• International Journal of Aeronautical and Space Sciences
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• 제14권4호
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• pp.341-349
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• 2013

This paper considers the optimum design of flexbeam cross-sections for a full-scale bearingless helicopter rotor, using an efficient hybrid optimization algorithm based on particle swarm optimization, and an improved genetic algorithm, with an effective constraint handling scheme for constrained nonlinear optimization. The basic operators of the genetic algorithm, of crossover and mutation, are revisited, and a new rank-based multi-parent crossover operator is utilized. The rank-based crossover operator simultaneously enhances both the local, and the global exploration. The benchmark results demonstrate remarkable improvements, in terms of efficiency and robustness, as compared to other state-of-the-art algorithms. The developed algorithm is adopted for two baseline flexbeam section designs, and optimum cross-section configurations are obtained with less function evaluations, and less computation time.

• Journal of Information Processing Systems
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• 제18권3호
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• pp.332-343
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• 2022

To address the problems of the gravitational search algorithm (GSA) in which the population is prone to converge prematurely and fall into the local solution when solving the single-objective optimization problem, a sine map jumping gravity search algorithm based on asynchronous learning is proposed. First, a learning mechanism is introduced into the GSA. The agents keep learning from the excellent agents of the population while they are evolving, thus maintaining the memory and sharing of evolution information, addressing the algorithm's shortcoming in evolution that particle information depends on the current position information only, improving the diversity of the population, and avoiding premature convergence. Second, the sine function is used to map the change of the particle velocity into the position probability to improve the convergence accuracy. Third, the Levy flight strategy is introduced to prevent particles from falling into the local optimization. Finally, the proposed algorithm and other intelligent algorithms are simulated on 18 benchmark functions. The simulation results show that the proposed algorithm achieved improved the better performance.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1169-1182
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• 2020

The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However, the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. The genetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and the optimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods and discussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved better performance than ACO_R, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reach the global optimal solution.

• 정보처리학회논문지B
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• 제16B권4호
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• pp.319-326
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• 2009

PSO(Particle Swarm Optimization)는 군집(swarm)을 구성하는 단순한 개체들인 입자(particle)들이 각자의 경험을 공유하여 문제의 해답을 찾는 최적화 알고리즘으로 다양한 분야에서 응용되고 있다. PSO에 대한 연구는 최적화를 위해 군집이 적합한 영역으로 빠르게 수렴하도록 하는 파라미터 값의 선정, 토폴로지, 입자의 이동에서 주로 이루어지고 있다. 표준 PSO 알고리즘은 입자 자신과 최고의 이웃이 제공하는 정보만을 이용해서 이동하므로 다양한 영역을 탐색하지 못하고 지역적 최적점에 조기 수렴하는 경향이 있다. 본 논문에서는 군집이 다양한 영역을 탐색하기 위해, 각 입자는 더 나은 경험을 가진 이웃입자들의 정보를 상대적인 중요도에 따라서 참조하여 이동하도록 하였다. 다양한 영역의 탐색은 표준 PSO 알고리즘보다 지역적 최적화의 확률을 줄이고 탐색 속도를 가속화하며 탐색의 성공률을 높일 수 있다. 또한 군집이 지역적 미니멈으로부터 벗어나기 위한 검사 전략을 제안하여 탐색의 성공률을 높였다. 제안한 PSO 알고리즘을 평가하기 위하여, 벤치마크 함수들에 적용한 결과 최적화의 진행 속도 개선과 탐색 성공률의 향상이 있었다.

• 정보처리학회논문지B
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• 제14B권5호
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• pp.377-382
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• 2007

본 논문에서는 방사 기저함수 네트워크의 파라미터를 전 영역에서 최적화하는 학습 알고리즘을 제안한다. 기존의 학습 알고리즘들은 지역 최적화만을 수행하기 때문에 성능의 한계가 있고 최종 결과가 초기 네트워크 파라미터 값에 크게 의존하는 단점이 있다. 본 논문에서 제안하는 하이브리드 모의 담금질 기법은 모의 담금질 기법의 전 영역 탐색 능력과 경사 기반 학습 알고리즘의 지역 최적화 능력을 조합하여 전 파라미터 영역에서 해를 찾을 수 있도록 한다. 제안하는 기법을 함수 근사화 문제에 적용하여 기존의 학습 알고리즘에 비해 더 좋은 학습 및 일반화 성능을 보이는 네트워크 파라미터를 찾을 수 있으며, 초기 파라미터 값의 영향을 크게 줄일 수 있음을 보인다.

• 대한기계학회논문집A
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• 제29권10호
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• pp.1369-1376
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• 2005

In structural design, the design variables are frequently selected from certain discrete values. Various optimization algorithms have been developed fDr discrete design. It is well known that many function evaluations are needed in such optimization. Recently, sequential algorithm with orthogonal arrays (SOA), which is a search algorithm for a local minimum in a discrete space, has been developed. It considerably reduces the number of function evaluations. However, it only finds a local minimum and the final solution depends on the initial values of the design variables. A new algorithm is proposed to adopt a genetic algorithm (GA) in SOA. The GA can find a solution in a global sense. The solution from the GA is used as the initial design of SOA. A sequential usage of the GA and SOA is carried out in an iterative manner until the convergence criteria are satisfied. The performance of the algorithm is evaluated by various examples.

• 한국공간구조학회논문집
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• 제8권3호
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• pp.75-82
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• 2008

본 연구에서는 방향벡터(direction vector)를 이용한 지역 탐색법과 유전자 알고리즘을 결합한 새로운 알고리즘인 D-GA를 제안한다. 새로운 개체(individual)를 찾기 위한 방향벡터로는 진화과정 중에 습득되는 정보를 활용하기 위한 학습방향벡터(Loaming direction vector)와 진화와는 무관하게 한 개체의 주변을 탐색하는 랜덤방향벡터(random direction vector) 등 두 가지를 구성하였다. 그리고, 10 부재 트러스 설계 문제에 단순 유전자 알고리즘과 D-GA를 적용하여 최적화를 수행하였고, 그 결과를 비교 검토함으로써 단순 GA에 비하여 D-GA의 정확성 및 효율성이 향상되었음을 확인하였다.

• 전자공학회논문지A
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• 제29A권4호
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• pp.64-73
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• 1992

This paper presents the design of multi-level logic optimization algorithm and the development of the SMILE system based on the algorithm. Considering the fanin constraints in algorithmic level, SMILE performs global and local optimization in a predefined sequence using heuristic information. Designed under the Sogang Silicon Compiler design environment, SMILE takes the SLIF netlist or Berkeley equation formats obtained from high-level synthesis process, and generates the optimized circuits in the same format. Experimental results show that SMILE produces the promising results for some circuits from MCNC benchmarks, comparable to the popularly used multi-level logic optimization system, MIS.

• 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.

• 산업기술연구
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• 제15권
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• pp.63-70
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• 1995

This paper presents an impact control algorithm for reducing the potentially damaging effects by interation of redundant manipulators with their environments. In the proposed control algorithm, the redundancy is resolved at the torque level by locally minimizing joint torque, subject to the operational space dynamic formulation which maps the joint torque set into the operational forces. For a given pre-impact velocity of the manipulator, the proposed approach is on generating joint space trajectories throughout the motion near the contact which instantaneously minimize the impulsive force which is a scalar function of manipulator's configurations. The comparative evaluation of the proposed algorithm with a local torque optimization algorithm with a local torque optimization algorithm without reducing impact is performed by computer simulation. The simulation results illustrate the effectiveness of the algorithm in reducing both the effects of impact and large torque requirements.