Gene selection algorithms for microarray data based on least squares support vector machine

BACKGROUND: In discriminant analysis of microarray data, usually a small number of samples are expressed by a large number of genes. It is not only difficult but also unnecessary to conduct the discriminant analysis with all the genes. Hence, gene selection is usually performed to select important genes. RESULTS: A gene selection method searches for an optimal or near optimal subset of genes with respect to a given evaluation criterion. In this paper, we propose a new evaluation criterion, named the leave-one-out calculation (LOOC, A list of abbreviations appears just above the list of references) measure. A gene selection method, named leave-one-out calculation sequential forward selection (LOOCSFS) algorithm, is then presented by combining the LOOC measure with the sequential forward selection scheme. Further, a novel gene selection algorithm, the gradient-based leave-one-out gene selection (GLGS) algorithm, is also proposed. Both of the gene selection algorithms originate from an efficient and exact calculation of the leave-one-out cross-validation error of the least squares support vector machine (LS-SVM). The proposed approaches are applied to two microarray datasets and compared to other well-known gene selection methods using codes available from the second author. CONCLUSION: The proposed gene selection approaches can provide gene subsets leading to more accurate classification results, while their computational complexity is comparable to the existing methods. The GLGS algorithm can also better scale to datasets with a very large number of genes

A machine learning approach for the identification of odorant binding proteins from sequence-derived properties

Background: Odorant binding proteins (OBPs) are believed to shuttle odorants from the environment to the underlying odorant receptors, for which they could potentially serve as odorant presenters. Although several sequence based search methods have been exploited for protein family prediction, less effort has been devoted to the prediction of OBPs from sequence data and this area is more challenging due to poor sequence identity between these proteins. Results: In this paper, we propose a new algorithm that uses Regularized Least Squares Classifier (RLSC) in conjunction with multiple physicochemical properties of amino acids to predict odorantbinding proteins. The algorithm was applied to the dataset derived from Pfam and GenDiS database and we obtained overall prediction accuracy of 97.7% (94.5% and 98.4% for positive and negative classes respectively). Conclusion: Our study suggests that RLSC is potentially useful for predicting the odorant binding proteins from sequence-derived properties irrespective of sequence similarity. Our method predicts 92.8% of 56 odorant binding proteins non-homologous to any protein in the swissprot database and 97.1% of the 414 independent dataset proteins, suggesting the usefulness of RLSC method for facilitating the prediction of odorant binding proteins from sequence information

Biochemical systems identification by a random drift particle swarm optimization approach

BACKGROUND: Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for signalling pathways. The problem is stated as a data-driven nonlinear regression problem, which is converted into a nonlinear programming problem with many nonlinear differential and algebraic constraints. Due to the typical ill conditioning and multimodality nature of the problem, it is in general difficult for gradient-based local optimization methods to obtain satisfactory solutions. To surmount this limitation, many stochastic optimization methods have been employed to find the global solution of the problem. RESULTS: This paper presents an effective search strategy for a particle swarm optimization (PSO) algorithm that enhances the ability of the algorithm for estimating the parameters of complex dynamic biochemical pathways. The proposed algorithm is a new variant of random drift particle swarm optimization (RDPSO), which is used to solve the above mentioned inverse problem and compared with other well known stochastic optimization methods. Two case studies on estimating the parameters of two nonlinear biochemical dynamic models have been taken as benchmarks, under both the noise-free and noisy simulation data scenarios. CONCLUSIONS: The experimental results show that the novel variant of RDPSO algorithm is able to successfully solve the problem and obtain solutions of better quality than other global optimization methods used for finding the solution to the inverse problems in this study

Electricity load demand time series forecasting with Empirical Mode Decomposition based Random Vector Functional Link network

Short-term electricity load demand forecasting is a critical process in the management of modern power system. An ensemble method composed of Empirical Mode Decomposition (EMD) and Random Vector Functional Link network (RVFL) is presented in this paper. Due to the randomly generated weights between input and hidden layers and the close form solution for parameter tuning, RVFL network is a universal approximator with the advantages of fast training. By introducing ensemble approach via EMD into RVFL network, the performance can be significantly improved. Five electricity load demand datasets from Australian Energy Market Operator (AEMO) were used to evaluate the performance of the proposed method. The attractiveness of the proposed EMD based RVFL network can be demonstrated by the comparison with six benchmark methods

Minimizing harmonic distortion in power system with optimal design of hybrid active power filter using differential evolution

Hybrid active power filter (HAPF) is an advanced form of harmonic filter combining advantages of both active and passive filters. In HAPF, selection of active filter gain, passive inductive and capacitive reactances, while satisfying system constraints on individual and overall voltage and current harmonic distortion levels, is the main challenge. To optimize HAPF parameters, this paper proposes an approach based on differential evolution (DE) algorithm called L-SHADE. SHADE is the success history based parameter adaptation technique of DE optimization process for a constrained, multimodal non-linear objective function. L-SHADE improves the performance of SHADE with linearly reducing the population size in successive generations. The study herein considers two frequently used topologies of HAPF for parameter estimation. A single objective function consisting of both total voltage harmonic distortion (VTHD) and total current harmonic distortion (ITHD) is formulated and finally harmonic pollution (HP) is minimized in a system comprising of both non-linear source and non-linear loads. Several case studies of a selected industrial plant are performed. The output results of L-SHADE algorithm are compared with a similar past study and also with other well-known evolutionary algorithms

Optimal placement of wind turbines in a windfarm using L-SHADE algorithm

Setting of turbines in a windfarm is a complex task as several factors need to be taken into consideration. During recent years, researchers have applied various evolutionary algorithms to windfarm layout problem by converting it to a single objective and at the most two objective optimization problem. The prime factor governing placement of turbines is the wake effect attributed to the loss of kinetic energy by wind after it passes over a turbine. Downstream turbine inside the wake region generates less output power. Optimizing the wake loss helps extract more power out of the wind. The cost of turbine is tactically entwined with generated output to form single objective of cost per unit of output power e.g. cost/kW. This paper proposes an application of L-SHADE algorithm, an advanced form of Differential Evolution (DE) algorithm, to minimize the objective cost/kW. SHADE is a success history based parameter adaptation technique of DE. L-SHADE improves the performance of SHADE with linearly reducing the population size in successive generations. DE has historically been used mainly for optimization of continuous variables. The present study suggests an approach of using algorithm L-SHADE in discrete location optimization problem. Case studies of varying wind directions with constant and variable wind speeds have been performed and results are compared with some of the previous studies

Optimization of wind turbine rotor diameters and hub heights in a windfarm using differential evolution algorithm

In this paper some of the optimized wind turbine layouts in a windfarm, as presented by many authors, have been chosen as basis for further evaluation and study. The objective of most of earlier studies was to minimize cost per kW of power produced. This paper focuses from different perspective of optimization of turbine rotor diameters and hub heights to improve overall windfarm efficiency at a reduced cost. Differential Evolution (DE) algorithm is employed to optimize rotor diameters and hub heights of turbines in a windfarm

Optimal power flow solutions incorporating stochastic wind and solar power

Generations from several sources in an electrical network are to be optimally scheduled for economical and efficient operation of the network. Optimal power flow problem is formulated with all relevant system parameters including generator outputs and solved subsequently to obtain the optimal settings. The network may consist of conventional fossil fuel generators as well as renewable sources like wind power generators and solar photovoltaic. Classical optimal power flow itself is a highly non-linear complex problem with non-linear constraints. Incorporating intermittent nature of solar and wind energy escalates the complexity of the problem. This paper proposes an approach to solve optimal power flow combining stochastic wind and solar power with conventional thermal power generators in the system. Weibull and lognormal probability distribution functions are used for forecasting wind and solar photovoltaic power output respectively. The objective function considers reserve cost for overestimation and penalty cost for underestimation of intermittent renewable sources. Besides, emission factor is also included in objectives of selected case studies. Success history based adaptation technique of differential evolution algorithm is adopted for the optimization problem. To handle various constraints in the problem, superiority of feasible solutions constraint handling technique is integrated with success history based adaptive differential evolution algorithm. The algorithm thus combined and constructed gives optimum results satisfying all network constraints

Short-term Electricity Price Forecasting with Empirical Mode Decomposition based Ensemble Kernel Machines

Short-term electricity price forecasting is a critical issue for the operation of both electricity markets and power systems. An ensemble method composed of Empirical Mode Decomposition (EMD), Kernel Ridge Regression (KRR) and Support Vector Regression (SVR) is presented in this paper. For this purpose, the electricity price signal was first decomposed into several intrinsic mode functions (IMFs) by EMD, followed by a KRR which was used to model each extracted IMF and predict the tendencies. Finally, the prediction results of all IMFs were combined by an SVR to obtain an aggregated output for electricity price. The electricity price datasets from Australian Energy Market Operator (AEMO) are used to test the effectiveness of the proposed EMD-KRR-SVR approach. Simulation results demonstrated attractiveness of the proposed method based on both accuracy and efficiency

Decomposition based multi-objective evolutionary algorithm for windfarm layout optimization

An efficient windfarm layout to harness maximum power out of the wind is highly desirable from technical and commercial perspectives. A bit of flexibility on layout gives leeway to the designer of windfarm in planning facilities for erection, installation and future maintenance. This paper proposes an approach where several options of optimized usable windfarm layouts can be obtained in a single run of decomposition based multi-objective evolutionary algorithm (MOEA/D). A set of Pareto optimal vectors is obtained with objective as maximum output power at minimum wake loss i.e. at maximum efficiency. Maximization of both output power and windfarm efficiency are set as two objectives for optimization. The objectives thus formulated ensure that in any single Pareto optimal solution the number of turbines used are placed at most optimum locations in the windfarm to extract maximum power available in the wind. Case studies with actual manufacturer data for wind turbines of same as well as different hub heights and with realistic wind data are performed under the scope of this research study