scholarly journals Orthogonal Genetic Algorithm for Planar Thinned Array Designs

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Li Zhang ◽  
Yong-Chang Jiao ◽  
Bo Chen ◽  
Hong Li
Keyword(s):  
Genetic Algorithm ◽  
Factor Analysis ◽  
Orthogonal Array ◽  
Orthogonal Design ◽  
Crossover Operator ◽  
Sidelobe Level ◽  
Thinned Array ◽  
Better Than ◽  
Peak Sidelobe Level

An orthogonal genetic algorithm (OGA) is applied to optimize the planar thinned array with a minimum peak sidelobe level. The method is a genetic algorithm based on orthogonal design. A crossover operator formed by the orthogonal array and the factor analysis is employed to enhance the genetic algorithm for optimization. In order to evaluate the performance of the OGA, 20×10-element planar thinned arrays have been designed to minimize peak sidelobe level. The optimization results by the OGA are better than the previously published results.

scholarly journals Genetic Algorithm for Biobjective Urban Transit Routing Problem

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
J. S. C. Chew ◽  
L. S. Lee ◽  
H. V. Seow
Keyword(s):  
Genetic Algorithm ◽  
Feasible Solution ◽  
Benchmark Problems ◽  
Computational Results ◽  
Crossover Operator ◽  
Urban Transit ◽  
Routing Problem ◽  
Genetic Algorithm Approach ◽  
Better Than

This paper considers solving a biobjective urban transit routing problem with a genetic algorithm approach. The objectives are to minimize the passengers’ and operators’ costs where the quality of the route sets is evaluated by a set of parameters. The proposed algorithm employs an adding-node procedure which helps in converting an infeasible solution to a feasible solution. A simple yet effective route crossover operator is proposed by utilizing a set of feasibility criteria to reduce the possibility of producing an infeasible network. The computational results from Mandl’s benchmark problems are compared with other published results in the literature and the computational experiments show that the proposed algorithm performs better than the previous best published results in most cases.

scholarly journals Linear Array Thinning with Cavity backed U-slot Patch Antenna using Genetic Algorithm

2021 ◽  
Vol 5 (1) ◽  
pp. 9
Author(s):  
Novalia Pertiwi ◽  
Fannush Shofi Akbar ◽  
Eko Setijadi ◽  
Gamantyo Hendrantoro
Keyword(s):  
Genetic Algorithm ◽  
Patch Antenna ◽  
Linear Array ◽  
Dense Array ◽  
Sidelobe Level ◽  
Low Sidelobe ◽  
Genetic Algorithm Method ◽  
Peak Sidelobe Level

In this paper, a thinned linear array with Cavity backed U-slot Patch has been investigated using the Genetic Algorithm to minimize peak sidelobe level and the number of antenna elements. One of the essential steps in the Genetic Algorithm method is a crossover, which uses the Paired Top Ten and Combined Top Five rules applied to the Cavity backed U-slot Patch antenna. The peak sidelobe level value is -18.63 dB with a array filling of 63.33% at the broadside angle using Combined Top Five rules. In Paired Top Ten, the peak sidelobe level value is -19.48 dB with a array filling of 70%. The two methods are still better as compared to a dense array. This study is essential in the development of radar technologies since it needs a low sidelobe level.

scholarly journals Low Sidelobe Sparse Concentric Ring Arrays Optimization Using Modified GA

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Ke-song Chen ◽  
Yong-yun Zhu ◽  
Xiao-long Ni ◽  
Hui Chen
Keyword(s):  
Genetic Algorithm ◽  
Computational Cost ◽  
Optimization Method ◽  
Sidelobe Level ◽  
Concentric Ring ◽  
Low Sidelobe ◽  
Array Aperture ◽  
Peak Sidelobe Level

To minimize the peak sidelobe level (PSLL) of sparse concentric ring arrays, this paper presents an optimization method of grid ring radii of these arrays. The proposed method is based on modified real genetic algorithm (MGA); it makes grid ring radii as optimal variables and makes elements more reasonably distributed on the array aperture. Also, it can improve the PSLL of the sparse concentric ring arrays and can meanwhile control the computational cost. The simulated results confirming the efficiency and the robustness of the algorithm are provided at last.

scholarly journals Sidelobe Reduction in a Planar Array using Genetic Algorithm under Backlobe Reduction Condition

2017 ◽  
Vol 16 (1) ◽  
pp. 1-8
Author(s):  
Reena Manandhar ◽  
Prapun Suksompong ◽  
Chalie Charoenlarpnopparut
Keyword(s):  
Genetic Algorithm ◽  
Research Work ◽  
Sidelobe Level ◽  
Reduction Condition ◽  
Planar Array ◽  
Binary Genetic Algorithm ◽  
3D Beamforming ◽  
Level Reduction ◽  
Peak Sidelobe Level

The peak sidelobe level (PSL) minimizing amplitude weights for planar array, with 3D beamforming under the backlobe level reduction (BLL) condition is proposed. Binary genetic algorithm (BGA) is performed on the amplitude weights to achieve low PSL. BLL reduction condition for the inter-element distance between the antenna elements is applied to achieve reduced BLL. Earlier studies only focus on minimizing sidelobe level of planar array. BLL reduction condition has not yet been applied for planar array case. Hence a different way of achieving the amplitude weights to reduce PSL with 3D beamforming using BGA, under the BLL reduction condition is proposed in this paper. Obtained PSL and BLL for  planar array by applying optimized weights under BLL condition is -20.89 dB and -2.37 dB respectively. PSL is reduced by 8.84 dB compared to  uniform planar array. BLL is reduced by 2.37 dB compared to planar array discussed in existing research work.

scholarly journals A Building-Block-Based Genetic Algorithm for Solving the Robots Allocation Problem in a Robotic Mobile Fulfilment System

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Jingtian Zhang ◽  
Fuxing Yang ◽  
Xun Weng
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Scheduling ◽  
Building Blocks ◽  
Allocation Problem ◽  
Accurate Analysis ◽  
Allocation Problems ◽  
Scheduling Method ◽  
Project Scheduling Problem ◽  
Block Based ◽  
Better Than

Robotic mobile fulfilment system (RMFS) is an efficient and flexible order picking system where robots ship the movable shelves with items to the picking stations. This innovative parts-to-picker system, known as Kiva system, is especially suited for e-commerce fulfilment centres and has been widely used in practice. However, there are lots of resource allocation problems in RMFS. The robots allocation problem of deciding which robot will be allocated to a delivery task has a significant impact on the productivity of the whole system. We model this problem as a resource-constrained project scheduling problem with transfer times (RCPSPTT) based on the accurate analysis of driving and delivering behaviour of robots. A dedicated serial schedule generation scheme and a genetic algorithm using building-blocks-based crossover (BBX) operator are proposed to solve this problem. The designed algorithm can be combined into a dynamic scheduling structure or used as the basis of calculation for other allocation problems. Experiment instances are generated based on the characteristics of RMFS, and the computation results show that the proposed algorithm outperforms the traditional rule-based scheduling method. The BBX operator is rapid and efficient which performs better than several classic and competitive crossover operators.

scholarly journals A GENETIC APPROACH FOR LONG TERM VIRTUAL ORGANIZATION DISTRIBUTION

2011 ◽  
Vol 20 (02) ◽  
pp. 271-295 ◽  
Author(s):  
VÍCTOR SÁNCHEZ-ANGUIX ◽  
SOLEDAD VALERO ◽  
ANA GARCÍA-FORNES
Keyword(s):  
Genetic Algorithm ◽  
Virtual Organization ◽  
Genetic Material ◽  
Multi Agent System ◽  
Crossover Operator ◽  
Agent Based ◽  
Multi Agent ◽  
Global Goal ◽  
Computational Resources

An agent-based Virtual Organization is a complex entity where dynamic collections of agents agree to share resources in order to accomplish a global goal or offer a complex service. An important problem for the performance of the Virtual Organization is the distribution of the agents across the computational resources. The final distribution should provide a good load balancing for the organization. In this article, a genetic algorithm is applied to calculate a proper distribution across hosts in an agent-based Virtual Organization. Additionally, an abstract multi-agent system architecture which provides infrastructure for Virtual Organization distribution is introduced. The developed genetic solution employs an elitist crossover operator where one of the children inherits the most promising genetic material from the parents with higher probability. In order to validate the genetic proposal, the designed genetic algorithm has been successfully compared to several heuristics in different scenarios.

scholarly journals Spectrum Sensing for Cognitive Radio Using Genetic Algorithm

2018 ◽  
Vol 14 (09) ◽  
pp. 190 ◽  
Author(s):  
Shewangi Kochhar ◽  
Roopali Garg
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
False Alarm ◽  
Spectrum Sensing ◽  
Optimization Technique ◽  
Primary User ◽  
Time Frame ◽  
Probability Of False Alarm ◽  
Sensing Time ◽  
Better Than

<p>Cognitive Radio has been skillful technology to improve the spectrum sensing as it enables Cognitive Radio to find Primary User (PU) and let secondary User (SU) to utilize the spectrum holes. However detection of PU leads to longer sensing time and interference. Spectrum sensing is done in specific “time frame” and it is further divided into Sensing time and transmission time. Higher the sensing time better will be detection and lesser will be the probability of false alarm. So optimization technique is highly required to address the issue of trade-off between sensing time and throughput. This paper proposed an application of Genetic Algorithm technique for spectrum sensing in cognitive radio. Here results shows that ROC curve of GA is better than PSO in terms of normalized throughput and sensing time. The parameters that are evaluated are throughput, probability of false alarm, sensing time, cost and iteration.</p>

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