Performance Comparison Of Genetic Algorithm Research Articles

Objective-Subjective Sound Quality Correlation Performance Comparison of Genetic Algorithm Based Regression Models and Neural Network Based Approach

Every product is growingly being evaluated in terms of acoustic characteristics. The most accurate way to rate sound quality is by performing jury tests; however, jury tests require a lot of time and human resources. To overcome this problem, jury tests results can be correlated to objective sound quality metrics owing to the fact that objective metrics could be easily obtained from sound data. In this study, advanced techniques for feature identification are explored to correlate objective metrics to subjective perception retrieved from jury tests. The data set refers to the interior noise of a regional propeller aircraft. Artificial Neural Network and two regression models (i.e. linear and quadratic regression models) have been chosen to predict subjective metrics according to the objective data. To obtain the optimized model parameters for the regression models, a Genetic Algorithm has been used as optimization strategy. In each modelling, 85 percent of sound sample data have been utilized to perform the model and remaining 15 percent have been reserved for testing the models. The results showed that the Artificial Neural Network can provide better prediction.

A Performance Comparison of Genetic Algorithm and Particle Swarm Optimization applied to TSP

A Performance Comparison of Genetic Algorithm and Particle Swarm Optimization applied to TSP

Adaptive infinite impulse response system identification using opposition based hybrid coral reefs optimization algorithm

An efficient global adaptive algorithm is required to determine the parameters of infinite impulse response (IIR) filter owing to the error cost surface of adaptive IIR system identification problem being generally nonlinear and non-differentiable. In this paper, a new bio-inspired algorithm, called opposition based hybrid coral reefs optimization algorithm (OHCRO) is applied for the IIR system identification problem. Coral reefs optimization algorithm (CRO) is a novel global algorithm, which mimics the behaviors of corals’ reproduction and coral reef formation. OHCRO is a modified version of CRO, on the one hand utilizing opposition based learning to accelerate global convergence, on the other hand cooperating with rotational direction method to enhance the local search capability. In addition, the Laplace broadcast spawning and power mutation brooding operator are used to maintain the diversity. The simulation studies have been performed for the performance comparison of genetic algorithm, particle swarm optimization and its variants, differential evolution and its variants and the proposed OHCRO for well-known benchmark examples with same order and reduced order filters. Simulation results and comparative studies justify the efficacy of the OHCRO based system identification approach in terms of convergence speed, identified coefficients and fitness values. In conclusion, OHCRO is a promising method for adaptive IIR system identification.

Performance Comparison of GA, DE, PSO and SA Approaches in Enhancement of Total Transfer Capability using FACTS Devices

In this paper the performance of meta-heuristics algorithms such as GA (Genetic Algorithm), DE (Differential Evolution), PSO (Particle Swarm Optimization) and SA (Simulated Annealing) for the problem of TTC enhancement using FACTS devices are compared. In addition to that in the assessment procedure of TTC two novel techniques are proposed. First the optimization algorithm which is used for TTC enhancement is simultaneously used for assessment of TTC. Second the power flow is done using Broyden - Shamanski method with Sherman - Morrison formula (BSS). The proposed approach is tested on WSCC 9 bus, IEEE 118 bus test systems and the results are compared with the conventional Repeated Power Flow (RPF) using Newton Raphson (NR) method which indicates that the proposed method provides better TTC enhancement and computational efficacy than the conventional procedure.

Performance comparison of genetic algorithm and principal component analysis methods for ECG signal extraction

Electrocardiogram (ECG) signal analysis is a technique to diagnose the cardiac diseases. But, the desired electrocardiogram signals are often corrupted by baseline interference, power line interference and electromyogram. Here, a method is proposed to extract ECG from noisy signals based on Singular Value Decomposition (SVD) and Genetic Algorithm. The advantage of this method compared to conventional methods like adaptive filtering, neural networks is that, it does not require any prior knowledge of the signals. It is found that the signal to noise ratio improvement is nearly double when compared to neural network methods.

Performance comparison of GA, PSO, and DE approaches in estimating low atmospheric refractivity profiles

Particles swarm optimization (PSO) and differential evolution (DE) algorithms based on optimization are employed to estimate low atmospheric refractivity profiles from radar sea clutter. Low atmospheric refractivity profiles are modeled as evaporation ducts. The objective functions, which are used to evaluate the fit of simulated and measured power in estimation procedures, are also investigated at different frequencies such as L-, S-, C- and X-frequency at 10 m/s wind speeds. The results show that all the objective functions are multi-peak functions. The Adjusted Barton Model of radar cross section (RCS) is adopted. PSO and DE algorithms are compared with genetic algorithm (GA) by 200 Monte Carlo simulation estimations. Simulation results indicate that DE has the best global search ability, and PSO has the highest success probability. According to the statistical results, PSO algorithm with the population size 30 is the appropriate way for evaporation duct estimation.

Application and comparison of intelligent optimisation techniques for SSSC-based controller design

This paper presents the application and performance comparison of Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO) optimisation techniques in designing a Static Synchronous Series Compensator (SSSC)-based controller. The design problem is formulated as an optimisation problem and both the techniques are employed to design the proposed controller. The results are presented and compared to show the effectiveness of both the techniques. It is observed that, in terms of computational time, GA approach is faster than PSO. However, PSO seems to arrive at its final parameter values in fewer generations than GA.

A comparative study of heuristic algorithms: GA and UMDA in spatially multiplexed communication systems

A performance comparison of genetic algorithm (GA) and the univariate marginal distribution algorithm (UMDA) as decoders in multiple input multiple output (MIMO) communication system is presented in this paper. While the optimal maximum likelihood (ML) decoder using an exhaustive search method is prohibitively complex, simulation results show that the GA and UMDA optimized MIMO detection algorithms result in near optimal bit error rate (BER) performance with significantly reduced computational complexity. The results also suggest that the heuristic based MIMO detection outperforms the vertical bell labs layered space time (VBLAST) detector without severely increasing the detection complexity. The performance of UMDA is found to be superior to that of GA in terms of computational complexity and the BER performance.