An Improved Flower Pollination Algorithm with Chaos Theory for Function Optimization

Flower pollination algorithm (FPA) is one of the newly developed bio-inspired optimization approaches, inspiring by the flower pollination process. In this study, an improved FPA was developed, in which the utilization of the chaos theory was performed, such that the variety of the initial population was enhanced. Ten different chaotic maps were considered, and various benchmark functions were employed to validate the performance of the chaotic FPA. Comparison results indicated that integration of the FPA with chaos theory led to improvement in terms of convergence characteristic, where the Circle chaotic map gave the best optimization performance.