• 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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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.

Particle Swarm Optimization for Redundancy Allocation of Multi-level System considering Alternative Units (대안 부품을 고려한 다계층 시스템의 중복 할당을 위한 입자 군집 최적화)

  • Chung, Il Han
    • Journal of the Korean Society for Quality Management
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    • v.47 no.4
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    • pp.701-711
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    • 2019
  • Purpose: The problem of optimizing redundancy allocation in multi-level systems is considered when each item in a multi-level system has alternative items with the same function. The number of redundancy of multi-level system is allocated to maximize the reliability of the system under path set and cost limitation constraints. Methods: Based on cost limitation and path set constraints, a mathematical model is established to maximize system reliability. Particle swarm optimization is employed for redundant allocation and verified by numerical experiments. Results: Comparing the particle swarm optimization method and the memetic algorithm for the 3 and 4 level systems, the particle swarm optimization method showed better performance for solution quality and search time. Particularly, the particle swarm optimization showed much less than the memetic algorithm for variation of results. Conclusion: The proposed particle swarm optimization considerably shortens the time to search for a feasible solution in MRAP with path set constraints. PS optimization is expected to reduce search time and propose the better solution for various problems related to MRAP.

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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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.

Network Selection Algorithm for Heterogeneous Wireless Networks Based on Multi-Objective Discrete Particle Swarm Optimization

  • Zhang, Wenzhu;Kwak, Kyung-Sup;Feng, Chengxiao
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.6 no.7
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    • pp.1802-1814
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    • 2012
  • In order to guide users to select the most optimal access network in heterogeneous wireless networks, a network selection algorithm is proposed which is designed based on multi-objective discrete particle swarm optimization (Multi-Objective Discrete Particle Swarm Optimization, MODPSO). The proposed algorithm keeps fast convergence speed and strong adaptability features of the particle swarm optimization. In addition, it updates an elite set to achieve multi-objective decision-making. Meanwhile, a mutation operator is adopted to make the algorithm converge to the global optimal. Simulation results show that compared to the single-objective algorithm, the proposed algorithm can obtain the optimal combination performance and take into account both the network state and the user preferences.

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.

Feeder Reconfiguration Using Binary Coding Particle Swarm Optimization

  • Wu, Wu-Chang;Tsai, Men-Shen
    • International Journal of Control, Automation, and Systems
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    • v.6 no.4
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    • pp.488-494
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    • 2008
  • This paper proposes an effective approach based on binary coding Particle Swarm Optimization (PSO) to identify the switching operation plan for feeder reconfiguration. The proposed method considers the advantages and disadvantages of existing particle swarm optimization method and redefined the operators of PSO algorithm to fit the application field of distribution systems. Shift operator is proposed to construct the binary coding particle swarm optimization for feeder reconfiguration. A typical distribution system of Taiwan Power Company is used in this paper to demonstrate the effectiveness of the proposed method. The test results show that the proposed method can apply to feeder reconfiguration problems more effectively and stably than existing method.

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.

Applying Particle Swarm Optimization for Enhanced Clustering of DNA Chip Data (DNA Chip 데이터의 군집화 성능 향상을 위한 Particle Swarm Optimization 알고리즘의 적용기법)

  • Lee, Min-Soo
    • The KIPS Transactions:PartD
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    • v.17D no.3
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    • pp.175-184
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    • 2010
  • Experiments and research on genes have become very convenient by using DNA chips, which provide large amounts of data from various experiments. The data provided by the DNA chips could be represented as a two dimensional matrix, in which one axis represents genes and the other represents samples. By performing an efficient and good quality clustering on such data, the classification work which follows could be more efficient and accurate. In this paper, we use a bio-inspired algorithm called the Particle Swarm Optimization algorithm to propose an efficient clustering mechanism for large amounts of DNA chip data, and show through experimental results that the clustering technique using the PSO algorithm provides a faster yet good quality result compared with other existing clustering solutions.