• Title, Summary, Keyword: Particle swarm optimization (PSO)

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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.

The Strategies for Exploring Various Regions and Recognizing Local Minimum of Particle Swarm Optimization (PSO의 다양한 영역 탐색과 지역적 미니멈 인식을 위한 전략)

  • Lee, Young-Ah;Kim, Tack-Hun;Yang, Sung-Bong
    • The KIPS Transactions:PartB
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    • v.16B no.4
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    • pp.319-326
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    • 2009
  • PSO(Particle Swarm Optimization) is an optimization algorithm in which simple particles search an optimal solution using shared information acquired through their own experiences. PSO applications are so numerous and diverse. Lots of researches have been made mainly on the parameter settings, topology, particle's movement in order to achieve fast convergence to proper regions of search space for optimization. In standard PSO, since each particle uses only information of its and best neighbor, swarm does not explore diverse regions and intended to premature to local optima. In this paper, we propose a new particle's movement strategy in order to explore diverse regions of search space. The strategy is that each particle moves according to relative weights of several better neighbors. The strategy of exploring diverse regions is effective and produces less local optimizations and accelerating of the optimization speed and higher success rates than standard PSO. Also, in order to raise success rates, we propose a strategy for checking whether swarm falls into local optimum. The new PSO algorithm with these two strategies shows the improvement in the search speed and success rate in the test of benchmark functions.

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.

Footstep Planning of Biped Robot Using Particle Swarm Optimization (PSO를 이용한 이족보행로봇의 보행 계획)

  • Kim, Seung-Seok;Kim, Yong-Tae
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.86-90
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    • 2007
  • 본 논문에서는 Particle Swarm Optimization(PSO) 기법을 이용한 이족보행로봇의 보행 계획방법을 제안한다. 이족보행로봇의 보행 프리미티브를 기반으로 PSO의 학습 및 군집 특성을 이용하여 장애물이 있는 작업공간에서 보행 계획을 수행하였다. 먼저 PSO의 탐색알고리즘을 사용하여 장애물을 회피하는 실행 가능한 보행 프리미티브들의 순서를 찾아내고 탐색된 순서를 바탕으로 경로 최적화 알고리즘을 수행하는 보행 계획방법을 제안하였다. 제안된 PSO 기반 이족보행로봇의 보행 계획방법은 모의실험을 통하여 발걸음 탐색 시간이 줄고 최적화된 보행 경로를 생성하는 것을 검증하였다.

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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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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.

Design of 2-D IIR Digital Filters Based on a Particle Swam Optimization (Particle Swarm Optimization을 이용한 2차원 IIR 디지털필터의 설계)

  • Lee, Young-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.13 no.7
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    • pp.1312-1320
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    • 2009
  • This paper presents an efficient design method of 2-D infinite impulse response(IIR) digital filter based on a particle swarm optimization(PSO) algorithm. The design task is reformulated as a constrained minimization problem and is solved by our newly developed PSO algorithm. To ensure the stability of the designed 2-D IIR digital filters, a new stability strategy is embedded in the basic PSO algorithm. The superiority of the proposed method is demonstrated by several experiments. The results show that the approximation error of the resultant filters are better than those of the digital filters which designed by recently published filter design methods. The proposed design method can also obtain the stable2-D IIR digital filters.

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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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.

Performance Comparison of Discrete Particle Swarm Optimizations in Sequencing Problems (순서화 문제에서 01산적 Particle Swarm Optimization들의 성능 비교)

  • Yim, D.S.
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.33 no.4
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    • pp.58-68
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    • 2010
  • Particle Swarm Optimization (PSO) which has been well known to solve continuous problems can be applied to discrete combinatorial problems. Several DPSO (Discrete Particle Swarm Optimization) algorithms have been proposed to solve discrete problems such as traveling salesman, vehicle routing, and flow shop scheduling problems. They are different in representation of position and velocity vectors, operation mechanisms for updating vectors. In this paper, the performance of 5 DPSOs is analyzed by applying to traditional Traveling Salesman Problems. The experiment shows that DPSOs are comparable or superior to a genetic algorithm (GA). Also, hybrid PSO combined with local optimization (i.e., 2-OPT) provides much improved solutions. Since DPSO requires more computation time compared with GA, however, the performance of hybrid DPSO is not better than hybrid GA.