Sumitra Mukhopadhyay

An Adaptive Pheromone Updation of the Ant‐System using LMS Technique

We propose a modified model of pheromone updation for Ant‐System, entitled as Adaptive Ant System (AAS), using the properties of basic Adaptive Filters. Here, we have exploited the properties of Least Mean Square (LMS) algorithm for the pheromone updation to find out the best minimum tour for the Travelling Salesman Problem (TSP). TSP library has been used for the selection of benchmark problem and the proposed AAS determines the minimum tour length for the problems containing large number of cities. Our algorithm shows effective results and gives least tour length in most of the cases as compared to other existing approaches.

Efficient player selection strategy based diversified particle swarm optimization algorithm for global optimization

Sport is one of the activities of human being where cooperative, competitive, self-learning and interactive environment helps in the overall improvement of the performance. These learning processes are very effective to regulate the players in a good direction as well as to enhance their capability of exploring the new techniques. Particle Swarm Optimization (PSO) is a popular stochastic optimization algorithm, used for solving real-world engineering problems. However, it usually suffers from local confinement and easily loses its diversity. In this paper, we have integrated the properties of sports with PSO algorithm and proposed an efficient player selection strategy based diversified PSO (EPS-dPSO), which improves the fitness and robustness of the technique without compromising the computational complexity of the algorithm. The properties of player selection is adopted to enhance the diversity within the search phase as well as to incorporate intense searching of the space. We have comprehensively evaluated the performance of proposed EPS-dPSO by applying it on standard benchmark problems. Experimental result shows that it not only tracks the global optimum within the small search interval but also able to obtain good result for large and asymmetrical search space and also insensitive to initialization of the problems. Further, tests are carried out on the benchmark functions from CEC2005, the large dimensional problems of CEC2008 and some real world problems from CEC2011. All the experimental results indicate the effectiveness and efficiency of the proposed EPS-dPSO compared to other traditional algorithms.

Fuzzy rule extraction using robust particle swarm optimization

Automatic fuzzy rule extraction assumes the realization of fuzzy if-then rules using a pre-assigned structure rather than an optimal one. In this paper, Particle Swarm Optimization (PSO) is used to simultaneously evolve the structure and the parameters of the fuzzy rule base. However, the existing PSO based adaptation employs randomness, which makes the rate of convergence dependent on the initial states and the end result can not be reproduced repeatedly with a pre-assigned value of iterations. The algorithm has been modified by removing the randomness in parameter learning, making it very robust. The scheme provides the flexibility in extracting the optimal set of fuzzy rules for a prescribed residual error in function approximation and prediction. Simulation studies and the comprehensive analysis demonstrate that an efficient learning technique as well as the structure development of the fuzzy system, can be achieved by the proposed approach.

SoC FPGA implementation of energy based cooperative spectrum sensing algorithm for Cognitive Radio

Spectrum sensing is an important aspect for Cognitive Radio Networks (CRNs) to enable efficient usage of the licensed spectrum bands when the primary users are inactive. For real time application, rapid detection of spectrum holes are of prime importance. In this paper, Finite State Machine (FSM) based SoC architecture design for energy based single and cooperative spectrum sensing techniques have been proposed. Next, the proposed architectures are implemented completely using Verilog Hardware Description language (HDL) only in Xilinx Virtex-IV (XC4VLX25) FPGA board. Finally, the overall implementation of the proposed architectures are tested using FPGA in Loop (FIL) based test environment.

Noise tolerant classification of aerial images into manmade structures and natural-scene images based on statistical dispersion measures

Objective of this paper is to categorize aerial images into two classes: manmade structures and natural-scene images. A novel noise tolerant approach based on statistical dispersion measures is presented here. In this approach, three statistical dispersion measures namely standard deviation, mean absolute deviation and median absolute deviation are used as features. With these measures, a feature vector of size 3×1 is formed and applied to probabilistic neural network (PNN) for classification purpose. From the database of 112 images, 14 images (7 from each class) are used for training purpose. For testing, we have used remaining 98 images (47 images manmade class and 51 images of natural scene class). The proposed method gives 95.75% correct classification for images with manmade structure and 98.04% for natural scene images.

An Improved Ant System using Least Mean Square algorithm

In this paper, we propose a modified model of pheromone updation for Ant-System (AS), entitled as Improved Ant System (IAS), and develop a new modeling framework for the above mentioned AS using the properties of basic Adaptive Filters. Here, we have exploited the properties of Least Mean Square (LMS) algorithm for the pheromone updation to find out the best minimum tour length for the Travelling Salesman Problem (TSP) and to resolve the basic shortcoming of easily falling into local optima and slow convergence speed. The desired length is updated in every iteration, which is the global minimum length and LMS algorithm is used to calculate the cost function (i.e., pheromone, which depends on the tour length). Hence, the pheromone is updated for the best minimum tour path. This improved algorithm has better search ability and good convergence speed. TSP library has been used for selection of a benchmark problem and the proposed IAS determines the minimum tour length for the problems containing large number of cities. Our algorithm shows effective results and gives least tour length in most of the cases as compared to other existing approaches.

Feature Selection Using Multi-Objective Optimization Technique for Supervised Cancer Classification.

In this chapter, a new multi-objective blended particle swarm optimization (MOBPSO) technique is proposed for the selection of significant and informative genes from the cancer datasets. As the basic optimization algorithm suffers from the local trapping, a blended Laplacian operator is integrated with it to overcome the drawback. The concept is also implemented for differential evolution, artificial bee colony, genetic algorithm and subsequently multi-objective blended differential evolution (MOBDE), multi-objective blended artificial bee colony (MOBABC) and multi-objective blended genetic algorithm (MOBGA) are proposed to extract the relevant genes from the cancer datasets. Proposed methodology utilizes two objective functions to sort out the genes which are differentially expressed from class to class as well as provides good results for the classification of disease. Experimental result reveals that the proposed methodology very efficiently selects differential and biologically relevant genes which are effective for the classification of disease which in turn offers more useful information about the gene–disease association.

FIL-DGA based hardware optimization system

Abstract This paper presents a new algorithm entitled as dominant character genetic algorithm (DGA) along with its hardware architecture entitled as dominant character genetic algorithm hardware architecture (DGA-Arch) for real parameter optimization problem. In DGA, the evolution process is inspired from the dominant characteristics present in human cognizance and it is realized by varying the mutation probability of the genes. On the other hand, DGA-Arch is a resource efficient, highly flexible architecture which is designed and integrated with field programmable gate array-in-loop (FIL) environment and an overall FIL based DGA (FIL-DGA) optimization system is developed. The DGA-Arch was implemented on Virtex IV (ML401, XC4VLX25) field programmable gate array (FPGA) chip with maximum of 5% logic slice utilization and tested for 18 benchmark problems. On an average, the proposed hardware manifested speedup of about 130× over software genetic algorithm (GA) implementation for the test problems. The performance is also compared using 5 modified functions with different GA based hardware reported in existing literature and is found to optimize problems more accurately with greater repeatability and diversity. The DGA-Arch reached convergence within 0.0005–0.009% of function evaluations compared to the total search space and requires almost no repeated synthesis in different problem environment. Later, the FIL-DGA system has been employed to adapt the parameters of few classical engineering problems and a real world application in cognitive radio environment.

Bi-stage hierarchical selection of pathway genes for cancer progression using a swarm based computational approach

Abstract Background Understanding of molecular mechanism, lying beneath the carcinogenic expression, is very essential for early and accurate detection of the disease. It predicts various types of irregularities and results in effective drug selection for the treatment. Pathway information plays an important role in mapping of genotype information to phenotype parameters. It helps to find co-regulated gene groups whose collective expression is strongly associated with the cancer development. Method In this paper, we have proposed a bi-stage hierarchical swarm based gene selection technique which combines two methods, proposed in this paper for the first time. First one is a multi-fitness discrete particle swarm optimization (MFDPSO) based feature selection procedure, having multiple fitness functions. This technique uses multi-filtering based gene selection procedure. On top of it, a new blended Laplacian artificial bee colony algorithm (BLABC) is proposed and it is used for automatic clustering of the selected genes obtained from the first procedure. We have performed 10 times 10-fold cross validation and compared our proposed method with various statistical and swarm based gene selection techniques for different popular cancer datasets. Result Experimental results show that the proposed method as a whole performs significantly well. The MFDPSO based system in combination with BLABC generates a good subset of pathway markers which provides more effective insight into the gene-disease association with high accuracy and reliability.

Efficient coordinator guided particle swarm optimization for real-parameter optimization

Particle swarm optimization (PSO) is a stochastic optimization algorithm which usually suffers from local confinement losing its diversity. In this paper, we have proposed an efficient coordinator guided PSO (ECG-PSO), which provides a good diversity to the swarms maintaining good convergence speed and hence improves the fitness and robustness of the technique. We comprehensively evaluate the performance of the ECG-PSO by applying it on real-parameter benchmark optimization functions. Again, the result of comparison shows that ECG-PSO is more efficient compared to other PSO variants for solving complex problems.