Use of TSK type fuzzy system based fitness function approximation for efficient optimization of low-profile wideband diversity PIFA by PSO

Bhattacharya, Rajarshi; Bhattacharyya, Tarun Kanti; Garg, Ramesh Kumar

doi:10.1109/aemc.2009.5430677

Cited by 1 publication

(2 citation statements)

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“…Combining each ordered pair of location into a complex number , the optimization problem can be expressed as a search for the optimal to minimize the SLL. The normalized array factor of the symmetric planar array is given by: (11) Let be the elevation angle corresponding to the maximum SLL in plane: (12) where is the set of angle excluding the main beam. For such an array, the SLL minimization problem can be solved by minimizing the cost function , which is defined as sum of SLL in and planes [28], [29] (13)…”

Section: Synthesis Of Uniformly Excited Unequally Spaced Linear Amentioning

confidence: 99%

“…PSO has received a lot of attention due to its simplicity of implementation and its capability of escaping from the traps of local optima [3], [4]. Apart from many other applications like design of ultra-wideband (UWB) antennas [5], multi-band antennas [6]- [8], artificial magnetic conductors [9], modeling of passive components [10]- [12] etc., Manuscript PSO has been most extensively utilized in different types of antenna array design problems [3], [4], [13]- [23].…”

Section: Introductionmentioning

confidence: 99%

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Position Mutated Hierarchical Particle Swarm Optimization and its Application in Synthesis of Unequally Spaced Antenna Arrays

Bhattacharya

Bhattacharyya

Garg

2012

IEEE Trans. Antennas Propagat.

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A family of position mutated hierarchical particle swarm optimization algorithms with time varying acceleration coefficients (viz.HPSO-TVAC, ) is introduced in this paper. The proposed position mutation schemes help the swarm to get out of local optima traps and the hierarchical nature of the swarm prevents premature convergence. One distinct advantage of the proposed algorithms over the existing mutated PSO algorithms is that HPSO-TVAC do not involve any controlling parameter. Performance of the proposed algorithms is evaluated on standard benchmark functions. Comparative study shows that HPSO-TVAC performs better than the other HPSO-TVAC, HPSO-TVAC, comprehensive learning PSO (CLPSO), adaptive-CLPSO (A-CLSPO), PSO with time-varying inertia weight (PSO-TVIW), and constriction factor PSO (CFPSO)for the benchmark functions considered. We apply the proposed algorithm to the synthesis of uniformly excited, unequally dpaced linear array to minimize sidelobe level (SLL) and to control first-null-beamwidth (FNBW) and null locations. Further, we apply the proposed algorithm to the synthesis of unequally spaced sparse planar array to minimize SLL.

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