Biogeography in optimization algorithms: a closer look

Abstract

<span>Biogeography can be broken down into bio and geography, which would imply the geography, i.e., the dispersion of biological organisms. The entire field of biology inspired algorithm is inclined towards providing the most optimal solution for a given problem set. Computer science experts want to always learn from the surroundings. Nature is sporadic and spontaneous and the erratic nature of a habitat is the very differentiating factor between a real world and an ideal world problem. Things change and that nothing remains constant. The diversification of a certain habitat is bound to change through external influences, some for the better, and some for the worse. This paper tries to mimic the natural influences in a habitat in a python environment and try to come up with a minimal objective value after iterating through the given metaheuristic algorithm.</span>

Microstrip antenna optimization using evolutionary algorithms

“…The binary numbers are applied to the antenna structure to generate an optimized solution. The frequency shifts from 4.9 to 2.16 GHz with the miniaturization shape of a rectangular microstrip antenna [33]. The optimized antenna could be dual-polarized and broadband.…”