A parallel compact cuckoo search algorithm for three-dimensional path planning

This paper shows a modified particle swarm optimization (PSO) algorithm with multiple subpopulations for solving multimodal function optimization problems.The best particle within each subpopulation is recorded and then applied into the velocity updating formula to update all particles in each subpopulation.To show the efficiency of the proposed method, two kinds of function optimizations including a single modal function optimization and a complex multimodal function optimization are provided.Simulation results will demonstrate the convergence behavior of particles by the number of iterations, and the global and local system solutions are solved by these best particles of subpopulations. In this paper, a modified particle swarm optimization (PSO) algorithm is developed for solving multimodal function optimization problems. The difference between the proposed method and the general PSO is to split up the original single population into several subpopulations according to the order of particles. The best particle within each subpopulation is recorded and then applied into the velocity updating formula to replace the original global best particle in the whole population. To update all particles in each subpopulation, the modified velocity formula is utilized. Based on the idea of multiple subpopulations, for the multimodal function optimization the several optima including the global and local solutions may probably be found by these best particles separately. To show the efficiency of the proposed method, two kinds of function optimizations are provided, including a single modal function optimization and a complex multimodal function optimization. Simulation results will demonstrate the convergence behavior of particles by the number of iterations, and the global and local system solutions are solved by these best particles of subpopulations.

A modified particle swarm optimization with multiple subpopulations for multimodal function optimization problems

In this article, a novel approach for 2-channel linear phase quadrature mirror filter (QMF) bank design based on a hybrid of gradient based optimization and optimization of fractional derivative constraints is introduced. For the purpose of this work, recently proposed nature inspired optimization techniques such as cuckoo search (CS), modified cuckoo search (MCS) and wind driven optimization (WDO) are explored for the design of QMF bank. 2-Channel QMF is also designed with particle swarm optimization (PSO) and artificial bee colony (ABC) nature inspired optimization techniques. The design problem is formulated in frequency domain as sum of L2 norm of error in passband, stopband and transition band at quadrature frequency. The contribution of this work is the novel hybrid combination of gradient based optimization (Lagrange multiplier method) and nature inspired optimization (CS, MCS, WDO, PSO and ABC) and its usage for optimizing the design problem. Performance of the proposed method is evaluated by passband error (ϕp), stopband error (ϕs), transition band error (ϕt), peak reconstruction error (PRE), stopband attenuation (As) and computational time. The design examples illustrate the ingenuity of the proposed method. Results are also compared with the other existing algorithms, and it was found that the proposed method gives best result in terms of peak reconstruction error and transition band error while it is comparable in terms of passband and stopband error. Results show that the proposed method is successful for both lower and higher order 2-channel QMF bank design. A comparative study of various nature inspired optimization techniques is also presented, and the study singles out CS as a best QMF optimization technique.

Design of two-channel filter bank using nature inspired optimization based fractional derivative constraints.

During the last two decades, there has been substantial progress in multirate digital filters and filter banks. This includes the design of quadrature mirror filters (QMF). A two-channel QMF bank is extensively used in many signal processing fields such as subband coding of speech signal, image processing, antenna systems, design of wavelet bases, and biomedical engineering and in digital audio industry. Therefore, new efficient design techniques are being proposed by several authors in this area. This paper presents an overview of analysis and design techniques of the two-channel QMF bank. Application in the area of subband coding and future research trends are also discussed.

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Two-Channel Quadrature Mirror Filter Bank: An Overview

Design of digital IIR filter: A research survey

In this paper, a new particle swarm optimization (PSO) based method is proposed for the design of a two-channel linear phase quadrature mirror filter (QMF) bank in frequency domain. The origional particle swarm optimization technique is modified by introducing the concept of Scout Bee from Artificial Bee Colony (ABC) technique for designing a low pass prototype filter having ideal filter characteristics in the passband and stopband regions, and its magnitude response at quadrature frequency is 0.707. The design problem is formulated as a linear combination of passband error and residual stop band energy of the low pass filter, and the square error of the overall transfer function of the QMF bank at the quadrature frequency π/2, in the transition band. The design results included in the paper clearly show the improvement of the proposed PSO technique over earlier reported results.

S. M. Rafi

Papers

An improved particle swarm optimization method for multirate filter bank design

A hybrid method for designing linear-phase quadrature mirror filter bank