Constrained Multi-Objective Genetic Algorithm Variants for Design and Optimization of Tri-Band Microstrip Patch Antenna loaded CSRR for IoT Applications: A Comparative Case Study

TL;DR

This work tackles the automated design of tri-band microstrip patch antennas loaded with CSRRs intended for IoT by benchmarking five MOGA variants (PGA, NSGA-I/II/III, SPEA) against a scalarized weighted-sum GA within a MATLAB-CST co-simulation. The scalarized approach directly optimizes a single fitness combining $S_{11}$ at $2.4$, $3.6$, and $5.2$ GHz, achieving deep resonances with $S_{11}$ values of $-21.56$ dB, $-16.60$ dB, and $-27.69$ dB and gains up to $3.99$ dBi, while reducing antenna size by about 16.3%. NSGA-II and NSGA-III generally deliver the best convergence and diversity among the Pareto-based methods, whereas PGA, NSGA-I, and SPEA show weaker or unstable performance. Overall, the constrained scalarized MOGA provides a robust, fast-converging path to compact, multi-band IoT antennas and demonstrates the value of converting multi-objective antenna design into a single objective when appropriate constraints are imposed.

Abstract

This paper presents an automated antenna design and optimization framework employing multi-objective genetic algorithms (MOGAs) to investigate various evolutionary optimization approaches, with a primary emphasis on multi-band frequency optimization. Five MOGA variants were implemented and compared: the Pareto genetic algorithm (PGA), non-dominated sorting genetic algorithm with niching (NSGA-I), non-dominated sorting genetic algorithm with elitism (NSGA-II), non-dominated sorting genetic algorithm using reference points (NSGA-III), and strength Pareto evolutionary algorithm (SPEA). These algorithms are employed to design and optimize microstrip patch antennas loaded with complementary split-ring resonators (CSRRs). A weighted-sum scalarization approach was adopted within a single-objective genetic algorithm framework enhanced with domain-specific constraint handling mechanisms. The optimization addresses the conflicting objectives of minimizing the return loss ($S_{11} < -10$~dB) and achieving multi-band resonance at 2.4~GHz, 3.6~GHz, and 5.2~GHz. The proposed method delivers a superior overall performance by aggregating these objectives into a unified fitness function encompassing $S_{11}$(2.4~GHz), $S_{11}$(3.6~GHz), and $S_{11}$(5.2~GHz). This approach effectively balances all three frequency bands simultaneously, rather than exploring trade-off solutions typical of traditional multi-objective approaches. The antenna was printed on a Rogers RT5880 substrate with a dielectric constant of 2.2 , loss tangent of 0.0009 , and thickness of 1.57~mm . Scalarization approach achieved return loss values of $-21.56$~dB, $-16.60$~dB, and $-27.69$~dB, with corresponding gains of 1.96~dBi, 2.6~dB, and 3.99~dBi at 2.4~GHz, 3.6~GHz, and 5.2~GHz, respectively.

Figures (11)

• Figure 1: The architecture of our proposed approach
• Figure 2: Reference microstrip patch antenna at 5.2 GHz (a) Geometry, (b) Reflection Coefficient $S_{11}$
• Figure 3: Evolution of the microstrip patch antenna with CSRR: (a) Stage 1, (b) Stage 2, (c) schematic representation of the proposed multiband antenna, and (d) reflection coefficient $S_{11}$ for the various design stages.
• Figure 4: Geometry of the proposed antenna: (a) top view and (b) bottom view
• Figure 5: Reflection Coefficient $S_{11}$ of the Proposed Antenna