Flower Pollination Algorithm for Global Optimization

By Xin-She Yang. International Conference on Unconventional Computation and Natural Computation, 2012

2,223 citations recorded by the source corpus.

Flower pollination is an intriguing process in the natural world. Its evolutionary characteristics can be used to design new optimization algorithms. In this paper, we propose a new algorithm, namely, flower pollination algorithm, inspired by the pollination process of flowers. We first use ten test functions to validate the new algorithm, and compare its performance with genetic algorithms and particle swarm optimization. Our simulation results show the flower algorithm is more efficient than both GA and PSO. We also use the flower algorithm to solve a nonlinear design benchmark, which shows the convergence rate is almost exponential.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/94f32df65915ce1150ed21abc8c4bab822ca96ad

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Citations in the corpus

A survey on new generation metaheuristic algorithms (2019, background): “Yang (2012b) proposes a flower pollination algorithm, inspired by the pollination process of flowers.”
Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm (2015, methodology): “The results are followed by the FPA, PSO, and SMS algorithms.”
Red fox optimization algorithm (2021, methodology): “Flower Pollination Algorithm (FPA) proposed in Yang (2012), simulates the motion of pollen with the wind to search the space of solutions for the optimal point.”
An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges (2023, background): “FPA is adaptive, flexible, and easy to utilize in different optimization problems.”
Mapping the Landscape of Artificial Intelligence Applications against COVID-19 (2020, methodology): “For example, Al-qaness et al. (2020) combine an adaptive neuro-fuzzy inference system (ANFIS) (Jang, 1993) with an enhanced ﬂower pollination algorithm (FPA) (Yang, 2012) and salp swarm algorithm (SSA) (Mirjalili et al., 2017) to optimize the parameters of the model.”
Flower pollination algorithm: A novel approach for multiobjective optimization (2014, methodology): “Flower pollination algorithm (FPA) was developed by Xin-She Yang in 2012 (Yang 2012), inspired by the ﬂow pollination process of ﬂowering plants.”
Nature-Inspired Optimization Algorithms: Challenges and Open Problems (2020, background): “Random permutations are used in many algorithms, such as DE and FPA.”
Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems (2022, neutral): “…optimization (WDO) [38], cat swarm optimization (CSO) [39], fruit fly optimization algorithm (FFOA) [40], and flower pollination algorithm (FPA) [41], Salp swarm algorithm (SSA) [42], Pathfinder algorithm (PFA) [43] and its improved version [3], and Harris hawks optimization (HHO) Algorithm…”
Metaheuristic design of feedforward neural networks: A review of two decades of research (2017, methodology): “There are algorithms inspired by the behavior of animals, birds, and insects, such as gray wolf optimization (GWO) [113], flower pollination (FP) [114], cuckoo search (CS) [115], firefly (FF) [116], etc.”
Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems (2018, methodology): “A large number of nature-inspired metaheuristics have been proposed in the last decades [4], for example, Ant Colony Optimization (ACO) [5], the Artificial Bee Colony (ABC) [6], Bacterial Colony Foraging (BCF) [7], the Bat-Inspired Algorithm (BA) [8], the Dolphin Echolocation (DE) [9], the Firefly A”