Jehad Ababneh

Linear phase FIR filter design using particle swarm optimization and genetic algorithms

In this paper, a linear phase FIR filter is designed using particle swarm optimization (PSO) and genetic algorithms (GA). Two design cases are considered. In the first case, the filter length, passband and stopband frequencies, and the ratio of the passband and stopband ripples size are specified. In the second case, a feasible passband and stopband ripples size in addition to the other three filter specifications are specified. The later situation is not explicitly considered by the Parks-McClellan (PM) algorithm. Furthermore, the PSO and the GA are used to design optimum FIR filters for which the filter coefficients are represented using finite word length. In all cases, the design goal is successfully achieved using the PSO and compared with that obtained using the GA. For the problem at hand, it is found that the PSO outperforms the GA in some of the presented design cases.

Synthesis of Aperiodic Linear Phased Antenna Arrays Using Particle Swarm Optimization

In this article, the particle swarm optimization (PSO) algorithm is used to synthesize an optimal linear array. The technique is applied to synthesize a linear antenna array in the Chebyshev sense or to eliminate a grating lobe. This is may be achieved by optimizing the excitation currents and/or the relative locations of the array elements. These various design parameters are considered in this paper. In synthesizing equiripple radiation patterns, two approaches were used. The excitation currents feeding the array or the spacing between the array elements are optimized. It is to be noted that the desired equal side lobes level is achieved simultaneously with the narrowest possible beamwidth. Although the optimization problem may become nonlinear, convex, or nonconvex, especially if the interelement distances are the optimized parameters, it can be handled using the PSO algorithm. The PSO is simple to implement and does not require evaluation of gradients or coded parameters. In order to effectively utilize this algorithm, it is important to define an objective function that returns a single number to enable the PSO algorithm to minimize it. In this paper, the objective function is formulated to take into consideration both the lobe level and the main beam width. The results obtained using the particle swarm optimization technique are in excellent agreement with those available in the literature.

Simple model for quantum-dot semiconductor optical amplifiers using artificial neural networks

A simple, accurate, and fast multilayer feedforward artificial neural network (ANN) model for quantum-dot semiconductor optical amplifiers (QD-SOAs) is developed. The developed ANN model has demonstrated excellent precision and accurately models the physical characteristics of QD-SOAs such as pulse amplification and four-wave mixing characteristics. Furthermore, the developed ANN model requires a very small computational time compared with numerical models, which is very attractive for computer-aided-design applications. The model is used to create very interesting design curves for QD-SOAs. In addition, the model is suitable for physical-parameter extraction from available measured data

Minimum bit error rate multiuser detection of SDMA-OFDM system using differential evolutionary algorithm

Space division multiple access aided orthogonal frequency division multiplexing (SDMA-OFDM) is a promising technique for high data rate future wireless communications. In this paper, a minimum bit error rate (MBER) differential evolution (DE) algorithm based multiuser detector (MUD) for SDMA-OFDM system is proposed. The proposed algorithm directly minimizes the bit error rate (BER) cost function by selecting the optimum weight vectors. Simulation results show that the proposed DE based MUD outperforms the minimum mean-squared error (MMSE) based MUD in terms of the achievable BER. Simulation results also show that the performance of the DE based MUD is comparable to that of the particle swarm optimization (PSO) based MUD.

Greedy particle swarm and biogeography-based optimization algorithm

Purpose – The purpose of this paper is to propose an algorithm that combines the particle swarm optimization (PSO) with the biogeography-based optimization (BBO) algorithm. Design/methodology/approach – The BBO and the PSO algorithms are jointly used in to order to combine the advantages of both algorithms. The efficiency of the proposed algorithm is tested using some selected standard benchmark functions. The performance of the proposed algorithm is compared with that of the differential evolutionary (DE), genetic algorithm (GA), PSO, BBO, blended BBO and hybrid BBO-DE algorithms. Findings – Experimental results indicate that the proposed algorithm outperforms the BBO, PSO, DE, GA, and the blended BBO algorithms and has comparable performance to that of the hybrid BBO-DE algorithm. However, the proposed algorithm is simpler than the BBO-DE algorithm since the PSO does not have complex operations such as mutation and crossover used in the DE algorithm. Originality/value – The proposed algorithm is a gener...

Solving independent component analysis contrast functions with particle swarm optimization

Independent Component Analysis (ICA) is a statistical computation method that transforms a random vector in another one whose components are independent. Because the marginal distributions are usually unknown, the final problem is reduced to an optimization of a contrast function, a function that measures the independence of the components. In this paper, the stochastic global Particle Swarm Optimization (PSO) algorithm is used to solve the optimization problem. The PSO is used to separate some selected benchmarks signals based on two different contrast functions. The results obtained using the PSO are compared with classical ICA algorithms. It is shown that the PSO is a more powerful and robust technique and capable of finding the original signals or sources when classical ICA algorithms give poor results or fail to converge.

Using particle swarm optimization for minimizing mutual information in independent component analysis

Independent Component Analysis (ICA) aims to recover a set of independent random variables starting from observations that are a mixture of them. Since the prior knowledge of the marginal distributions is unknown with the only restriction of at most one Gaussian component, the problem is usually formulated as an optimization one, where the goal is the maximization (minimization) of a cost function that in the optimal value approximates the statistical independence hypothesis. In this paper, we consider the ICA contrast function based on the mutual information. The stochastic global Particle Swarm Optimization (PSO) algorithm is used to solve the optimization problem. PSO is an evolutionary algorithm where the potential solutions, called particles, fly through the problem space by following the current optimum particles. It has the advantage that it works for non-differentiable functions and when no gradient information is available, providing a simple implementation with few parameters to adjust. We apply successfully PSO to separate some selected benchmarks signals.