• Journal of international Conference on Electrical Machines and Systems
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• 제1권1호
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• pp.135-142
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• 2012

In this paper, optimal selection of the unified power flow controller (UPFC) damping controller parameters in order to improve the power system dynamic response and its stability based on two modified intelligent algorithms have been proposed. These algorithms are based on a modified intelligent particle swarm optimization (PSO) and continuous genetic algorithm (GA). After extraction of UPFC dynamic model, intelligent PSO and genetic algorithms are used to select the effective feedback signal of the damping controller; then, to compare the performance of the proposed UPFC controller in damping the critical modes of a single-machine infinite-bus (SMIB) power system, the simulation results are presented. The comparison shows the good performance of both presented PSO and genetic algorithms in an optimal selection of UPFC damping controller parameters and damping oscillations.

• 한국항공운항학회지
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• 제24권4호
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• pp.12-19
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• 2016

In the case of hard problems to find solutions or complx combination problems, there are various optimization algorithms that are used to solve the problem. Among these optimization algorithms, the representative of the optimization algorithm created by imitating the behavior patterns of the organism is the PSO (Particle Swarm Optimization) algorithm. Since the PSO algorithm is easily implemented, and has superior performance, the PSO algorithm has been used in many fields, and has been applied. In particular, PSO-SAPARB (PSO with Swarm Arrangement, Parameter Adjustment and Reflective Boundary) algorithm is an advanced PSO algorithm created to complement the shortcomings of PSO algorithm. In this paper, this PSO-SAPARB algorithm was applied to the longitudinal controller design of a VTOL (Vertical Take-Off and Landing) aircraft that has the advantages of fixed-wing aircraft and rotorcraft among drones which has attracted attention in the field of UAVs. Also, through the introduction and performance of the Korean SBAS (Satellite Based Augmentation System) named KASS (Korea Augmentation Satellite System) which is being developed currently, this paper deals with the availability of algorithm such as the PSO-SAPARB.

• 전기학회논문지
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• 제62권11호
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• pp.1578-1583
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• 2013

In this paper, stochastic optimization algorithms of PSO (Particle Swarm Optimization) and APSO (Adaptive Particle Swam Optimization) are studied and compared. It is revealed that the APSO provides faster convergence and better search efficiency than the conventional PSO when they are adopted to find the global minimum of a two-dimensional function. The advantages of the APSO comes from the ability to control the inertia weight, and acceleration coefficients. To verify that the APSO is working better than the standard PSO, the design of a 10GHz microstrip patch as one of the elements of a high frequency array antenna is taken as a test-case and shows the optimized result with 5 iterations in the APSO and 28 iterations in th PSO.

• 정보처리학회논문지B
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• 제15B권4호
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• pp.331-340
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• 2008

특징 선택은 기계 학습에서 분류의 성능을 높이기 위해 사용되는 방법이다. 여러 방법들이 개발되고 사용되어 오고 있으나, 전체 데이터에서 최적화된 특징 부분집합을 구성하는 문제는 여전히 어려운 문제로 남아있다. 생태계 모방 알고리즘은 생물체들의 행동 원리 등을 기반으로하여 만들어진 진화적 알고리즘으로, 최적화된 해를 찾는 문제에서 매우 유용하게 사용되는 방법이다. 특징 선택 문제에서도 생태계 모방 알고리즘을 이용한 해결방법들이 제시되어 오고 있으며, 이에 본 논문에서는 생태계 모방 알고리즘을 이용한 특징 선택 방법을 개선하는 방안을 제시한다. 이를 위해 잘 알려진 생태계 모방 알고리즘인 유전자 알고리즘(GA)과 파티클 집단 최적화 알고리즘(PSO)을 이용하여 데이터에서 가장분류 성능이 우수한 특징 부분집합을 만들어 내도록 하고, 최종적으로 개별 특징의 사전 중요도를 설정하여 생태계 모방 알고리즘을 개선하는 방법을 제안하였다. 이를 위해 개별 특징의 우수도를 구할 수 있는 mRMR이라는 방법을 이용하였다. 이렇게 설정한 사전 중요도를 이용하여 GA와 PSO의 진화 연산을 수정하였다. 데이터를 이용한 실험을 통하여 제안한 방법들의 성능을 검증하였다. GA와 PSO를 이용한 특징 선택 방법은 그 분류 정확도에 있어서 뛰어난 성능을 보여주었다. 그리고 최종적으로 제시한 사전 중요도를 이용해 개선된 방법은 그 진화 속도와 분류 정확도 면에서 기존의 GA와 PSO 방법보다 더 나아진 성능을 보여주는 것을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권12호
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• pp.5780-5802
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• 2017

Cloud computing system consists of distributed resources in a dynamic and decentralized environment. Therefore, using cloud computing resources efficiently and getting the maximum profits are still challenging problems to the cloud service providers and cloud service users. It is important to provide the efficient scheduling. To schedule cloud resources, numerous heuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search (CS) algorithms have been adopted. The paper proposes a Modified Particle Swarm Optimization (MPSO) algorithm to solve the above mentioned issues. We first formulate an optimization problem and propose a Modified PSO optimization technique. The performance of MPSO was evaluated against PSO, and GA. Our experimental results show that the proposed MPSO minimizes the task execution time, and maximizes the resource utilization rate.

• 한국건축시공학회지
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• 제17권4호
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• pp.369-375
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• 2017

본 연구는 제설작업의 효율성을 높일 수 있도록 제설차량의 작업구간 할당 및 제설기지 위치를 최적화할 수 있는 PSO 알고리듬을 제시하였다. 기존의 PSO 알고리듬을 개선하여 해공간의 탐색 성능을 높일 수 있는 개선된 알고리듬을 제시하였으며, 제설차량의 작업구간 할당 문제에 적용할 수 있도록 개체의 표현 및 적합도 합수값을 제시하였다. 또한 제시한 알고리듬의 타당성을 검증하기 위하여 지자체의 실제 사례에 적용하였으며, 기존 알고리듬과 개선된 알고리듬을 비교하였다. 그 결과 개선된 PSO의 경우 기존 알고리듬보다 폭넓게 해공간을 탐색하여 지역해에 빠지지 않고 더 우수한 해를 도출하는 것을 알 수 있다. 또한 개별 제설차량의 작업부하가 평준화될 수 있도록 작업구간을 할당할 수 있으며, 할당된 작업구간에 가장 가까운 지점을 도출하여 제설전진기지의 위치를 결정하는데 활용될 수 있음을 알 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권9호
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• pp.2037-2051
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• 2012

Cognitive radio (CR) is envisioned as a promising paradigm of exploiting intelligence for enhancing efficiency of underutilized spectrum bands. In CR, the main concern is to reliably sense the presence of primary users (PUs) to attain protection against harmful interference caused by potential spectrum access of secondary users (SUs). In this paper, evolutionary algorithms, namely, particle swarm optimization (PSO) and genetic algorithm (GA) are proposed to minimize the total sensing decision error at the common soft data fusion (SDF) centre of a structurally-centralized cognitive radio network (CRN). Using these techniques, evolutionary operations are invoked to optimize the weighting coefficients applied on the sensing measurement components received from multiple cooperative SUs. The proposed methods are compared with each other as well as with other conventional deterministic algorithms such as maximal ratio combining (MRC) and equal gain combining (EGC). Computer simulations confirm the superiority of the PSO-based scheme over the GA-based and other conventional MRC and EGC schemes in terms of detection performance. In addition, the PSO-based scheme also shows promising convergence performance as compared to the GA-based scheme. This makes PSO an adequate solution to meet real-time requirements.

• Industrial Engineering and Management Systems
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• 제11권3호
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• pp.215-223
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• 2012

This paper focuses on three very similar evolutionary algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). While GA is more suitable for discrete optimization, PSO and DE are more natural for continuous optimization. The paper first gives a brief introduction to the three EA techniques to highlight the common computational procedures. The general observations on the similarities and differences among the three algorithms based on computational steps are discussed, contrasting the basic performances of algorithms. Summary of relevant literatures is given on job shop, flexible job shop, vehicle routing, location-allocation, and multimode resource constrained project scheduling problems.

• Journal of Electrical Engineering and Technology
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• 제6권1호
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• pp.32-41
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• 2011

This paper presents a new method of Fuzzy Particle Swarm Optimization (FPSO)-based Congestion Management (CM) by optimal rescheduling of active powers of generators. In the proposed method, generators are selected based on their sensitivity to the congested line for efficient utilization. The task of optimally rescheduling the active powers of the participating generators to reduce congestion in the transmission line is attempted by FPSO, Fitness Distance Ratio PSO (FDR-PSO), and conventional PSO. The FPSO and FDR-PSO algorithms are tested on the IEEE 30-bus and Practical Indian 75-bus systems, after which the results are compared with conventional PSO to determine the effectiveness of CM. Compared with FDR-PSO and PSO, FPSO can better perform the optimal rescheduling of generators to relieve congestion in the transmission line.

• 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.