• Title, Summary, Keyword: PSO

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Comparison Studies of PSO Techniques for PV System Allocation Problem (PV 시스템 계획 문제에 대한 PSO 기법들의 비교 연구)

  • Diolata, Ryan;Song, Hwa-Chang;Joo, Young-Hoon
    • Proceedings of the KIEE Conference
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    • pp.482-483
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    • 2008
  • This paper compares particle swarm optimization techniques for PV allocation planning problem in power systems. PV allocation planning problem is formulated as a mixed-integer nonlinear problem. Five variants of PSO techniques are investigated for the applicability on the PV allocation problem. Namely, PSO with constant inertia weight approach (PSO-CIW), PSO with time varying inertia weight (PSO-TVIW), PSO with random inertia weight (PSO-RIW), PSO with constriction factor (PSO-CF) and PSO with time varying acceleration coefficients (PSO-TVAC).

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The PSO-PID Speed Controller Design for the BLDC Motor (BLDC 모터를 위한 PSO-PID 속도 제어기 설계)

  • Kim, Seung-Ki;Han, Byung-Jo;Yang, Hai-Won
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.60 no.9
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    • pp.1777-1782
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    • 2011
  • Brushless DC motors applied in many control systems because of the good respose characteristic and the easy control characteristic. The speed control of the BLDC motors is important in the systems. This paper has designed PSO-PID speed controller for the speed control of BLDC motors. The PSO algorithm optimized the parameters of the PID controller in the PSO-PID speed controller. The several methods obtained the optimal inertia weight of the PSO algorithm by comparison. The optimal inertia weight of the PSO algorithm optimized the PSO-PID speed controller for BLDC motors. This paper confirmed the performance of proposed PSO-PID speed controller through simulation results.

On Convergence and Parameter Selection of an Improved Particle Swarm Optimization

  • Chen, Xin;Li, Yangmin
    • International Journal of Control, Automation, and Systems
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    • v.6 no.4
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    • pp.559-570
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    • 2008
  • This paper proposes an improved particle swarm optimization named PSO with Controllable Random Exploration Velocity (PSO-CREV) behaving an additional exploration behavior. Different from other improvements on PSO, the updating principle of PSO-CREV is constructed in terms of stochastic approximation diagram. Hence a stochastic velocity independent on cognitive and social components of PSO can be added to the updating principle, so that particles have strong exploration ability than those of conventional PSO. The conditions and main behaviors of PSO-CREV are described. Two properties in terms of "divergence before convergence" and "controllable exploration behavior" are presented, which promote the performance of PSO-CREV. An experimental method based on a complex test function is proposed by which the proper parameters of PSO-CREV used in practice are figured out, which guarantees the high exploration ability, as well as the convergence rate is concerned. The benchmarks and applications on FCRNN training verify the improvements brought by PSO-CREV.

Effects of Petroleum Spray Oil on Photosynthesis Characteristics in Citrus Leaves (Petroleum Spray Oil 살포가 감귤 잎의 광합성관련 특성에 미치는 영향)

  • Kang, Si-Yong;Kim, Pan-Gi;Park, Jin-Hee;Riu, Key-Zung
    • Korean Journal of Environmental Agriculture
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    • v.20 no.3
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    • pp.186-191
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    • 2001
  • Recently, petroleum spray oil(PSO) has been used to control key pests in integrated pest management (IPM) of citrus and other orchards in Australia and USA. In order to clarify the influences of a newly developed PSO (D-C Tron $Plus^{(R)}$) on citrus leaves, 0.33% or 1.0% of PSO were sprayed to potted 4-year-old citrus trees under some kinds of condition, and then the changes of photosynthesis, transpiration, stomatal conductance and chlorophyll fluorescence(Fv/Fm) were determined. When sprayed with 1.0% PSO, the photosynthetic rate, transpiration and stomatal conductance of citrus leaves were decreased by 20%, and then recovered in 20 days after treatment (DAT), while there were little influences by the spray of 0.33% PSO. The value of Fv/Fm decreased more under the $34/24^{\circ}C$ temperature condition than that of under the $30/20^{\circ}C$ and $28/16^{\circ}C$ condition. The high temperature ($50^{\circ}C$ for 10 hours)-treated trees sprayed with PSO 1.0% or PSO 1.0% plus dithianon 1/2000 dilution showed not only the increase of rate in dropped leaf but also the reduced photosynthesis and Fv/Fm compared with $30/20^{\circ}C$ temperature-treated ones. From the results of this study, the spray of 1.0% PSO can inhibit the physiological activities in citrus leaf, particularly under high temperature condition after spray or the mixing-spray with a fungicide (dithianon WP, 75%).

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Fuzzy PSO Congestion Management using Sensitivity-Based Optimal Active Power Rescheduling of Generators

  • Venkaiah, Ch;Vinod Kumar, D M
    • Journal of Electrical Engineering and Technology
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    • v.6 no.1
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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.

PSO-SAPARB Algorithm applied to a VTOL Aircraft Longitudinal Dynamics Controller Design and a Study on the KASS (수직이착륙기 종축 제어기 설계에 적용된 입자군집 최적화 알고리즘과 KASS 시스템에 대한 고찰)

  • Lee, ByungSeok;Choi, Jong Yeoun;Heo, Moon-Beom;Nam, Gi-Wook;Lee, Joon Hwa
    • Journal of the Korean Society for Aviation and Aeronautics
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    • v.24 no.4
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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.

Coupling Particles Swarm Optimization for Multimodal Electromagnetic Problems

  • Pham, Minh-Trien;Song, Min-Ho;Koh, Chang-Seop
    • Journal of Electrical Engineering and Technology
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    • v.5 no.3
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    • pp.423-430
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    • 2010
  • Particle swarm optimization (PSO) algorithm is designed to find a single global optimal point. However, the PSO needs to be modified in order to find multiple optimal points of a multimodal function. These modifications usually divide a swarm of particles into multiple subswarms; in turn, these subswarms try to find their own optimal point, resulting in multiple optimal points. In this work, we present a new PSO algorithm, called coupling PSO to find multiple optimal points of a multimodal function based on coupling particles. In the coupling PSO, each main particle may generate a new particle to form a couple, after which the couple searches its own optimal point using non-stop-moving PSO algorithm. We tested the suggested algorithm and other ones, such as clustering PSO and niche PSO, over three analytic functions. The coupling PSO algorithm was also applied to solve a significant benchmark problem, the TEAM workshop problem 22.

Design of Leg Length for a Legged Walking Robot Based on Theo Jansen Using PSO (PSO를 이용한 테오얀센 기반의 보행로봇 다리설계)

  • Kim, Sun-Wook;Kim, Dong-Hun
    • Journal of Korean Institute of Intelligent Systems
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    • v.21 no.5
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    • pp.660-666
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    • 2011
  • In this paper, we proposed a Particle Swarm Optimization(PSO) to search the optimal link lengths for legged walking robot. In order to apply the PSO algorithm for the proposed, its walking robot kinematic analysis is needed. A crab robot based on four-bar linkage mechanism and Jansen mechanism is implemented in H/W. For the performance index of PSO, the stride length of the legged walking robot is defined, based on the propose kinematic analysis. Comparative simulation results present to illustrate the viability and effectiveness of the proposed method.

Particle Swarm Optimization for Scheduling and Permutation Problems using Random Key Representation (일정 및 순서 문제를 위한 난수 표현법을 사용한 PSO)

  • Lee, Sangwook
    • Proceedings of the Korea Contents Association Conference
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    • pp.331-332
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    • 2010
  • PSO는 사회 심리학과 진화 계산에 영감을 얻어 Kenney와 Eberhart에 의해 처음 소개되었다. PSO는 다양한 분야의 연속문제들에 성공적으로 적용되어 왔으나, 시퀀셜 문제를 위한 PSO 연구는 거의 없었다. 본 논문에서는 일정 및 순서 문제에 PSO를 적옹하기 위해 난수 표현법을 사용한 PSO를 제안한다. 실험결과 제안한 알고리즘은 일정 및 순저 문제를 해결하기위한 좋은 가능성을 지녔음을 보여주었다.

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Comparative Study on Dimensionality and Characteristic of PSO (PSO의 특징과 차원성에 관한 비교연구)

  • Park Byoung-Jun;Oh Sung-Kwun;Kim Yong-Soo;Ahn Tae-Chon
    • Journal of Institute of Control, Robotics and Systems
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    • v.12 no.4
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    • pp.328-338
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    • 2006
  • A new evolutionary computation technique, called particle swarm optimization(PSO), has been proposed and introduced recently. PSO has been inspired by the social behavior of flocking organisms, such as swarms of birds and fish schools and PSO is an algorithm that follows a collaborative population-based search model. Each particle of swarm flies around in a multidimensional search space looking for the optimal solution. Then, Particles adjust their position according to their own and their neighboring-particles experience. In this paper, characteristics of PSO such as mentioned are reviewed and compared with GA which is based on the evolutionary mechanism in natural selection. Also dimensionalities of PSO and GA are compared throughout numeric experimental studies. The comparative studies demonstrate that PSO is characterized as simple in concept, easy to implement, and computationally efficient and can generate a high-quality solution and stable convergence characteristic than GA.