A new hybridized backtracking search optimization algorithm with differential evolution for sidelobe suppression of uniformly excited concentric circular antenna arrays

Das, Sudipta; Mandal, Durbadal; Kar, Rajib; Ghoshal, Sakti Prasad

doi:10.1002/mmce.20857

Cited by 19 publications

(4 citation statements)

References 20 publications

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“…Exploitation and exploration are key search mechanisms in solving complex optimisation problems. Numerous optimisation cases have demonstrated that basic BSA has a strong ability of exploration because of its mutation and crossover strategies, which the better or worse individuals have the same probability to be selected. However, the poor exploitation of basic BSA is also because of the strategies, leading to the problems of slow convergence speed and low search precision.…”

Section: Enhancements Of Backtracking Search Optimisationmentioning

confidence: 99%

Enhancement of backtracking search algorithm for identifying soil parameters

Jin

Yin

2020

Num Anal Meth Geomechanics

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In this paper, an enhanced backtracking search algorithm (so-called MBSA-LS) for parameter identification is proposed with two modifications: (a) modifying the mutation of original backtracking search algorithm (BSA)considering the contribution of current best individual for accelerating convergence speed and (b) novelly incorporating an efficient differential evolution (DE) as local search for improving the quality of population. The proposed MBSA-LS is first validated with better performance than the original BSA and some other typical state-of-the-art optimization algorithms on a benchmark of soil parameter identification in terms of effectiveness, efficiency, and robustness. Then, the efficiency of the MBSA-LS is further illustrated by two representative cases: identifying soil parameters from both laboratory tests and field measurements. All comparisons demonstrate that the proposed MBSA-LS algorithm can give accurate results in a short time. Finally, to conveniently solve the problems of parameter identification, a practical tool ErosOpt for parameter identification is developed by integrating the proposed MBSA-LS and some other efficient algorithms for readers to conduct the parameter identification using optimisation algorithms. K E Y W O R D Sconstitutive model, graphical user interface, local search, optimisation, parameter identification, pressuremeter

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Section: Enhancements Of Backtracking Search Optimisationmentioning

confidence: 99%

Enhancement of backtracking search algorithm for identifying soil parameters

Jin

Yin

2020

Num Anal Meth Geomechanics

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“…As a result, many improvement research studies have been developed to overcome the drawback. e major modified research studies include the following five categories: (1) modified BSA algorithms by developing hybridization mechanisms [31][32][33][34][35][36][37][38][39], (2) modified BSA algorithms by developing novel initialization mechanisms [40][41][42][43][44][45][46], (3) modified BSA algorithms by developing novel reproduction (mutation or crossover) mechanisms [47][48][49][50][51][52][53][54][55], (4) modified BSA algorithms by developing novel selection mechanisms [56][57][58][59][60], and (5) modified BSA algorithms by developing novel control mechanisms for parameters (including the control factor F which is randomly generated in the original BSA) [61][62][63][64][65][66][67][68][69].…”

Section: Introductionmentioning

confidence: 99%

Deep‐Mining Backtracking Search Optimization Algorithm Guided by Collective Wisdom

Liu

Miao³

et al. 2019

Mathematical Problems in Engineering

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The backtracking search optimization algorithm (BSA) is a recently proposed evolutionary algorithm with simple structure and well global exploration capability, which has been widely used to solve optimization problems. However, the exploitation capability of the BSA is poor. This paper proposes a deep-mining backtracking search optimization algorithm guided by collective wisdom (MBSAgC) to improve its performance. The proposed algorithm develops two learning mechanisms, i.e., a novel topological opposition-based learning operator and a linear combination strategy, by deeply mining the winner-tendency of collective wisdom. The topological opposition-based learning operator guides MBSAgC to search the vertices in a hypercube about the best individual. The linear combination strategy contains a difference vector guiding individuals learning from the best individual. In addition, in order to balance the overall performance, MBSAgC simulates the clusterity-tendency strategy of collective wisdom to develop another difference vector in the above linear combination strategy. The vector guides individuals to learn from the mean value of the current generation. The performance of MBSAgC is tested on CEC2005 benchmark functions (including 10-dimension and 30-dimension), CEC2014 benchmark functions, and a test suite composed of five engineering design problems. The experimental results of MBSAgC are very competitive compared with those of the original BSA and state-of-the-art algorithms.

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“…One of the methods for side lobe suppression and beam control is to use optimization techniques. For antenna array applications, popular optimization techniques are genetic algorithm, ant colony optimization, simulated annealing, particle swarm optimization [10][11][12][13][14][15][16]. Differential equation with back tracking search algorithm is applied to reduce side lobe level of concentric circular array [10].…”

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confidence: 99%

Side Lobe Level Reduction of Phased Array Using Tchebyscheff Distribution and Particle Swarm Optimization

Nandi¹,

Roy²

2017

IJET

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Abstract-Phased array antenna produces highly directive beam along with high side lobe level (SLL) which causes interference in other directions and level of side lobe changes with beam steering. To mitigate such constraint, in this paper, particle swarm optimization (PSO) is applied along with Tchebyscheff distribution (TD) to the linear phased array antenna. Simulating radiation patterns of linear phased array antenna at different scanning angles, using Tchebyscheff distribution, particle swarm optimization and combined Tchebyscheff distribution with particle swarm optimization (TD-PSO) are compared. Maximum side lobe level is reduced by 11 dB using TD-PSO method than Tchebyscheff distribution method. Better SLL suppression was obtained using TD-PSO but with slight increase of the main null to null beamwidth, compared to TD array. The research work presented here may be useful for radio navigation and satellite communication where phased array antennas are widely used.Keyword -Tchebyscheff distribution, phased array, side lobe level, particle swarm optimization I. INTRODUCTION In phased array antenna resulting radiation pattern is steered at any particular direction by changing electronically the relative phase of feed current between the antenna elements in the array [1][2][3]. Phased array antenna is widely used for RADAR, satellite communication and other vehicular applications. The main drawback of phased array is higher side lobe level which changes with beam tilting and causes interference. Various methods of designing phased array with lower side lobe levels can be found in literature [4][5][6][7][8][9]. Statistically tapered amplitude arrays based on empirical or analytical formula found application in array synthesis methods to reduce side lobe level with maintaining the desired gain [4][5][6][7]. Tchebyscheff array is very well known optimal array pattern synthesizing method for the linear array where individual element fed with the current value equal to the Tchebyscheff coefficient and the resulting array pattern produces fixed lowest side lobe level for a particular beamwidth [7]. A method for finding optimum weights for minimizing side lobe level of arbitrary linear array is described in [6].There are various proposed strategies and methods where by tapering the feeding current amplitude of elements, side lobe levels are reduced [4-6, 8, 9]. One of the methods for side lobe suppression and beam control is to use optimization techniques. For antenna array applications, popular optimization techniques are genetic algorithm, ant colony optimization, simulated annealing, particle swarm optimization [10][11][12][13][14][15][16]. Differential equation with back tracking search algorithm is applied to reduce side lobe level of concentric circular array [10]. Null placing and minimizing the side lobe level during scanning of a linear array antenna using Pareto optimal synthesis procedure is described in [11]. Thinning is another technique of side lobe level reduction of linear array [16]. The problem of ...

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A new hybridized backtracking search optimization algorithm with differential evolution for sidelobe suppression of uniformly excited concentric circular antenna arrays

Cited by 19 publications

References 20 publications

Enhancement of backtracking search algorithm for identifying soil parameters

Enhancement of backtracking search algorithm for identifying soil parameters

Deep‐Mining Backtracking Search Optimization Algorithm Guided by Collective Wisdom

Side Lobe Level Reduction of Phased Array Using Tchebyscheff Distribution and Particle Swarm Optimization

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