Multi-Objective Optimization Algorithm Develpoment using Chaotic Maps to Design of a Planar Microstrip Monopole Antenna
Subject Areas : Antenna Design and Application
Vahid Hosseini
1
,
Yousef Farhang
2
,
Kambiz Majidzadeh
3
,
Changiz Ghobadi
4
1 - Department of Computer Engineering, Payame Noor University, Tehran, Iran.
2 - Faculty of Engineering and Architecture, Department of Computer engineering, Istanbul Esenyurt University, Turkey.
3 - Computer Engineering Department, Urmia Branch, Islamic Azad University, Urmia, Iran
4 - Department of Electrical Engineering, Urmia University, Urmia, Iran
Keywords: Optimization Algorithm, Chaotic Map, Monopole Antenna, PSO and GA.,
Abstract :
This research uses a new multi-objective optimization algorithm to design a single pole antenna with specific electromagnetic characteristics. This algorithm uses a hybrid chaotic function to integrate the customized mutated particle swarm algorithm with the modified genetic algorithm. By avoiding getting trapped in local minima, the new hybrid approach achieves desired results faster than conventional particle swarm algorithms and genetic algorithms. The performance of the proposed meta-heuristic algorithm has been successfully simulated and stabilized using benchmark functions such as Rastrigen's function, Ackley's function, Rosenbrook's function, and Booth's function. Finally, the validity of the presented approach for electromagnetic applications is demonstrated by optimizing a planar microstrip monopole antenna with a simple structure. The proposed algorithm allows the optimization criteria to be customized to achieve the predetermined results for return loss and resonance frequency. The optimization algorithm developed in MATLAB is used to determine the necessary parameter settings in order to achieve the expected frequency bands using custom mutated particle swarm algorithm or heuristic modified genetics. The dimensions of the proposed antenna elements significantly affect the antenna performance.
Integrating metaheuristic algorithms using chaotic maps to overcome challenges in algorithm combination.
Enhancement of the classical Genetic Algorithm for improved performance.
Development of a multi-objective hybrid optimization algorithm with superior performance.
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