• Title, Summary, Keyword: particle swarm optimization

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Utilizing Particle Swarm Optimization into Multimodal Function Optimization

  • Pham, Minh-Trien;Baatar, Nyambayar;Koh, Chang-Seop
    • Proceedings of the KIEE Conference
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    • pp.86-89
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    • 2008
  • There are some modified methods such as K-means Clustering Particle Swarm Optimization and Niching Particle Swarm Optimization based on PSO which aim to locate all optima in multimodal functions. K-means Clustering Particle Optimization could locate all optima of functions with finite number of optima. Niching Particle Swarm Optimization is able to locate all of optima but high computing time. Because of those disadvantages, we proposed a new method that could locate all of optima with reasonal time. We applied our method and others as well to analytic functions. By comparing the outcomes, it is shown that our method is significantly more effective than the two others.

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Combinatorial particle swarm optimization for solving blocking flowshop scheduling problem

  • Eddaly, Mansour;Jarboui, Bassem;Siarry, Patrick
    • Journal of Computational Design and Engineering
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    • v.3 no.4
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    • pp.295-311
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    • 2016
  • This paper addresses to the flowshop scheduling problem with blocking constraints. The objective is to minimize the makespan criterion. We propose a hybrid combinatorial particle swarm optimization algorithm (HCPSO) as a resolution technique for solving this problem. At the initialization, different priority rules are exploited. Experimental study and statistical analysis were performed to select the most adapted one for this problem. Then, the swarm behavior is tested for solving a combinatorial optimization problem such as a sequencing problem under constraints. Finally, an iterated local search algorithm based on probabilistic perturbation is sequentially introduced to the particle swarm optimization algorithm for improving the quality of solution. The computational results show that our approach is able to improve several best known solutions of the literature. In fact, 76 solutions among 120 were improved. Moreover, HCPSO outperforms the compared methods in terms of quality of solutions in short time requirements. Also, the performance of the proposed approach is evaluated according to a real-world industrial problem.

Photovoltaic System Allocation Using Discrete Particle Swarm Optimization with Multi-level Quantization

  • Song, Hwa-Chang;Diolata, Ryan;Joo, Young-Hoon
    • Journal of Electrical Engineering and Technology
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    • v.4 no.2
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    • pp.185-193
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    • 2009
  • This paper presents a methodology for photovoltaic (PV) system allocation in distribution systems using a discrete particle swarm optimization (DPSO). The PV allocation problem is in the category of mixed integer nonlinear programming and its formulation may include multi-valued dis-crete variables. Thus, the PSO requires a scheme to deal with multi-valued discrete variables. This paper introduces a novel multi-level quantization scheme using a sigmoid function for discrete particle swarm optimization. The technique is employed to a standard PSO architecture; the same velocity update equation as in continuous versions of PSO is used but the particle's positions are updated in an alternative manner. The set of multi-level quantization is defined as integer multiples of powers-of-two terms to efficiently approximate the sigmoid function in transforming a particle's position into discrete values. A comparison with a genetic algorithm (GA) is performed to verify the quality of the solutions obtained.

Multi-Grouped Particle Swarm Strategy for Multi-modal Optimization (Multi-modal 최적화를 위한 다중 그룹 Particle Swarm 전략)

  • Seo, Jang-Ho;Jung, Hyun-Kyo
    • Proceedings of the KIEE Conference
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    • pp.1026-1028
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    • 2005
  • 본 논문에서는 PSO(Particle Swarm Optimization)에 기초하여 multi-modal 최적화를 위한 다중 그룹 Particle Swarm 최적화 알고리즘(MGPSO)을 제안하였다. 제안된 알고리즘은 PSO의 기본 특성을 유지하기 때문에 기존의 혼합형 타입의 최적화 방식에 비하여 빠른 수렴 시간을 가지며 구성방식이 간단하다. 여러 개의 피크를 가지는 테스트 함수를 통해 본 논문에서 제시한 알고리즘의 타당성을 입증하였으며, 영구자석 매입형 전동기의 최적 설계에 적용하여 그 유용성을 확인하였다.

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Charging Control Strategy of Electric Vehicles Based on Particle Swarm Optimization

  • Boo, Chang-Jin
    • Journal of IKEEE
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    • v.22 no.2
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    • pp.455-459
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    • 2018
  • In this paper, proposed a multi-channel charging control strategy for electric vehicle. This control strategy can adjust the charging power according to the calculated state-of-charge (SOC). Electric vehicle (EV) charging system using Particle Swarm Optimization (PSO) algorithm is proposed. A stochastic optimization algorithm technique such as PSO in the time-of-use (TOU) price used for the energy cost minimization. Simulation results show that the energy cost can be reduced using proposed method.

Optimization of the Parameter of Neuro-Fuzzy system using Particle Swarm Optimization (PSO를 이용한 뉴로-퍼지 시스템의 파라미터 최적화)

  • Kim Seung-Seok;Kim Yong-Tae;Kim Ju-Sik;Jeon Byeong-Seok
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.168-171
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    • 2006
  • 본 논문에서는 Particle Swarm Optimization 기법을 이용한 뉴로-퍼지 시스템의 파라미터 동정을 실시한다. PSO의 학습 및 군집 특성을 이용하여 시스템을 학습한다. 유전 알고리즘과 같은 무작위 탐색법을 이용하며 하나의 해 군집에 대해 다수 객체들이 탐색하는 기법을 통하여 최적해 부분의 탐색성능을 높여 전체 모델의 학습성능을 개선하고자 한다. 제안된 기법의 유용성을 시뮬레이션을 통하여 보이고자 한다.

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An Optmival design of Circularly Polarization Antenna for Sensor Node using Adaptive Particle Swarm Optimization (APSO 알고리즘을 이용한 센서노드용 원형편파 안테나 최적설계)

  • Kim, Koon-Tae;Kang, Seong-In;Oh, Seung-Hun;Lee, Jeong-Hyeok;Han, Jun-Hee;Jang, Dong-Hyeok;Wu, Chao;Kim, Hyeong-Seok
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.682-685
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    • 2014
  • In this paper, an improved designed of the circularly polarization antenna for sensor node. Stochastic optimization algorithms of Particle Swarm Optimization (PSO) and Adaptive Particle Swam Optimization(APSO) are studied and compared. To verify that the APSO is working better than the standard PSO, the design of a circularly polarization antenna is shows the optimized result with 27 iterations in the APSO and 41 iterations in th PSO.

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Hybrid PSO and SSO algorithm for truss layout and size optimization considering dynamic constraints

  • Kaveh, A.;Bakhshpoori, T.;Afshari, E.
    • Structural Engineering and Mechanics
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    • v.54 no.3
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    • pp.453-474
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    • 2015
  • A hybrid approach of Particle Swarm Optimization (PSO) and Swallow Swarm Optimization algorithm (SSO) namely Hybrid Particle Swallow Swarm Optimization algorithm (HPSSO), is presented as a new variant of PSO algorithm for the highly nonlinear dynamic truss shape and size optimization with multiple natural frequency constraints. Experimentally validation of HPSSO on four benchmark trusses results in high performance in comparison to PSO variants and to those of different optimization techniques. The simulation results clearly show a good balance between global and local exploration abilities and consequently results in good optimum solution.

Hybrid BFPSO Approach for Effective Tuning of PID Controller for Load Frequency Control Application in an Interconnected Power System

  • Anbarasi, S.;Muralidharan, S.
    • Journal of Electrical Engineering and Technology
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    • v.12 no.3
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    • pp.1027-1037
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    • 2017
  • Penetration of renewable energy sources makes the modern interconnected power systems to have more intelligence and flexibility in the control. Hence, it is essential to maintain the system frequency and tie-line power exchange at nominal values using Load Frequency Control (LFC) for efficient, economic and reliable operation of power systems. In this paper, intelligent tuning of the Proportional Integral Derivative (PID) controller for LFC in an interconnected power system is considered as a main objective. The chosen problem is formulated as an optimization problem and the optimal gain parameters of PID controllers are computed with three innovative swarm intelligent algorithms named Particle Swarm Optimization (PSO), Bacterial Foraging Optimization Algorithm (BFOA) and hybrid Bacterial Foraging Particle Swarm Optimization (BFPSO) and a comparative study is made between them. A new objective function designed with necessary time domain specifications using weighted sum approach is also offered in this report and compared with conventional objective functions. All the simulation results clearly reveal that, the hybrid BFPSO tuned PID controller with proposed objective function has better control performances over other optimization methodologies.

Swarm Intelligence-based Optimal Design for Selecting the Kinematic Parameters of a Manipulator According to the Desired Task Space Trajectory (요청한 작업 경로에 따른 매니퓰레이터의 기구학적 변수 선정을 위한 군집 지능 기반 최적 설계)

  • Lee, Joonwoo
    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.25 no.6
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    • pp.504-510
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    • 2016
  • Robots are widely utilized in many fields, and various demands need customized robots. This study proposes an optimal design method based on swarm intelligence for selecting the kinematic parameter of a manipulator according to the task space trajectory desired by the user. The optimal design method is dealt with herein as an optimization problem. This study is based on swarm intelligence-based optimization algorithms (i.e., ant colony optimization (ACO) and particle swarm optimization algorithms) to determine the optimal kinematic parameters of the manipulator. The former is used to select the optimal kinematic parameter values, whereas the latter is utilized to solve the inverse kinematic problem when the ACO determines the parameter values. This study solves a design problem with the PUMA 560 when the desired task space trajectory is given and discusses its results in the simulation part to verify the performance of the proposed design.