• Title, Summary, Keyword: particle swarm optimization

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Visualization Tool Design for Searching Process of Particle Swarm Optimization (Particle Swarm Optimization 탐색과정의 가시화를 위한 툴 설계)

  • 유명련
    • Journal of Korea Multimedia Society
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    • v.6 no.2
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    • pp.332-339
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    • 2003
  • To solve the large scale optimization problem approximately, various approaches have been introduced. Recently the Particle Swarm Optimization has been introduced. The Particle Swarm Optimization simulates the process of birds flocking or fish schooling for food, as with the information of each agent is skated by other agents. The Particle Swarm Optimization technique has been applied to various optimization problems whose variables are continuous. However, there are seldom trials for visualization of searching process. This paper proposes a new visualization tool for searching process of Particle Swarm Optimization algorithm. The proposed tool is effective for understanding the searching process of Particle Swarm Optimization method and educational for students. The computational results can be shown tiny and very helpful for education.

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Coupling Particles Swarm Optimization for Multimodal Electromagnetic Problems

  • Pham, Minh-Trien;Baatar, Nyambayar;Koh, Chang-Seop
    • Proceedings of the KIEE Conference
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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

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EP Based PSO Method for Solving Multi Area Unit Commitment Problem with Import and Export Constraints

  • Venkatesan, K.;Selvakumar, G.;Rajan, C. Christober Asir
    • Journal of Electrical Engineering and Technology
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    • v.9 no.2
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    • pp.415-422
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    • 2014
  • This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming based particle swarm optimization (EPPSO) method. The objective of this paper is to determine the optimal or near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie lines. The evolutionary programming based particle swarm optimization method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie lines depends upon the operating cost of generation at each hour and tie line transfer limits. Case study of four areas with different load pattern each containing 7 units (NTPS) and 26 units connected via tie lines have been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based particle swarm optimization method with conventional dynamic programming (DP), evolutionary programming (EP), and particle swarm optimization (PSO) method. Experimental results show that the application of this evolutionary programming based particle swarm optimization method has the potential to solve multi area unit commitment problem with lesser computation time.

A Study on Optimization Model of Time-Cost Trade-off Analysisusing Particle Swarm Optimization (Particle Swarm Optimization을 이용한 공기-비용 절충관계 최적화 모델에 관한 연구)

  • Park, U-Yeol;An, Sung-Hoon
    • Journal of the Korea Institute of Building Construction
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    • v.8 no.6
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    • pp.91-98
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    • 2008
  • It is time-consuming and difficulty to solve the time-cost trade-off problems, as there are trade-offs between time and cost to complete the activities in construction projects and this problems do not have unique solutions. Typically, heuristic methods, mathematical models and GA models has been used to solve this problems. As heuristic methods and mathematical models are have weakness in solving the time-cost trade-off problems, GA based model has been studied widely in recent. This paper suggests the time-cost trade-off optimization algorithm using particle swarm optimization. The traditional particle swarm optimization model is modified to generate optimal tradeoffs among construction time and cost efficiently. An application example is analyzed to illustrate the use of the suggested algorithm and demonstrate its capabilities in generating optimal tradeoffs among construction time and cost. Future applications of the model are suggested in the conclusion.

Economic Dispatch Using Hybrid Particle Swarm Optimization with Prohibited Operating Zones and Ramp Rate Limit Constraints

  • Prabakaran, S.;Senthilkuma, V.;Baskar, G.
    • Journal of Electrical Engineering and Technology
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    • v.10 no.4
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    • pp.1441-1452
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    • 2015
  • This paper proposes a new Hybrid Particle Swarm Optimization (HPSO) method that integrates the Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) techniques. The proposed method is applied to solve Economic Dispatch(ED) problems considering prohibited operating zones, ramp rate limits, capacity limits and power balance constraints. In the proposed HPSO method, the best features of both EP and PSO are exploited, and it is capable of finding the most optimal solution for the non-linear optimization problems. For validating the proposed method, it has been tested on the standard three, six, fifteen and twenty unit test systems. The numerical results show that the proposed HPSO method is well suitable for solving non-linear economic dispatch problems, and it outperforms the EP, PSO and other modern metaheuristic optimization methods reported in the recent literatures.

Vibration Based Structural Damage Detection Technique using Particle Swarm Optimization with Incremental Swarm Size

  • Nanda, Bharadwaj;Maity, Damodar;Maiti, Dipak Kumar
    • International Journal of Aeronautical and Space Sciences
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    • v.13 no.3
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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.

Prewarping Techniques Using Fuzzy system and Particle Swarm Optimization (퍼지 시스템과 Particle Swarm Optimization(PSO)을 이용한 Prewarping 기술)

  • Jang, U-Seok;Gang, Hwan-Il
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.272-274
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    • 2006
  • In this paper, we concentrate on the mask design problem for optical micro-lithography. The pre-distorted mask is obtained by minimizing the error between the designed output image and the projected output image. We use the particle swarm optimization(PSO) and fuzzy system to insure that the resulting images are identical to the desired image. Our method has good performance for the iteration number by an experiment.

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Particle Swarm Optimizations to Solve Multi-Valued Discrete Problems (다수의 값을 갖는 이산적 문제에 적용되는 Particle Swarm Optimization)

  • Yim, Dong-Soon
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.36 no.3
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    • pp.63-70
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    • 2013
  • Many real world optimization problems are discrete and multi-valued. Meta heuristics including Genetic Algorithm and Particle Swarm Optimization have been effectively used to solve these multi-valued optimization problems. However, extensive comparative study on the performance of these algorithms is still required. In this study, performance of these algorithms is evaluated with multi-modal and multi-dimensional test functions. From the experimental results, it is shown that Discrete Particle Swarm Optimization (DPSO) provides better and more reliable solutions among the considered algorithms. Also, additional experiments shows that solution quality of DPSO is not lowered significantly when bit size representing a solution increases. It means that bit representation of multi-valued discrete numbers provides reliable solutions instead of becoming barrier to performance of DPSO.

A Modified Particle Swarm Optimization Algorithm : Information Diffusion PSO (새로운 위상 기반의 Particle Swarm Optimization 알고리즘 : 정보파급 PSO)

  • Park, Jun-Hyuk;Kim, Byung-In
    • Journal of Korean Institute of Industrial Engineers
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    • v.37 no.3
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    • pp.163-170
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    • 2011
  • This paper proposes a modified version of Particle Swarm Optimization (PSO) called Information Diffusion PSO (ID-PSO). In PSO algorithms, premature convergence of particles could be prevented by defining proper population topology. In this paper, we propose a variant of PSO algorithm using a new population topology. We draw inspiration from the theory of information diffusion which models the transmission of information or a rumor as one-to-one interactions between people. In ID-PSO, a particle interacts with only one particle at each iteration and they share their personal best solutions and recognized best solutions. Each particle recognizes the best solution that it has experienced or has learned from another particle as the recognized best. Computational experiments on the benchmark functions show the effectiveness of the proposed algorithm compared with the existing methods which use different population topologies.

Optimization of Fuzzy Learning Machine by Using Particle Swarm Optimization (PSO 알고리즘을 이용한 퍼지 Extreme Learning Machine 최적화)

  • Roh, Seok-Beom;Wang, Jihong;Kim, Yong-Soo;Ahn, Tae-Chon
    • Journal of Korean Institute of Intelligent Systems
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    • v.26 no.1
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    • pp.87-92
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    • 2016
  • In this paper, optimization technique such as particle swarm optimization was used to optimize the parameters of fuzzy Extreme Learning Machine. While the learning speed of conventional neural networks is very slow, that of Extreme Learning Machine is very fast. Fuzzy Extreme Learning Machine is composed of the Extreme Learning Machine with very fast learning speed and fuzzy logic which can represent the linguistic information of the field experts. The general sigmoid function is used for the activation function of Extreme Learning Machine. However, the activation function of Fuzzy Extreme Learning Machine is the membership function which is defined in the procedure of fuzzy C-Means clustering algorithm. We optimize the parameters of the membership functions by using optimization technique such as Particle Swarm Optimization. In order to validate the classification capability of the proposed classifier, we make several experiments with the various machine learning datas.