Most Valuable Player Algorithm for Circular Antenna Arrays Optimization to Maximum Sidelobe Levels Reduction

Optimally designed arrays are used to obtain high gain and directivity. Based on the desired application, different radiation patterns can be generated. These patterns have generally a main beam and some sidelobes. These last ones are undesirable because they can cause electromagnetic interference and compatibility issues. Therefore, sidelobe levels (SLLs) reduction is one of the most important factors for the design of antenna arrays and arrays must be designed in a way to reduce SLLs. In this paper, the optimal design of circular antenna arrays (CAA) for maximum SLLs reduction is investigated using a newly developed metaheuristic that is the most valuable player algorithm. This algorithm is inspired from sport and it has been proven to be an excellent optimization algorithm for mathematical benchmarks. Three cases with different number of arrays are investigated. For comparison purposes, ten other well-known optimization algorithms are used. The design results obtained show the superiority of the proposed algorithm for the optimal design of CAA and for sidelobe reduction over many other algorithms.

[1]  Scott Kirkpatrick,et al.  Optimization by Simmulated Annealing , 1983, Sci..

[2]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[3]  G. S. N. Raju,et al.  Some studies on arrays for sidelobe reduction required for EMI control , 1995, 1995 International Conference on Electromagnetic Interference and Compatibility (INCEMIC).

[4]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[5]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[6]  Shu-Cherng Fang,et al.  An Electromagnetism-like Mechanism for Global Optimization , 2003, J. Glob. Optim..

[7]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[8]  M. Dessouky,et al.  EFFICIENT SIDELOBE REDUCTION TECHNIQUE FOR SMALL-SIZED CONCENTRIC CIRCULAR ARRAYS , 2006 .

[9]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[10]  M. Khodier,et al.  LINEAR AND CIRCULAR ARRAY OPTIMIZATION: A STUDY USING PARTICLE SWARM INTELLIGENCE , 2009 .

[11]  C. W. Wang.  Antenna Models for Electromagnetic Compatibility Analyses , 2012 .

[12]  H. Bouchekara.  Optimal design of electromagnetic devices using a black-hole-based optimization technique , 2013, IEEE Transactions on Magnetics.

[13]  Abdolreza Hatamlou,et al.  Black hole: A new heuristic optimization approach for data clustering , 2013, Inf. Sci..

[14]  Tamer Ölmez,et al.  A new metaheuristic for numerical function optimization: Vortex Search algorithm , 2015, Inf. Sci..

[15]  Sakti Prasad Ghoshal,et al.  Comparative Study of Circular and Hexagonal Antenna Array Synthesis Using Improved Particle Swarm Optimization , 2015 .

[16]  G. G. Devi,et al.  Application of Genetic Algorithm for Reduction of Sidelobes from Thinned Arrays , 2015 .

[17]  G S N Raju,et al.  Sidelobe level reduction in linear antenna array synthesis using Cuckoo Search & Accelerated Particle Swarm algorithms , 2016, 2016 International Conference on ElectroMagnetic Interference & Compatibility (INCEMIC).

[18]  Bilal Alatas,et al.  Sports inspired computational intelligence algorithms for global optimization , 2019, Artificial Intelligence Review.

[19]  Geng Sun,et al.  Beam pattern design of circular antenna array via efficient biogeography-based optimization , 2017 .

[20]  H. R. E. H. Bouchekara,et al.  Most Valuable Player Algorithm: a novel optimization algorithm inspired from sport , 2020, Oper. Res..