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Optimization of Linear Arrays using Modified Social Group Optimization Algorithm


Affiliations
1 Department of ECE, Centurion University of Technology and Management Andhra Pradesh, AP, India
2 Centurion University of Technology and Management Andhra Pradesh, AP, India
3 Centurion University of Technology and Management, Odisha, India

In this paper, optimization of the linear array (LA) antenna is performed using modified social group optimization algorithm (SGOA). First step of the work involves in transforming the electromagnetic engineering problem to an optimization problem which is completely described in terms of objectives. Linear array synthesis is inherently considered as a multi-attribute problem. The pattern synthesis of LA is carried out with several objectives involving sidelobe level (SLL), beam-width (BW) and desired nulls. The SLL suppression with BW constraint is considered as first objective of this work and the results are compared with several evolutionary computing algorithms like ant lion (ALO), grey wolf (GWO) and root-runner (RRA). Following this, the MSGOA is further used to synthesise null patterns in which the pattern is completely described in terms of nulls with SLL and BW as constraints. The entire simulation-based experimentation is performed using Matlab® on i5 computing system.
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  • Optimization of Linear Arrays using Modified Social Group Optimization Algorithm

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Authors

E V S D N S L K Srikala
Department of ECE, Centurion University of Technology and Management Andhra Pradesh, AP, India
M Murali
Centurion University of Technology and Management Andhra Pradesh, AP, India
M Vamshi Krishna
Centurion University of Technology and Management, Odisha, India
G S N Raju
Centurion University of Technology and Management Andhra Pradesh, AP, India

Abstract


In this paper, optimization of the linear array (LA) antenna is performed using modified social group optimization algorithm (SGOA). First step of the work involves in transforming the electromagnetic engineering problem to an optimization problem which is completely described in terms of objectives. Linear array synthesis is inherently considered as a multi-attribute problem. The pattern synthesis of LA is carried out with several objectives involving sidelobe level (SLL), beam-width (BW) and desired nulls. The SLL suppression with BW constraint is considered as first objective of this work and the results are compared with several evolutionary computing algorithms like ant lion (ALO), grey wolf (GWO) and root-runner (RRA). Following this, the MSGOA is further used to synthesise null patterns in which the pattern is completely described in terms of nulls with SLL and BW as constraints. The entire simulation-based experimentation is performed using Matlab® on i5 computing system.