Pyari Mohan Pradhan

IIR system identification using cat swarm optimization

Conventional derivative based learning rule poses stability problem when used in adaptive identification of infinite impulse response (IIR) systems. In addition the performance of these methods substantially deteriorates when reduced order adaptive models are used for such identification. In this paper the IIR system identification task is formulated as an optimization problem and a recently introduced cat swarm optimization (CSO) is used to develop a new population based learning rule for the model. Both actual and reduced order identification of few benchmarked IIR plants is carried out through simulation study. The results demonstrate superior identification performance of the new method compared to that achieved by genetic algorithm (GA) and particle swarm optimization (PSO) based identification.

Solving multiobjective problems using cat swarm optimization

Highlights? A new multiobjective cat swarm optimization (MOCSO) algorithm is proposed. ? MOCSO is more efficient than MOPSO and NSGA-II. ? This algorithm is tested using benchmark functions. ? Sensitivity analysis of different parameters of MOCSO algorithm is carried out. This paper proposes a new multiobjective evolutionary algorithm (MOEA) by extending the existing cat swarm optimization (CSO). It finds the nondominated solutions along the search process using the concept of Pareto dominance and uses an external archive for storing them. The performance of our proposed approach is demonstrated using standard test functions. A quantitative assessment of the proposed approach and the sensitivity test of different parameters is carried out using several performance metrics. The simulation results reveal that the proposed approach can be a better candidate for solving multiobjective problems (MOPs).

Connectivity constrained wireless sensor deployment using multiobjective evolutionary algorithms and fuzzy decision making

Deployment of sensor nodes is an important issue in designing sensor networks. The sensor nodes communicate with each other to transmit their data to a high energy communication node which acts as an interface between data processing unit and sensor nodes. Optimization of sensor node locations is essential to provide communication for a longer duration. An energy efficient sensor deployment based on multiobjective particle swarm optimization algorithm is proposed here and compared with that of non-dominated sorting genetic algorithm. During the process of optimization, sensor nodes move to form a fully connected network. The two objectives i.e. coverage and lifetime are taken into consideration. The optimization process results in a set of network layouts. A comparative study of the performance of the two algorithms is carried out using three performance metrics. The sensitivity analysis of different parameters is also carried out which shows that the multiobjective particle swarm optimization algorithm is a better candidate for solving the multiobjective problem of deploying the sensors. A fuzzy logic based strategy is also used to select the best compromised solution on the Pareto front.

Survey Comparative performance analysis of evolutionary algorithm based parameter optimization in cognitive radio engine: A survey

One of the important features of the cognitive radio engine is to adapt the parameters of radio to fulfill certain objectives in a time varying wireless environment. In order to achieve this adaptation, six evolutionary algorithms are employed for optimizing the predefined fitness functions in the radio environment. The performance of genetic algorithm, particle swarm optimization, differential evolution, bacterial foraging optimization, artificial bee colony optimization and cat swarm optimization algorithm in different modes of operation are studied in detail. Each algorithm is tested in single and multicarrier communication system in order to acknowledge the advantage of multicarrier communication systems in wireless environment. The spectral interference introduced by the cognitive user into the primary users band and that introduced by the primary user into the cognitive users band are also investigated. The performance of different algorithms are compared using convergence characteristics and four statistical metrics.

Pareto optimization of cognitive radio parameters using multiobjective evolutionary algorithms and fuzzy decision making

Abstract The design of cognitive radio engine aims at adapting the radio parameters to a predefined set of objective functions in communication system and may be formulated as a constrained multiobjective optimization problem. In the proposed work, an efficient design of orthogonal frequency division multiplexing based cognitive radio is carried out using multiobjective evolutionary algorithms. The performances of different algorithms are assessed and compared using three statistical metrics. The simulation results show that our proposed approach outperforms other algorithms while designing a cognitive radio engine. Our proposed approach which is based on the concept of cat swarm optimization, not only efficiently computes but also finds better nondominating solutions. In this paper, multiobjective evolutionary algorithms are applied to the parameter adaptation of a OFDM based cognitive radio engine. The spectral interference between primary and cognitive users is taken into consideration which plays a major role in communication. Due to heuristic nature of evolutionary algorithms, the stability of the simulation results is verified using different statistical tests. A fuzzy logic based strategy is shown in order to find out a compromised solution on the Pareto front.

Cooperative spectrum sensing in cognitive radio network using multiobjective evolutionary algorithms and fuzzy decision making

The cognitive radio has emerged as a potential solution to the problem of spectrum scarcity. Spectrum sensing unit in cognitive radio deals with the reliable detection of primary users signal. Cooperative spectrum sensing exploits the spatial diversity between cognitive radios to improve sensing accuracy. The selection of the weight assigned to each cognitive radio and the global decision threshold can be formulated as a constrained multiobjective optimization problem where probabilities of false alarm and detection are the two conflicting objectives. This paper uses evolutionary algorithms to solve this optimization problem in a multiobjective framework. The simulation results offered by different algorithms are assessed and compared using three performance metrics. This study shows that our approach which is based on the concept of cat swarm optimization outperforms other algorithms in terms of quality of nondominating solutions and efficient computation. A fuzzy logic based strategy is used to find out a compromise solution from the set of nondominated solutions. Different tests are carried out to assess the stability of the simulation results offered by the heuristic evolutionary algorithms. Finally the sensitivity analysis of different parameters is performed to demonstrate their impact on the overall performance of the system.

A correlation based stochastic partitional algorithm for accurate cluster analysis

Most partitional clustering algorithms such as K–means, K–nearest neighbour, evolutionary techniques use distance based similarity measures to group the patterns of a data set. However the distance based algorithms may converge to local optima when there are large variations in the attributes of the data set, leading to improper clustering. In this paper we propose a simple stochastic partitional clustering algorithm based on a Pearson correlation based similarity measure. Experiments on real–life data sets demonstrate that the proposed method provides superior performance compared to distance based K–means algorithm.

Block Least Mean Squares Algorithm over Distributed Wireless Sensor Network

In a distributed parameter estimation problem, during each sampling instant, a typical sensor node communicates its estimate either by the diffusion algorithm or by the incremental algorithm. Both these conventional distributed algorithms involve significant communication overheads and, consequently, defeat the basic purpose of wireless sensor networks. In the present paper, we therefore propose two new distributed algorithms, namely, block diffusion least mean square (BDLMS) and block incremental least mean square (BILMS) by extending the concept of block adaptive filtering techniques to the distributed adaptation scenario. The performance analysis of the proposed BDLMS and BILMS algorithms has been carried out and found to have similar performances to those offered by conventional diffusion LMS and incremental LMS algorithms, respectively. The convergence analyses of the proposed algorithms obtained from the simulation study are also found to be in agreement with the theoretical analysis. The remarkable and interesting aspect of the proposed block-based algorithms is that their communication overheads per node and latencies are less than those of the conventional algorithms by a factor as high as the block size used in the algorithms.

Direct and Inverse Modeling of Plants Using Cat Swarm Optimization

Derivative based learning rule poses stability problem when used in adaptive plant modeling. In addition the performance of these techniques deteriorates when used for non-linear plant modeling. In this chapter, the plant modeling task is formulated as an optimization problem. A recently introduced evolutionary algorithm, cat swarm optimization (CSO), is used to develop a new population based learning rule for the model. Adaptive modeling of a benchmarked plant is carried out through simulation study. The performance of the CSO in presence of nonlinearity in the plant is also studied. The results demonstrate superior performance of the CSO compared to that achieved by genetic algorithm (GA) and particle swarm optimization (PSO) based approaches for adaptive modeling.

Design of cognitive radio engine using artificial bee colony algorithm

A cognitive radio engine adapts its radio parameters using metaheauristic learning algorithms in order to satisfy certain objectives in a radio environment. In this study, three evolutionary algorithms are used for optimizing the predefined fitness functions in the time varying wireless environment. The performances of genetic algorithm, particle swarm optimization and artificial bee colony algorithm are analysed in different modes of operation and in presence of spectral interference. The simulation results are compared using convergence characteristics and two statistical metrics.