Flower Pollination Algorithm: Basic Concepts, Variants, and Applications

Abstract

AbstractThe nature-inspired metaheuristic optimization algorithms have gained a significant research interest in solving several various nonlinear design problems in the field of science and engineering. This is attributed to its efficiency and simplicity in finding for either the global optima or near-global optima of various optimization design problems. The researchers are applying the optimization theory by formulating optimization algorithms that are inspired by nature and could be employed as optimization tools for engineering design problems. Among the numerous nature-inspired metaheuristic algorithms, the flower pollination optimization algorithm (FPA), motivated by the cross- and self-pollination processes of flowering plants and formulated by Yang in 2012 has gained significant research interest in finding globally optimal solutions. The advantages of FPA such as its simplicity, convergence toward the global optima, fast speed, and better success rate results in the numerous variants of FPA in solving the many NP-complete problems by some modifications or hybridization with other nature-inspired metaheuristic optimization algorithms. The chapter aims to inspire the researchers to solve challenging issues and complex engineering design problems that need to be a focus in the future and can be formulated by FPA and its new variants. Consequently, this chapter is very useful for the researchers as it provides the basic concept of flower pollination, its conversion into an optimization algorithm, FPA parametric studies, its variants, and applications of FPA in various domains.KeywordsFlower pollination algorithmHybridizationMetaheuristicsNature-inspired optimization algorithmOptimizationPareto optimal