PSO in Concentric Circular Arrays for Side Lobe Reduction with Symmetric Relocation Boundary Condition

Chakravorty, Pragnan; Mandal, Durbadal

doi:10.1007/978-81-322-2268-2_51

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…Many numerical techniques are available today [15][16][17][18][19][20][21][22][23] to solve different problems of modelling and optimization in science and engineering in general. Broadly the techniques can be classified into classical and meta-heuristic.…”

Section: (D) Use Of Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…The chosen optimization algorithm, PSO, is extremely popular in the problems of Electromagnetics because of its algorithmic simplicity and quicker convergence profile compared to evolutionary ones like Genetic Algorithm (GA) or Differential Evolution (DE). PSO particularly simulates swarm behaviour of flying bees or birds ; it has been so‐far used in a variety of problems in Electromagnetics . Particularly, their use in antenna radiation pattern synthesis has been extensive ; however, the algorithm suffers from a premature convergence in its basic form, which is why an improved technique is used here .…”

Section: Design Of the Filtermentioning

confidence: 99%

“…PSO particularly simulates swarm behaviour of flying bees or birds ; it has been so‐far used in a variety of problems in Electromagnetics . Particularly, their use in antenna radiation pattern synthesis has been extensive ; however, the algorithm suffers from a premature convergence in its basic form, which is why an improved technique is used here . The governing equations for the optimization algorithm are given below:

v_{n} italic= ω v_{n} italic+ c_{1} normalr normaln d_{1} ((), p_{italicbest italic, n} italic‐ x_{n}) italic+ c_{2} normalr normaln d_{2} ((), g_{italicbest italic, n} italic‐ x_{n})

x_{n} = x_{n} + normalΔ t v_{n}

where v n and x n are the respective velocity and position of the nth particle in a given iterative cycle, rnd 1 and rnd 2 are randomly generated numbers, Δt is the incremental time in each iterative cycle, ω is the inertial weight whereas c 1 and c 2 are self and collective inertias respectively.…”

Section: Design Of the Filtermentioning

confidence: 99%

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Microstrip bandpass filter design using split‐path method and optimized curvature corrections

Chakravorty

Mandal

2015

Int J Numerical Modelling

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Summary Bandpass filters with wide pass‐band are an essential requirement in various equipments of satellite and defence communication sectors. Here a method of split‐path interactions is proposed to approximately predict the resonant frequency and topology of bandpass filters which otherwise fall under the category of heuristic designs. Curved transmission lines are often required to make filter structures physically compact; however, curvature effects create errors in the theoretical (design) prediction of resonant or central frequencies for bandpass filter design. Earlier propositions on curvature corrections had been considerably precise, but recent design standards demand even higher accuracies. The prime feature of this work is the use of a meta‐heuristic optimization (i.e. Particle Swarm Optimization) technique in curvature corrections for the first time which brings accuracies of over 99% in the corrected results. The split paths used in this design are suitably curved, with the proposed optimized curvature correction technique, so as to attain a compact size of the filter. The resulting filter has a low insertion loss of around −1.00 dB and a sharp stopband cut‐off. Fabrication was done on a FR4 microstrip substrate with a good agreement between measured and simulated results. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: (D) Use Of Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Section: Design Of the Filtermentioning

confidence: 99%

v_{n} italic= ω v_{n} italic+ c_{1} normalr normaln d_{1} ((), p_{italicbest italic, n} italic‐ x_{n}) italic+ c_{2} normalr normaln d_{2} ((), g_{italicbest italic, n} italic‐ x_{n})

x_{n} = x_{n} + normalΔ t v_{n}

Section: Design Of the Filtermentioning

confidence: 99%

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Microstrip bandpass filter design using split‐path method and optimized curvature corrections

Chakravorty

Mandal

2015

Int J Numerical Modelling

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Role of Boundary Dynamics in Improving Efficiency of Particle Swarm Optimization on Antenna Problems

Chakravorty

Mandal

2015

2015 IEEE Symposium Series on Computational Intelligence

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Antenna Array Failure Correction Using Improved Particle Swarm Optimization With Wavelet Mutation

Mitra¹,

Mandal

Kar

et al. 2019

AzJHPC

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The advent of complex engineering systems has made computation intensive design processes inevitable. Unfortunately, such processes require larger resources, i.e., larger memory, parallel processing, etc., and time than usual. As a result, the high performance issues like managing these processes to run within limited resource pools has become ever more challenging. This paper reports an application of intelligent computing in the specific area of antenna arrays and implicitly shows how a computationally intensive process can improve its performance within a limited available resource by judiciously combining different algorithms. Failures in radiating elements in an antenna array usually increase the undesired side lobe radiation, thereby distorting the original radiation pattern. Here, restoration of the original pattern has been attempted by restricting the side lobe levels (SLLs) to the desired threshold using a meta-heuristic optimization technique named Improved Particle Swarm Optimization with Wavelet Mutation (IPSOWM). Also, as a reference, the performance of this algorithm has been compared with Improved Particle Swarm Optimization (IPSO) technique. Results show that IPSOWM yields a better solution to the existing problem as compared to IPSO.

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PSO in Concentric Circular Arrays for Side Lobe Reduction with Symmetric Relocation Boundary Condition

Cited by 3 publications

References 10 publications

Microstrip bandpass filter design using split‐path method and optimized curvature corrections

Microstrip bandpass filter design using split‐path method and optimized curvature corrections

Role of Boundary Dynamics in Improving Efficiency of Particle Swarm Optimization on Antenna Problems

Antenna Array Failure Correction Using Improved Particle Swarm Optimization With Wavelet Mutation

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