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

Abstract

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.

Array Pattern Synthesis Using a Digital Position Shift Method

“…What has been proposed in [18] is that the main limitation of PSO is its tendency to converge prematurely at local optima and in [13] the iteration numbers to converge or the number of the fitness function evaluations is an investigative topic.…”

“…The performance of the employed optimization scheme is an important factor in the success of a pattern synthesis method, in terms of solution quality, computational load, and stability. Particle swarm optimization (PSO) has received considerable attention because of its simplicity of implementation and its capability of escaping from the traps of local optima [17], [18].…”

“…In [18], the authors revealed that one of the aspects of PSO's capability of finding the global optima mainly depends upon the capability of exploring the search space. Initial higher value of inertia weight applied to the last velocity improves exploration of the search space and its lower value toward the end of search helps to attain faster convergence [18].…”

Linear Array Pattern Synthesis Using An Improved Multiobjective Genetic Algorithm

“…Multiobjective optimization for array pattern synthesis has been an academic issue [1][2][3][4][5][6][7][8][9][10]. Thinned arrays have been an object of intense research due to several advantages associated with their lower cost, weight, power, and complexity compared with the fully filled arrays.…”

“…The SLL for 28-element array is −21.90 dB, and three nulls at 30 • , 32.5 • , and 35 • are lower than −60 dB by BSA [2]. With the same parameter set, a family of position-mutated hierarchical particle swarm optimization algorithms with timevarying acceleration coefficients (PM 4 HPSO-TVAC) [3] outperforms these algorithms for synthesizing unequally spaced 28-element linear array with the best SLL suppression of −23.63 dB and nulls control below −60 dB.…”