Attractive And Repulsive Particle Swarm Optimization Research Articles

Digital FIR Filter Design by PSO and its v ariants Attractive and Repulsive PSO(ARPSO) & Craziness based PSO(CRPSO)

Digital filters play a major role in signal processing that are employed in many applications such as in control systems, audio or video processing systems, noise reduction applications and different systems for communication. In this regard, FIR filters are employed because of frequency stability and linearity in their phase response. FIR filter design requires multi-modal optimization problems. Therefore, PSO (Particle Swarm Optimization) algorithm and its variants are more adaptable techniques based upon particles’ population in the search space and a great option for designing FIR filter. PSO and its different variants improve the solution characteristic by providing a unique approach for updating the velocity and position of the swarm. An optimized set of filter coefficient is produced by PSO and its variant algorithms which gives the optimized results in passband and stopband. In this research paper, Digital FIR filter is effectively designed by using PSO Algorithm and its two variants ARPSO and CRPSO in MATLAB. The outcomes prove that the filter design technique using CRPSO is better than filter design by PM algorithm. PSO and ARPSO Algorithms in the context of frequency spectrum and RMS error.

An Improved Ensemble of Random Vector Functional Link Networks Based on Particle Swarm Optimization with Double Optimization Strategy.

For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is one of suitable base-classifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In this paper, to obtain a more compact ensemble system with improved convergence performance, an improved ensemble of RVFL based on attractive and repulsive particle swarm optimization (ARPSO) with double optimization strategy is proposed. In the proposed method, ARPSO is applied to select and combine the candidate RVFL. As for using ARPSO to select the optimal base RVFL, ARPSO considers both the convergence accuracy on the validation data and the diversity of the candidate ensemble system to build the RVFL ensembles. In the process of combining RVFL, the ensemble weights corresponding to the base RVFL are initialized by the minimum norm least-square method and then further optimized by ARPSO. Finally, a few redundant RVFL is pruned, and thus the more compact ensemble of RVFL is obtained. Moreover, in this paper, theoretical analysis and justification on how to prune the base classifiers on classification problem is presented, and a simple and practically feasible strategy for pruning redundant base classifiers on both classification and regression problems is proposed. Since the double optimization is performed on the basis of the single optimization, the ensemble of RVFL built by the proposed method outperforms that built by some single optimization methods. Experiment results on function approximation and classification problems verify that the proposed method could improve its convergence accuracy as well as reduce the complexity of the ensemble system.

An Improved Attractive and Repulsive Particle Swarm Optimization for Nonconvex Economic Dispatch Problems

Abstract: This paper presents an improved attractive and repulsive particle swarm optimization (ARPSO) algorithm for nonconvex economic dispatch problem. The ARPSO algorithm enhances the exploration and exploitation behaviors of a particle by observing a diversity factor. This paper develops an improved ARPSO by introducing a penalty factor that forces each particle to repulse from the global worst particle. The advantage of the improved ARPSO is demonstrated numerically in comparison with the basic PSO and other variation of ARPSO.

Inversion of particle size distribution by spectral extinction technique using the attractive and repulsive particle swarm optimization algorithm

The particle size distribution (PSD) plays an important role in environmental pollution detection and human health protection, such as fog, haze and soot. In this study, the Attractive and Repulsive Particle Swarm Optimization (ARPSO) algorithm and the basic PSO were applied to retrieve the PSD. The spectral extinction technique coupled with the Anomalous Diffraction Approximation (ADA) and the Lambert-Beer Law were employed to investigate the retrieval of the PSD. Three commonly used monomodal PSDs, i.e. the Rosin-Rammer (R-R) distribution, the normal (N-N) distribution, the logarithmic normal (L-N) distribution were studied in the dependent model. Then, an optimal wavelengths selection algorithm was proposed. To study the accuracy and robustness of the inverse results, some characteristic parameters were employed. The research revealed that the ARPSO showed more accurate and faster convergence rate than the basic PSO, even with random measurement error. Moreover, the investigation also demonstrated that the inverse results of four incident laser wavelengths showed more accurate and robust than those of two wavelengths. The research also found that if increasing the interval of the selected incident laser wavelengths, inverse results would show more accurate, even in the presence of random error.

Optimal design of truss-structures using particle swarm optimization

A two-stage particle swarm optimization was utilized in this study to solve truss-structure optimization problem achieving minimum weight objective under stress, deflection, and kinematic stability constraints. The topologies of the truss-structure were optimized first from a given ground structure employing the modified binary particle swarm optimization (BPSO), and subsequently the size and shape of members were optimized utilizing the attractive and repulsive particle swarm optimization (ARPSO). The effectiveness of the proposed methodology was evaluated through a two-tier, 39-member, 12-node ground structure problem. It was observed that the proposed methodology can find superior truss structures than those reported in the literatures.

A new evolutionary computation technique for economic dispatch with security constraints

This paper presents an efficient and reliable evolutionary based approach to solve the economic load dispatch (ELD) with security constraints. A new approach is proposed which employs attractive and repulsive particle swarm optimization (ARPSO) algorithm for ELD. Incorporation of ARPSO as a derivative-free optimization technique in solving ELD with security (voltages and line-flows) constraints significantly relieves the assumptions imposed on the optimized objective function. The proposed approach has been implemented on three representative systems, i.e. IEEE 14 bus, IEEE 30 bus and IEEE 57 bus systems, respectively. The feasibility of the proposed method is demonstrated and the results are compared with linear programming, quadratic programming and genetic algorithm, respectively. The premature convergence problem, that is common in all evolutionary computation techniques, is solved in ARPSO by including the diversity factor in the Type 1 PSO algorithm. The developed algorithms are computationally faster (in terms of the number of load flows carried out) than the other methods because only one run is required.