The recent growth of metaheuristic search strategies has brought huge progress in the domain of computational optimization. The breakthrough started since the well-known Particle Swarm Optimization algorithm had been introduced and examined. The optimization technique presented in this contribution mimics the process of flower pollination. It is built on the foundation of the first technique of this kind—known as Flower Pollination Algorithm (FPA). In this paper, its simplified and improved version, obtained after extensive performance testing, is presented. It is based on only one natural phenomenon—called flower constancy—the natural mechanism allowing pollen-carrying insects to remember the positions of the best pollen sources. Modified FPA, named as Biotic Flower Pollination Algorithm (BFPA) and relying solely on biotic pollinators, outperforms original FPA, which itself proved to be a very effective approach. The paper first presents a short description of the original FPA and the changes leading to the Biotic Flower Pollination Algorithm. It also discusses the performance of the modified algorithm on a full set of CEC17 benchmark functions. Furthermore, in that aspect, a comparison between BFPA and other optimization algorithms is also given. Finally, a brief exemplary application of the modified algorithms in the field of probabilistic modeling, related to physics and engineering, is also presented.
