
• pp.617-626
• /
• 2021

A parallel Monte Carlo photon migration algorithm for graphics processing units that implements an improved load-balancing strategy is presented. Conventional parallel Monte Carlo photon migration algorithms suffer from a computational bottleneck due to their reliance on a simple load-balancing strategy that does not take into account the different length of the mean free paths of the photons. In this paper, path-partition load balancing is proposed to eliminate this computational bottleneck based on a mathematical formula that parallelizes the photon path tracing process, which has previously been considered non-parallelizable. The performance of the proposed algorithm is tested using three-dimensional photon migration simulations of a human skin model.

• 한국정보과학회논문지:소프트웨어및응용
• /
• 제28권1호
• /
• pp.26-30
• /
• 2001

다중개체군 유전자 알고리즘은 일반 유전자 알고리즘의 변형된 형태로써, 일반 유전자 알고리즘의 단점을 보완하기 위해서 고안되었다. 다중 개체군 유전자 알고리즘의 핵심은 이주방식이라고 할 수 있다. 현재 적합도 기반 이주방식과 임의 이주 방식이 널리 쓰이고 있다. 이 가운데 개체의 다양성을 유지하는 임의 이주 방식이 보다 널리 쓰이고 있는 일반적인 이주 방식이다. 본 논문은 다중 개체군 유전자 알고리즘의 새로운 이주 방식을 제안하다. 일반적인 이주 방식에 비해서 수렴 속도를 향상시킬 수 있다는 장점이 있다. 제안하는 이주 방식을 일반적인 이주 방식과 비교해 보았다.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
• /
• pp.629-633
• /
• 1998

Proposed here is a parallel genetic algorithm accompanied with intermittent migration among subpopulations. It is intended to maintain diversity in the population for a long period . This method was applied to finding out the global maximum of some multimodal functions for which no other methods seem to be useful . Preferable results and their detailed analysis are also presented.

• 한국정보과학회:학술대회논문집
• /
• 한국정보과학회 2011년도 한국컴퓨터종합학술대회논문집 Vol.38 No.1(B)
• /
• pp.9-12
• /
• 2011

가상화 기술은 물리적 컴퓨팅 자원의 추상화를 통해 다수의 운영 체제나 응용 프로그램이 물리적 서버의 자원을 공유하게 함으로써 소요비용을 절감하고 자원을 통합 관리할 수 있는 기술이다. 그러나 가상화 기반 클러스터에서는 클러스터를 이루는 물리적 서버들이 균형적으로 자원을 활용하지 못하고 특정 서버로 자원 활용률이 집중되는 현상(hotspot)이 발생 할 수 있다. 이에 본 논문에서는 Live migration 기술을 이용하여 가상화 클러스터의 자원 효율을 높이는 알고리즘을 제안한다. 제안 알고리즘은 hotspot의 원인이 되는 자원의 우선순위를 기반으로 가장 적합한 대상을 선정하여 가상머신을 이동시켜 클러스터의 자원 활용률의 균형을 도모하고 추가적인 hotspot의 발생을 최소화고 불필요한 Live migration을 방지하여 migration시에 발생하는 로드를 줄일 수 있다.

• 조명전기설비학회논문지
• /
• 제23권3호
• /
• pp.24-29
• /
• 2009

In case of the optimization problems that have many design variables, the conventional genetic algorithms(GA) fall into a trap of local minima with high probability. This problem is called the premature convergence problem. To overcome it, the parallel genetic algorithms which adopt the migration mechanism have been suggested. But it is hard to determine the several parameters such as the migration size and the migration interval for the parallel GAs. Therefore, we propose a new method to determine the migration interval automatically in this paper. To verify its validity, it is applied to some traditional mathematical optimization problems and is compared with the conventional parallel GA. It is also applied to the optimal design of the brushless DC motor for an electric wheel chair which is a real world problem and has five design variables.