Design optimization of CPW-fed microstrip patch antenna using constrained ABFO algorithm

This paper explores the potential of bio-inspired soft computational technique known as adaptive bacterial foraging optimization (ABFO) for the joint optimization of geometrical parameters of compact coplanar waveguide (CPW)-fed microstrip patch antenna with defected ground structure. The presented research work is divided into three phases. In the initial phase, the intended antenna is designed and analyzed using finite element-based electromagnetic simulator Ansoft HFSS 15.0. In the subsequent phase, the analytical equations of various design parameters are modeled using curve fitting technique in MATLAB and root mean square error-based fitness functions are derived for individual design parameters. Then, a joint cost function is formulated from individual fitness functions for evaluation in optimization algorithm. Adaptive BFO is an improvement in classical BFO algorithm that dynamically adjusts the run-length unit parameter to maintain the balance between exploration–exploitation trade-off. In the final phase, a variation in the adaptive BFO algorithm termed as ‘constrained ABFO’ is projected and designed to suit the bounded constraints imposed by anticipated antenna structure. The modified algorithm is efficaciously used for joint optimization of specific design parameters to transform ‘dual-band performance’ into ‘broadband performance’ for high-speed point-to-point wireless services. The performance of design optimization using constrained ABFO is compared with the original BFO, particle swarm optimization (PSO), hybrid bacterial foraging–particle swarm optimization (BF-PSO), invasive weed optimization (IWO) and artificial bee colony (ABC) techniques to scrutinize its adequacy.