• 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.

• Journal of 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.

• 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.

• 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.

• Proceedings of the KIEE Conference
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• 2009.07a
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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

• Proceedings of the KIEE Conference
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• 2005.07b
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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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.

• 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.

• 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.

• 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.