• 대한임베디드공학회논문지
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• 제14권1호
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• pp.19-24
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• 2019

Embedded swarm systems consist of a large number of robots that use local control laws based on spatial information nearby environment and adjacent robots. In this paper, we propose a new scheme for indirect swarm configuration in swarm interaction system to adapt the swarm operation according to the desired goal. Also, we provide a method for the operator to observe the state of the swarm, which results in providing appropriate input to the swarm. We analyze the stability properties of the proposed swarm system and show the simulation results.

• 한국멀티미디어학회논문지
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• 제6권2호
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• pp.332-339
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• 2003

경험적 탐색(Modem Heuristics) 방법을 이용하여 복잡한 문제들의 근사해를 구하는 것이 가능하여졌다. 최근 제시된 Particle Swarm Optimization은 경험적 탐색 방법중의 하나로써 조류나 어류 등의 생물의 무리가 각각의 개체가 가지고 있는 정보를 공유해가며 먹이를 찾아가는 과정을 모의한 것이다. 그러나, 다양한 문제들의 근사해를 구하기 위해 Particle Swarm Optimization 방법을 이용하여 왔지만 해를 탐색하는 과정을 보여주기 위한 시도는 이루어지지 않았다. 본 논문에서는 Particle Swarm Optimization의 탐색과정을 가시화 하는 것을 목적으로 한다. 가시화 하는 작업을 통해 그 탐색 능력을 시각적으로 파악하는 것이 가능하며 해결방법에 관한 이해를 돕고 교육적 효과도 기대 가능하다.

• 한국항공우주학회지
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• 제49권12호
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• pp.971-980
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• 2021

최근 드론 기술의 대중화와 함께 저비용의 소형 무인기를 다수 또는 군집으로 운용함으로써 상당한 군사적 효용성을 얻을 수 있음이 알려지면서, 군집무인체계의 전장 활용을 위한 연구가 활발히 진행되고 있다. 국방과학연구소에서는 이와 관련한 주요기술로 군집제어, 군집통신, 군집정보, 군집협업 기술을 식별하였으며, 1단계로써 대상 무인체를 운용하는 데 필요한 군집제어와 군집통신 기술에 대한 연구를 수행하였다. 본 논문에서는 소형 고정익 무인기 기반의 군집무인기시스템을 설계 및 제작하고, 군집제어 및 군집통신 기술을 비행시험으로 검증한 과정을 소개한다. 최종비행시험에서 무인기 19대가 군집비행을 수행함으로써 국내 최초로 군사적으로 활용도가 높은 고정익 무인기 약 20대 규모의 군집 비행시험에 성공하였다.

• Industrial Engineering and Management Systems
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• 제6권1호
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• pp.64-71
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• 2007

J. Kennedy and R. Eberhart first introduced the concept called as Particle Swarm Optimization (PSO). They applied it to optimize continuous nonlinear functions and demonstrated the effectiveness of the algorithm. Since then a considerable number of researchers have attempted to apply this concept to a variety of optimization problems and obtained reasonable results. In PSO, individuals communicate and exchange simple information with each other. The information among individuals is communicated in the swarm and the information between individuals and their swarm is also shared. Finally, the swarm approaches the optimal behavior. It is reported that reasonable approximate solutions of various types of test functions are obtained by employing PSO. However, if more precise solutions are required, additional algorithms and/or hybrid algorithms would be necessary. For example, the heading vector of the swarm can be slightly adjusted under some conditions. In this paper, we propose a hybrid algorithm to obtain more precise solutions. In the algorithm, when a better solution in the swarm is found, the neighborhood of a certain distance from the solution is searched. Then, the algorithm returns to the original PSO search. By this hybrid method, we can obtain considerably better solutions in less iterations than by the standard PSO method.

• International Journal of Aeronautical and Space Sciences
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• 제13권3호
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• pp.323-331
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• 2012

A simple and robust methodology is presented to determine the location and amount of crack in beam like structures based on the incremental particle swarm optimization technique. A comparison is made for assessing the performance of standard particle swarm optimization and the incremental particle swarm optimization technique for detecting crack in structural members. The objective function is formulated using the measured natural frequency of the intact structure and the frequency obtained from the finite element simulation. The outcomes of the simulated results demonstrate that the developed method is capable of detecting and estimating the extent of damages with satisfactory precision.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.786_787
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• 2009

This paper proposes a novel multimodal optimization method, Coupling particles swarm optimization (PSO), to find all optima in design space. This method based on the conventional Particle Swarm Optimization with modifications. The Coupling method is applied to make a couple from main particle and then each couple of particles searches its own optimum by using non-stop-moving PSO. We tested out our method and other one, such as ClusteringParticle Swarm Optimization and Niche Particle Swarm Optimization, on three analytic functions. The Coupling Particle Swarm Optimization is also applied to solve a significant benchmark problem, the TEAM workshop benchmark problem 22

• 한국소음진동공학회논문집
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• 제19권9호
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• pp.884-891
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• 2009

This study investigates some of swarm intelligence algorithms to tackle a traditional damage detection problem having stiffness degradation or damage in mechanical structures. Particle swarm(PSO) and ant colony optimization(ACO) methods have been exploited for localizing and estimating the location and extent damages in a structure. Both PSO and ACO are population-based, stochastic algorithms that have been developed from the underlying concept of swarm intelligence and search heuristic. A finite element (FE) model updating is implemented to minimize the difference in a set of natural frequencies between measured and baseline vibration data. Stiffness loss of certain elements is considered to simulate structural damages in the FE model. It is numerically shown that PSO and ACO algorithms successfully completed the optimization process of model updating in locating unknown damages in a truss structure.

• 제어로봇시스템학회논문지
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• 제18권9호
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• pp.812-818
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• 2012

In this paper, we propose an asynchronous behavior control algorithm of the swarm robot for surrounding intruders when detected an intruder in a surveillance environment. The proposed method is divided into three parts: First, we proposed the method for the modeling of a state of the swarm robot. Second, we proposed an asynchronous behavior control algorithm for the surrounding an intruder by the swarm robot. Third, we proposed a control method for the collision avoidance with the swarm robot. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권4호
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• pp.2038-2057
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• 2017

Gaussian learning is a new technology in the computational intelligence area. However, this technology weakens the learning ability of a particle swarm and achieves a lack of diversity. Thus, this paper proposes a vector Gaussian learning strategy and presents an effective approach, named particle swarm optimization based on vector Gaussian learning. The experiments show that the algorithm is more close to the optimal solution and the better search efficiency after we use vector Gaussian learning strategy. The strategy adopts vector Gaussian learning to generate the Gaussian solution of a swarm's optimal location, increases the learning ability of the swarm's optimal location, and maintains the diversity of the swarm. The method divides the states into normal and premature states by analyzing the state threshold of the swarm. If the swarm is in the premature category, the algorithm adopts an inertia weight strategy that decreases linearly in addition to vector Gaussian learning; otherwise, it uses a fixed inertia weight strategy. Experiments are conducted on eight well-known benchmark functions to verify the performance of the new approach. The results demonstrate promising performance of the new method in terms of convergence velocity and precision, with an improved ability to escape from a local optimum.

• 제어로봇시스템학회논문지
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• 제18권5호
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• pp.465-470
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• 2012

This paper proposes a distributed autonomous control method of swarm robot behavior strategy based on artificial immune system and an optimization strategy for artificial immune system. The behavior strategies of swarm robot in the system are depend on the task distribution in environment and we have to consider the dynamics of the system environment. In this paper, the behavior strategies divided into dispersion and aggregation. For applying to artificial immune system, an individual of swarm is regarded as a B-cell, each task distribution in environment as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows: When the environmental condition changes, the agent selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other agent using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. In order to decide more accurately select the behavior strategy, the optimized parameter learning procedure that is represented by stimulus function of antigen to antibody in artificial immune system is required. In this paper, particle swarm optimization algorithm is applied to this learning procedure. The proposed method shows more adaptive and robustness results than the existing system at the viewpoint that the swarm robots learning and adaptation degree associated with the changing of tasks.