Two new single/multi-objective multi-strategy algorithms for the parametric estimation of dual band-notched ultra wideband antennas

Abstract

This article introduces a novel single objective and a multi-objective multi-hybrid naked mole-rat (moIGDN) algorithm optimization techniques for solving numerical benchmarks and electromagnetic engineering problems. The hybridization was introduced to overcome the basic naked mole-rat algorithm’s (NMRA’s) local optima stagnation and poor exploration problems. The global exploration search equations of INFO, gazelle optimization algorithm (GOA), and dwarf mongoose optimization algorithm (DMO) have been added into the NMRA’s worker phase. Additionally, five mutation operators have been introduced for parametric enhancements and adaptations. For single-objective performance evaluation, three benchmark datasets namely classical benchmark, CEC 2014, CEC 2017, and CEC 2022 have been analysed. A comparison with JADE, SaDE, CMA-ES, success history based DE (SHADE), fractional-order calculus-based FPA (FA-FPO), LSHADE-SPACMA, NL-SHADE-LBC, evolutionary algorithms with eigen crossover (EA4eig), S-LSHADE-DP, L-SHADE-RSP-MID, EBOwithCMAR, LSHADE-EpSin among others. Apart from that, the proposed moIGDN algorithm was utilized to optimize the design of two electromagnetic multi-objective printed monopole antennas categorized as basic ultra-wideband (B-UWB) and dual band-notched ultra-wideband (DBN-UWB) antennas. Simulations have been performed by using the EM-MATLAB optimization interface with two objective functions: trying to minimize pass-band signal reflection and maximizing antenna gain, by mapping solutions to the Pareto-front boundary in the objective space. The best obtained antenna structure had an impedance bandwidth of 2.92–11.96 GHz (fractional bandwidth of 122%), efficient band-rejection characteristics for the WI-MAX (3.3 GHz–3.7 GHz) and WLAN (5.15 GHz–5.85 GHz) bands, and a compact geometry. Based on simulation results, moIGDN is found to be effective and can be utilized to solve complex wireless communication and advanced engineering optimization problems.