• Title/Summary/Keyword: Particle Swarm Algorithm

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Blind Audio Source Separation Based On High Exploration Particle Swarm Optimization

  • KHALFA, Ali;AMARDJIA, Nourredine;KENANE, Elhadi;CHIKOUCHE, Djamel;ATTIA, Abdelouahab
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.5
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    • pp.2574-2587
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    • 2019
  • Blind Source Separation (BSS) is a technique used to separate supposed independent sources of signals from a given set of observations. In this paper, the High Exploration Particle Swarm Optimization (HEPSO) algorithm, which is an enhancement of the Particle Swarm Optimization (PSO) algorithm, has been used to separate a set of source signals. Compared to PSO algorithm, HEPSO algorithm depends on two additional operators. The first operator is based on the multi-crossover mechanism of the genetic algorithm while the second one relies on the bee colony mechanism. Both operators have been employed to update the velocity and the position of the particles respectively. Thus, they are used to find the optimal separating matrix. The proposed method enhances the overall efficiency of the standard PSO in terms of good exploration and performance. Based on many tests realized on speech and music signals supplied by the BSS demo, experimental results confirm the robustness and the accuracy of the introduced BSS technique.

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.

A Study on Wall Emissivity Estimation using RPSO Algorithm (RPSO 알고리즘을 이용한 벽면 방사율 추정에 관한 연구)

  • Lee, Kyun-Ho;Baek, Seung-Wook;Kim, Ki-Wan;Kim, Man-Young
    • Proceedings of the KSME Conference
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    • 2007.05b
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    • pp.2476-2481
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    • 2007
  • An inverse radiation analysis is presented for the estimation of the wall emissivities for an absorbing, emitting, and scattering media with diffusely emitting and reflecting opaque boundaries. In this study, a repulsive particle swarm optimization(RPSO) algorithm which is a relatively recent heuristic search method is proposed as an effective method for improving the search efficiency for unknown parameters. To verify the performance of the proposed RPSO algorithm, it is compared with a basic particle swarm optimization(PSO) algorithm and a hybrid genetic algorithm(HGA) for the inverse radiation problem with estimating the wall emissivities in a two-dimensional irregular medium when the measured temperatures are given at only four data positions. A finite-volume method is applied to solve the radiative transfer equation of a direct problem to obtain measured temperatures.

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Optimal Capacitor Placement Considering Voltage-stability Margin with Hybrid Particle Swarm Optimization

  • Kim, Tae-Gyun;Lee, Byong-Jun;Song, Hwa-Chang
    • Journal of Electrical Engineering and Technology
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    • v.6 no.6
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    • pp.786-792
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    • 2011
  • The present paper presents an optimal capacitor placement (OCP) algorithm for voltagestability enhancement. The OCP issue is represented using a mixed-integer problem and a highly nonlinear problem. The hybrid particle swarm optimization (HPSO) algorithm is proposed to solve the OCP problem. The HPSO algorithm combines the optimal power flow (OPF) with the primal-dual interior-point method (PDIPM) and ordinary PSO. It takes advantage of the global search ability of PSO and the very fast simulation running time of the OPF algorithm with PDIPM. In addition, OPF gives intelligence to PSO through the information provided by the dual variable of the OPF. Numerical results illustrate that the HPSO algorithm can improve the accuracy and reduce the simulation running time. Test results evaluated with the three-bus, New England 39-bus, and Korea Electric Power Corporation systems show the applicability of the proposed algorithm.

Particle Swarm Assisted Genetic Algorithm for the Optimal Design of Flexbeam Sections

  • Dhadwal, Manoj Kumar;Lim, Kyu Baek;Jung, Sung Nam;Kim, Tae Joo
    • International Journal of Aeronautical and Space Sciences
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    • v.14 no.4
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    • pp.341-349
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    • 2013
  • This paper considers the optimum design of flexbeam cross-sections for a full-scale bearingless helicopter rotor, using an efficient hybrid optimization algorithm based on particle swarm optimization, and an improved genetic algorithm, with an effective constraint handling scheme for constrained nonlinear optimization. The basic operators of the genetic algorithm, of crossover and mutation, are revisited, and a new rank-based multi-parent crossover operator is utilized. The rank-based crossover operator simultaneously enhances both the local, and the global exploration. The benchmark results demonstrate remarkable improvements, in terms of efficiency and robustness, as compared to other state-of-the-art algorithms. The developed algorithm is adopted for two baseline flexbeam section designs, and optimum cross-section configurations are obtained with less function evaluations, and less computation time.

A new PSRO algorithm for frequency constraint truss shape and size optimization

  • Kaveh, A.;Zolghadr, A.
    • Structural Engineering and Mechanics
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    • v.52 no.3
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    • pp.445-468
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    • 2014
  • In this paper a new particle swarm ray optimization algorithm is proposed for truss shape and size optimization with natural frequency constraints. These problems are believed to represent nonlinear and non-convex search spaces with several local optima and therefore are suitable for examining the capabilities of new algorithms. The proposed algorithm can be viewed as a hybridization of Particle Swarm Optimization (PSO) and the recently proposed Ray Optimization (RO) algorithms. In fact the exploration capabilities of the PSO are tried to be promoted using some concepts of the RO. Five numerical examples are examined in order to inspect the viability of the proposed algorithm. The results are compared with those of the PSO and some other existing algorithms. It is shown that the proposed algorithm obtains lighter structures in comparison to other methods most of the time. As will be discussed, the algorithm's performance can be attributed to its appropriate exploration/exploitation balance.

A Study on a Gain-Enhanced Antenna for Energy Harvesting using Adaptive Particle Swarm Optimization

  • Kang, Seong-In;Kim, Koon-Tae;Lee, Seung-Jae;Kim, Jeong-Phill;Choi, Kyung;Kim, Hyeong-Seok
    • Journal of Electrical Engineering and Technology
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    • v.10 no.4
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    • pp.1780-1785
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    • 2015
  • In this paper, the adaptive particle swarm optimization (APSO) algorithm is employed to design a gain-enhanced antenna with a reflector for energy harvesting. We placed the reflector below the main radiating element. Its back-radiated field is reflected and added to the forward radiated field, which could increase the antenna gain. We adopt the adaptive particle swarm optimization (APSO) algorithm, which improves the speed of convergence with a high frequency solver. The result shows that performance of the optimized design successfully satisfied the design goal of the frequency band, gain and axial ratio.

Structural Design of Optimized Fuzzy Inference System Based on Particle Swarm Optimization (입자군집 최적화에 기초한 최적 퍼지추론 시스템의 구조설계)

  • Kim, Wook-Dong;Lee, Dong-Jin;Oh, Sung-Kwun
    • Proceedings of the IEEK Conference
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    • 2009.05a
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    • pp.384-386
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    • 2009
  • This paper introduces an effectively optimized Fuzzy model identification by means of complex and nonlinear system applying PSO algorithm. In other words, we use PSO(Particle Swarm Optimization) for identification of Fuzzy model structure and parameter. PSO is an algorithm that follows a collaborative population-based search model. Each particle of swarm flies around in a multidimensional search space looking for the optimal solution. Then, Particles adjust their position according to their own and their neighboring-particles experience. This paper identifies the premise part parameters and the consequence structures that have many effects on Fuzzy system based on PSO. In the premise parts of the rules, we use triangular. Finally we evaluate the Fuzzy model that is widely used in the standard model of gas data and sew data.

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Harmonic Elimination in Three-Phase Voltage Source Inverters by Particle Swarm Optimization

  • Azab, Mohamed
    • Journal of Electrical Engineering and Technology
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    • v.6 no.3
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    • pp.334-341
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    • 2011
  • This paper presents accurate solutions for nonlinear transcendental equations of the selective harmonic elimination technique used in three-phase PWM inverters feeding the induction motor by particle swarm optimization (PSO). With the proposed approach, the required switching angles are computed efficiently to eliminate low order harmonics up to the $23^{rd}$ from the inverter voltage waveform, whereas the magnitude of the fundamental component is controlled to the desired value. A set of solutions and the evaluation of the proposed method are presented. The obtained results prove that the algorithm converges to a precise solution after several iterations. The salient contribution of the paper is the application of the particle swarm algorithm to attenuate successfully any undesired loworder harmonics from the inverter output voltage. The current paper demonstrates that the PSO is a promising approach to control the operation of a three-phase voltage source inverter with a selective harmonic elimination strategy to be applied in induction motor drives.

Swarm Intelligence-based Power Allocation and Relay Selection Algorithm for wireless cooperative network

  • Xing, Yaxin;Chen, Yueyun;Lv, Chen;Gong, Zheng;Xu, Ling
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.3
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    • pp.1111-1130
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    • 2016
  • Cooperative communications can significantly improve the wireless transmission performance with the help of relay nodes. In cooperative communication networks, relay selection and power allocation are two key issues. In this paper, we propose a relay selection and power allocation scheme RS-PA-PSACO (Relay Selection-Power Allocation-Particle Swarm Ant Colony Optimization) based on PSACO (Particle Swarm Ant Colony Optimization) algorithm. This scheme can effectively reduce the computational complexity and select the optimal relay nodes. As one of the swarm intelligence algorithms, PSACO which combined both PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) algorithms is effective to solve non-linear optimization problems through a fast global search at a low cost. The proposed RS-PA-PSACO algorithm can simultaneously obtain the optimal solutions of relay selection and power allocation to minimize the SER (Symbol Error Rate) with a fixed total power constraint both in AF (Amplify and Forward) and DF (Decode and Forward) modes. Simulation results show that the proposed scheme improves the system performance significantly both in reliability and power efficiency at a low complexity.