Variants of the Flower Pollination Algorithm: A Review (original) (raw)
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Recent Advances in Flower Pollination Algorithm
International Journal of Computer Applications Technology and Research, 2016
Flower Pollination Algorithm (FPA) is a nature inspired algorithm based on pollination process of plants. Recently, FPA has become a popular algorithm in the evolutionary computation field due to its superiority to many other algorithms. As a consequence, in this paper, FPA, its improvements, its hybridization and applications in many fields, such as operations research, engineering and computer science, are discussed and analyzed. Based on its applications in the field of optimization it was seemed that this algorithm has a better convergence speed compared to other algorithms. The survey investigates the difference between FPA versions as well as its applications. To add to this, several future improvements are suggested.
A Review of the Applications of Bio-Inspired Flower Pollination Algorithm
The Flower Pollination Algorithm (FPA) is a novel bio-inspired optimization algorithm that mimics the real life processes of the flower pollination. In this paper, we review the applications of the Single Flower Pollination Algorithm (SFPA), Multi-objective Flower Pollination Algorithm an extension of the SFPA and the Hybrid of FPA with other bio-inspired algorithms. The review has shown that there is still a room for the extension of the FPA to Binary FPA. The review presented in this paper can inspire researchers in the bio-inspired algorithms research community to further improve the effectiveness of the PFA as well as to apply the algorithm in other domains for solving real life, complex and nonlinear optimization problems in engineering and industry. Further research and open questions were highlighted in the paper.
Flower pollination algorithm development: a state of art review
International Journal of System Assurance Engineering and Management, 2017
The journey of a modern man from a troglodyte is due to human nature to try to unfold the mysteries of nature to improve the lives of human beings. A few years back we even can't think that school of fish, genes, nature of bat or ant can be used to design optimization algorithms. As nature has the solution of every problem. Researchers working on optimization theory are developing optimization techniques which are inspired by nature and could be utilized as optimization tools for engineering problems. Recently, flower pollination algorithm, which is inspired by the pollination characteristics of flowering plants and associated flower constancy of some pollinating insects, caught the eye of many researchers in the world of optimization. This paper presents a brief review about the algorithm its developments and applications. In the last part of this paper, the authors have listed the limitations and topics within FPA that the authors consider as promising areas of future research.
Flower Pollination Algorithm for Global Optimization
Unconventional Computation and Natural Computation, 2012
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.
A Modified Flower Pollination Algorithm for Global Optimization
Expert and intelligent systems try to simulate intelligent human experts in solving complex real-world problems. The domain of problems varies from engineering and industry to medicine and education. In most situations, the system is required to take decisions based on multiple inputs, but the search space is usually very huge so that it will be very hard to use the traditional algorithms to take a decision; at this point, the metaheuristic algorithms can be used as an alternative tool to find near-optimal solutions. Thus, inventing new metaheuristic techniques and enhancing the current algorithms is necessary. In this paper, we introduced an enhanced variant of the Flower Pollination Algorithm (FPA). We hybridized the standard FPA with the Clonal Selection Algorithm (CSA) and tested the new algorithm by applying it to 23 optimization benchmark problems. The proposed algorithm is compared with five famous optimization algorithms, namely, Simulated Annealing, Genetic Algorithm, Flower Pollination Algorithm, Bat Algorithm, and Firefly Algorithm. The results show that the proposed algorithm is able to find more accurate solutions than the standard FPA and the other four techniques. The superiority of the proposed algorithm nominates it for being a part of intelligent and expert systems.
Flower Pollination Algorithm: An Introduction
2014
This presentation explains the fundamental ideas of the standard Flower Pollination Algorithm (FPA), which also contains the links to the free Matlab codes at Mathswork file exchanges and the animations of numerical simulations (video at Youtube). An example of multi-objective flower pollination algorithm (MOPFA) is also given with link to the Matlab code.
A Novel Flower Pollination Algorithm based on Genetic Algorithm Operators
Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016
The Flower Pollination Algorithm (FPA) is a new natural bio-inspired optimization algorithm that mimics the real-life processes of the flower pollination. Thus, the latter has a quick convergence, but its population diversity and convergence precision can be limited in some applications. In order to improve its intensification (exploitation) and diversification (exploration) abilities, we have introduced a simple modification in its general structure. More precisely, we have added both Crossover and Mutation Genetic Algorithm (GA) operators respectively, just after calculating the new candidate solutions and the greedy selection operation in its basic structure. The proposed method, called FPA-GA has been tested on all the CEC2005 contest test instances. Experimental results show that FPA-GA is very competitive.
Nature is a rich source of inspiration, which has inspired many researchers in many ways. Nowadays, new algorithms have been developed by the inspiration from nature. The flower pollination algorithm is based on the characteristics of pollination process of flowers plants. Pollination is a natural biological process of mating in plants. In flowers, pollen is carried to stigma through some mechanisms that confirm a proper balance in the genetic creations of the species. Another nature inspired algorithm — the Bat algorithm is based on the echolocation behavior of bats. In this paper, the Flower pollination algorithm is compared with the basic Bat algorithm. We have tested these two algorithms on both unimodal and multimodal, low and high dimensional continuous functions. Simulation results suggest that the Flower pollination algorithm can perform much better than the Bat algorithm on the continuous optimization problems.
Application of mutation operators to flower pollination algorithm
Expert Systems with Applications, 2017
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights A new concept based on mutation operators is applied to Flower Pollination Algorithm (FPA). Based on mutation, five new variants of FPA are proposed. Dynamic switch probability is used in all the proposed variants. Benchmarking of Variants with respect to standard FPA. Benchmarking and statistical testing of the best variant with respect to state-of-the-art algorithms.
A Hybrid Flower Pollination Algorithm for Engineering Optimization Problems
International Journal of Computer Applications, 2016
Flower pollination algorithm (FP) is a new nature-inspired algorithm, based on the characteristics of flowering plants. Combining with the features of flower pollination algorithm, an improved simulated annealing algorithm is proposed in this paper (FPSA). It can improve the speed of annealing. The initial state of simulated annealing and new solutions are generated by flower pollination. Therefore, it has the advantage of high quality and efficiency. The method combines the standard flower pollination algorithm (FP) with simulated annealing to enhance the search performance and speeds up the global convergence rate. Structural engineering optimization problems are presented to demonstrate the effectiveness and robustness of the proposed algorithm. The experimental results showed that the accuracy of finding the best solution and convergence speed performance of the proposed algorithm is competitive to those achieved by the existing algorithms.