Valadi K. Jayaraman

Particle swarm and ant colony algorithms hybridized for improved continuous optimization

This paper proposes PSACO (particle swarm ant colony optimization) algorithm for highly non-convex optimization problems. Both particle swarm optimization (PSO) and ant colony optimization (ACO) are co-operative, population-based global search swarm intelligence metaheuristics. PSO is inspired by social behavior of bird flocking or fish schooling, while ACO imitates foraging behavior of real life ants. In this study, we explore a simple pheromone-guided mechanism to improve the performance of PSO method for optimization of multimodal continuous functions. The proposed PSACO algorithm is tested on several benchmark functions from the usual literature. Numerical results comparisons with different metaheuristics demonstrate the effectiveness and efficiency of the proposed PSACO method.

Ant colony framework for optimal design and scheduling of batch plants

This paper presents a new co-operative search approach, the ant colony optimisation paradigm, for the optimal design of batch chemical processes and illustrates it by solving (1) the combinatorial optimisation problem of multiproduct batch scheduling and (2) the continuous function optimisation problem for the design of multiproduct batch plant with single product campaigns and horizon constraints. The ant algorithm is simple to implement and results of the case studies show its ability to provide speedy and accurate solutions.

Using pseudo amino acid composition to predict protein subnuclear localization: Approached with PSSM

Identification of Nuclear protein localization assumes significance as it can provide in depth insight for genome regulation and function annotation of novel proteins. A multiclass SVM classifier with various input features was employed for nuclear protein compartment identification. The input features include factor solution scores and evolutionary information (position specific scoring matrix (PSSM) score) apart from conventional dipeptide composition and pseudo amino acid composition. All the SVM classifiers with different sets of input features performed better than the previously available prediction classifiers. The jack-knife success rate thus obtained on the benchmark dataset constructed by Shen and Chou [Shen, H.B., Chou, K.C., 2005, Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition. Biochem. Biophys. Res. Commun. 337, 752-756] is 71.23%, indicating that the novel pseudo amino acid composition approach with PSSM and SVM classifier is very promising and may at least play a complimentary role to the existing methods.

A support vector machine-based method for predicting the propensity of a protein to be soluble or to form inclusion body on overexpression in Escherichia coli

MOTIVATION Inclusion body formation has been a major deterrent for overexpression studies since a large number of proteins form insoluble inclusion bodies when overexpressed in Escherichia coli. The formation of inclusion bodies is known to be an outcome of improper protein folding; thus the composition and arrangement of amino acids in the proteins would be a major influencing factor in deciding its aggregation propensity. There is a significant need for a prediction algorithm that would enable the rational identification of both mutants and also the ideal protein candidates for mutations that would confer higher solubility-on-overexpression instead of the presently used trial-and-error procedures. RESULTS Six physicochemical properties together with residue and dipeptide-compositions have been used to develop a support vector machine-based classifier to predict the overexpression status in E.coli. The prediction accuracy is approximately 72% suggesting that it performs reasonably well in predicting the propensity of a protein to be soluble or to form inclusion bodies. The algorithm could also correctly predict the change in solubility for most of the point mutations reported in literature. This algorithm can be a useful tool in screening protein libraries to identify soluble variants of proteins.

Multicanonical jump walk annealing assisted by tabu for dynamic optimization of chemical engineering processes

A hybrid methodology, viz., multicanonical jump walk annealing assisted by tabu list (MJWAT) is proposed for solving dynamic optimization problems in chemically reacting systems. This method combines the power of multicanonical sampling with the beneficial features of simulated annealing. Incorporating tabu list further enhances the efficiency of the method. The superior performance of the MJWAT is highlighted with the help of five benchmark case studies.

Microchannel reactors: applications and use in process development

Recent research results on microchannel reactors are reviewed with particular reference to their applications and use as a cost effective tool during process development tasks. The high surface to volume ratio, efficient heat and mass transfer characteristics, vastly improved fluid mixing etc., allow precision control of reaction with improved conversions, selectivities and yields of desired products. The reaction times are shorter as compared to the conventional reactors with less degradation and side products. Scalability and optimization are also significantly easier. The article reviews variety of applications to catalysis, polymers, advanced materials, biosystems, organic reaction etc., with emphasis on the related engineering science issues.

Identification of coding and non-coding sequences using local Hölder exponent formalism

MOTIVATION Accurate prediction of genes in genomes has always been a challenging task for bioinformaticians and computational biologists. The discovery of existence of distinct scaling relations in coding and non-coding sequences has led to new perspectives in the understanding of the DNA sequences. This has motivated us to exploit the differences in the local singularity distributions for characterization and classification of coding and non-coding sequences. RESULTS The local singularity density distribution in the coding and non-coding sequences of four genomes was first estimated using the wavelet transform modulus maxima methodology. Support vector machines classifier was then trained with the extracted features. The trained classifier is able to provide an average test accuracy of 97.7%. The local singularity features in a DNA sequence can be exploited for successful identification of coding and non-coding sequences. CONTACT Available on request from bd.kulkarni@ncl.res.in.

Dynamic behaviour of coupled CSTRs operating under different conditions

Abstract The present paper analyzes the consequences of coupling two independent CSTRs operating under different stability conditions. A variety of behavioural patterns of the coupled system have been shown to exist for different types of reactor combinations, viz. stable-stable, oscillatory-stable and oscillatory-oscillatory. The reactor output is in general sensitive to the exchange coefficient, a proper choice of which can be of use in practice. The results obtained also provide an explanation for the occurrence of aperiodic behaviour. More importantly, the present simple model, viz. the linear coupling of the lumped parameter systems, offers guidelines for the analysis of more complex distributed parameter systems such as catalyst pellet or the fixed bed.

Feature selection and classification employing hybrid ant colony optimization/random forest methodology.

Accurate classification of instances depends on identification and removal of redundant features. Classification of data having high dimensionality is usually performed in conjunction with an appropriate feature selection method. Feature selection enables identification of the most informative feature subset from the enormously vast search space that can accurately classify the given data. We propose an ant colony optimization (ACO)/random forest based hybrid filter-wrapper search technique, which traverses the search space and selects a feature subset with high classifying ability. We evaluate the performance of our algorithm on four widely studied CoEPrA (Comparative Evaluation of Prediction Algorithms, http://coepra.org) datasets. The performance of the software ants mediated hybrid filter/wrapper approach compares well with the available competition results. Thus, the proposed Ant Colony Optimization based technique can effectively find small feature subsets capable of classifying with a very good accuracy and can be employed for feature subset selection with a high level of confidence.

Taboo Search Algorithm for Continuous Function Optimization

The Taboo search framework provides a simple and effective procedure for solving global optimization problems involving continuous functions. This novel algorithm handles both constrained and unconstrained functions very well and can be successfully used for large-scale process optimization.