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Si T.,Bankura Unnayani Institute of Engineering | De A.,Dr. B. C. Roy Engineering College | Bhattacharjee A.K.,National Institute of Technology Durgapur
Advances in Intelligent Systems and Computing

In this work, a new method for creating diversity in Particle Swarm Optimization is devised. The key feature of this method is to derive velocity update equation for each particle in Particle Swarm Optimizer using Grammatical Swarm algorithm. Grammatical Swarm is a Grammatical Evolution algorithm based on Particle Swarm Optimizer. Each particle updates its position by updating velocity. In classical Particle Swarm Optimizer, same velocity update equation for all particles is responsible for creating diversity in the population. Particle Swarm Optimizer has quick convergence but suffers from premature convergence in local optima due to lack in diversity. In the proposed method, different velocity update equations are evolved using Grammatical Swarm for each particles to create the diversity in the population. The proposed method is applied on 8 well-known benchmark unconstrained optimization problems and compared with Comprehensive Learning Particle Swarm Optimizer. Experimental results show that the proposed method performed better than Comprehensive Learning Particle Swarm Optimizer. © Springer International Publishing Switzerland 2014. Source

Chatterjee A.,National Institute of Technology Durgapur | Mahanti G.K.,National Institute of Technology Durgapur | Pathak N.,Bankura Unnayani Institute of Engineering
Progress In Electromagnetics Research B

Scanning a planar array in the x-z plane directs the beam peak to any direction off the broadside along the same plane. Reduction of sidelobe level in concentric ring array of isotropic antennas scanned in the x-z plane result in a wide first null beamwidth (FNBW). In this paper, the authors propose pattern synthesis methods to reduce the sidelobe levels with fixed FNBW by making the scanned array thinned based on two different global optimization algorithms, namely Gravitational Search Algorithm (GSA) and modified Particle Swarm Optimization (PSO) algorithm. The thinning percentage of the array is kept more than 45 percent and the first null beamwidth (FNBW) is kept equal to or less than that of a fully populated, uniformly excited and 0.5λ spaced concentric circular ring array of same scanning angle and same number of elements and rings. Source

Si T.,Bankura Unnayani Institute of Engineering | Jana N.D.,National Institute of Technology Durgapur
International Journal of Intelligent Systems Technologies and Applications

Particle swarm optimisation (PSO) is population-based optimisation algorithm having stochastic in nature. PSO has quick convergence speed but often gets stuck into local optima due to lacks of diversity. In this work, first mutation operator adopted from Differential Evolution (DE) algorithm is applied in PSO with decreasing inertia weight (PSO-DMLB). In second method, DE mutation is applied in another PSO variant, namely Comprehensive Learning PSO (CLPSO). The second method is termed as CLPSO-DMLB. Local best position of each particle is muted by a predefined mutation probability with the scaled difference of two randomly selected particle's local best position to increase the diversity in the population to achieve better quality of solutions. The proposed methods are applied on wellknown benchmark unconstrained functions and obtained results are compared to show the effectiveness of the proposed methods. © 2012 Inderscience Enterprises Ltd. Source

Maji C.,Bankura Unnayani Institute of Engineering
Proceedings of 2014 International Conference on Contemporary Computing and Informatics, IC3I 2014

This paper focuses on minimization of interference to primary user (PU) through an optimal strategy of power allocation algorithm for source and relay nodes in multihop cognitive radio network (CRN) under the constraints of outage probability (successful delivery) and data rate over source-destination link. This objective is also studied in the framework of enhanced lifetime of the CRN. Extensive simulations are done for both energy aware (EA) and non-energy aware (NEA) power allocation schemes. Simulation results show that NEA based power allocation offers better capacity than EA scheme at the cost of slightly increased interference to PU. Simulation results also show a three dimensional (3D) relative trade-off performance among the data transmission capacity, network lifetime and total transmission power. © 2014 IEEE. Source

Pathak N.,Bankura Unnayani Institute of Engineering | Basu B.,National Institute of Technology Durgapur | Mahanti G.K.,National Institute of Technology Durgapur
Progress In Electromagnetics Research M

In this paper, the authors propose a method based on the combination of inverse fast Fourier transform (IFFT) and modified particle swarm optimization for side lobe reduction of a thinned mutually coupled linear array of parallel half-wave length dipole antennas with specified maximum return loss. The generated pattern is broadside (φ = 90 degree) in the horizontal plane. Mutual coupling between the half-wave length parallel dipole antennas has been taken care of by induced emf method considering the current distribution on each dipole to be sinusoidal. Directivity, first null beamwidth (FNBW), return loss of the thinned array is also calculated and compared with a fully populated array. Two cases have been considered, one with symmetric excitation voltage distribution and the other with asymmetric one. The method uses the property that for a linear array with uniform element spacing, an inverse Fourier transform relationship exists between the array factor and the element excitations. Inverse Fast Fourier Transform is used to calculate the array factor, which in turn reduces the computation time significantly. The element pattern of half-wave length dipole antenna has been assumed omnidirectional in the horizontal plane. Two examples are presented to show the flexibility and effectiveness of the proposed approach. Source

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