Sparse Linear Array Synthesis With Multiple Constraints Using Differential Evolution With Strategy Adaptation

Goudos, Sotirios K.; Siakavara, Katherine; Samaras, Theodoros; Vafiadis, E.; Sahalos, John N.

doi:10.1109/lawp.2011.2161256

Cited by 87 publications

(56 citation statements)

References 17 publications

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“…A DE strategy (jDE) that self-adapts the control parameters has been introduced in [31]. SaDE, a DE algorithm that self-adapts both control parameters and strategy based on learning experiences from previous generations [32] has been applied to microwave filter design, [33], and to linear arrays synthesis [34]. CoDE [35] is a recently presented adaptive DE variant, which combines three different trial vector generation strategies with three preset control parameter settings.…”

Section: Differential Evolutionmentioning

confidence: 99%

Multi-objective network planning optimization algorithm: human exposure, power consumption, cost, and capacity

et al. 2014

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Due to the huge popularity of wireless networks, future designs will not only consider the provided capacity, but also the induced exposure, the corresponding power consumption, and the economic cost. As these requirements are contradictory, it is not straightforward to design optimal wireless networks. Those contradicting demands have to satisfy certain requirements in practice. In this paper, a combination of two algorithms, a genetic algorithm and a quasi-particle swarm optimization, is developed, yielding a novel hybrid algorithm that generates further optimizations of indoor wireless network planning solutions, which is named hybrid indoor genetic optimization algorithm. The algorithm is compared with a heuristic network planner and composite differential evolution algorithm for three scenarios and two different environments. Results show that our hybrid-algorithm is effective for optimization of wireless networks which satisfy four demands: maximum coverage for a user-defined capacity, minimum power consumption, minimal cost, and minimal human exposure

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Section: Differential Evolutionmentioning

confidence: 99%

Multi-objective network planning optimization algorithm: human exposure, power consumption, cost, and capacity

et al. 2014

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“…Unfortunately, the synthesis for SA refers to a question of multiple constraints including the minimum element spacing, the antenna aperture, and the number of elements. Therefore, the use of stochastic optimization algorithms such as the simulated annealing [12], PSO [13], DE [14,15], and GA [16] is not a computationally efficient way for the synthesis of the SA including many elements. During the past few years, the deterministic approaches, such as the IFT [8][9][10], the methodology by means of density taper [17], had been proposed.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method Based on the Iterative Fourier Transform and the Differential Evolution for Pattern Synthesis of Sparse Linear Arrays

Wang

2018

International Journal of Antennas and Propagation

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A hybrid method that combines the iterative Fourier transform (IFT) and the algorithm of differential evolution (DE) is proposed to address the synthesis of low sidelobe sparse linear array (SLA) including many elements. Firstly, a thinned linear array (TLA) with the lattices spaced at half wavelength is obtained by the IFT. Then, for the elements of the TLA whose left or right spacing are greater than half wavelength, their placements are selected as the candidates which will be further optimized by the DE, as long as the interelement spacing is not less than half wavelength. Consequently, the convergence pressure of DE is greatly alleviated for the reason that the selected elements only accounts for a small part of the total. Therefore, the SLA with improved sidelobe performance can be obtained at relative low hardware cost. Several numerical instances confirmed the effectiveness of the proposed method.

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“…Due to their advantages, several methods have been developed: evolutionary techniques [1][2][3][4], compressive sensing inspired approaches [5][6][7][8], and many others [9][10][11][12][13]. In spite of the importance of the problem, no specific comparison procedures among synthesis methods have been developed up to date.…”

Section: Introductionmentioning

confidence: 99%

Comparison Guidelines and Benchmark Procedure for Sparse Array Synthesis

Pinchera¹,

Migliore²

2016

PIER M

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Abstract-A benchmarking procedure for sparse linear array synthesis methods is proposed. Our approach is based on the comparison of the performance of the array synthesis algorithm under test with the performance of a reference solution based on a optimally equispaced array. The benchmark procedure is demonstrated considering some examples regarding sparse synthesis method proposed in literature. Guidelines for the correct comparison of synthesis methods and some "tough problems" for the test of new sparse synthesis algorithms are also provided.

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Sparse Linear Array Synthesis With Multiple Constraints Using Differential Evolution With Strategy Adaptation

Cited by 87 publications

References 17 publications

Multi-objective network planning optimization algorithm: human exposure, power consumption, cost, and capacity

Multi-objective network planning optimization algorithm: human exposure, power consumption, cost, and capacity

A Hybrid Method Based on the Iterative Fourier Transform and the Differential Evolution for Pattern Synthesis of Sparse Linear Arrays

Comparison Guidelines and Benchmark Procedure for Sparse Array Synthesis

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