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flower pollination algorithm development: a state of art review

INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2017年第2期8卷 1858-1866页

作者： Pant, Sangeeta Kumar, Anuj Ram, Mangey Univ Petr & Energy Studies Dept Math Dehra Dun 248007 India Graph Era Univ Dept Math Dehra Dun 248002 India

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.

关键词： flower pollination algorithm Nature inspired optimization Levy distribution Hybrid algorithms

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flower pollination algorithm for Detection of Epistasis Associated with a Phenotype 12

Flower Pollination Algorithm for Detection of Epistasis Asso...

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14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) / 12th International Conference on Bioinformatics Models, Methods and algorithms (BIOINFORMATICS)

作者： Sitarcik, Jozef Lucka, Maria Krajcovic, Tibor Slovak Univ Technol Bratislava Fac Informat & Informat Technol Ilkovicova 2 Bratislava Slovakia KInIT Kempelen Inst Intelligent Technol Mlynske Nivy 5 Bratislava 81109 Slovakia

ISBN: (纸本)9789897584909

Detecting associations of SNPs with traits like complex diseases can provide valuable insights. However, due to the epistases - complex interactions between SNPs - SNP combinations need to be evaluated for their association with a trait. As the number of possible SNP combinations grows rapidly with increase of the number of SNPs, great computational challenges have to be tackled. In this paper, we propose FPepi, epistasis detection tool based on flower pollination algorithm with multiple objectives. Two variants of the algorithm are proposed, one using Gini score and K2 score as objectives, while the second variant uses K2 score and mutual information score. The flower pollination algorithm selects a small subset of potential SNP combinations, that are then evaluated by G-test. The proposed tool shown better results in detection power when compared with other similar tools.

关键词： Epistasis Detection flower pollination algorithm Single Nucleotide Polymorphisms

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flower pollination algorithm for Distribution Network Optimization Scheduling Including Microgrid 8

Flower Pollination Algorithm for Distribution Network Optimi...

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IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Zhan, Fangping Xiong, Hejin Chen, Feihu Wuhan Univ Technol Coll Automat Wuhan Hubei Peoples R China

ISBN: (纸本)9781538681787

In this paper, the optimal dispatching of distribution network with microgrid is studied. It is predicted that the output power of photovoltaic microgrid is the basis of optimal dispatching of active distribution network. The flower pollination algorithm with minimum cost and maximum flow and gravity search mechanism is applied to distribution network with microgrid. It is optimized and dispatched, mainly to solve the active interaction power of the microgrid and the distribution network, the active power of the controllable distributed power source, the optimal solution of the power grid purchase power of the distribution network and the network loss of the distribution network.

关键词： Distribution network optimization scheduling microgrid gravity search mechanism flower pollination algorithm

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flower pollination algorithm FOR REACTIVE POWER DISPATCH USING UNIFIED POWER FLOW CONTROLLER 7

FLOWER POLLINATION ALGORITHM FOR REACTIVE POWER DISPATCH USI...

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7th International Conference on Electrical Energy Systems (ICEES)

作者： Arun, G. Babu, E. Lakshmipriya, N. Hema, P. Shriram Engn Coll Elect & Elect Engn Tiruppur India

ISBN: (纸本)9781728176123

One of the most important tasks needed for proper operation and control of a power system is reactive power or Volt Ampere Reactance (VAR). By changing the reactive power control variables by Generator Voltages, Transformers tap settings and other reactive power sources such as Capacitor Banks and FACTS Units, reactive power management is carried out to minimize the losses in a power system. Reactive power VAR control offers improved power transfer capacity and overall device activity for system security enhancement. VAR is a complex problem of combinational optimization which involves non-linear functions with several local minimum and non-linear and discontinuous *** order to minimize real power losses, the VAR or reactive power problem is formulated as a non-linear restricted optimization problem with equality and inequality constraints. The problem is solved using the algorithm for flower pollination. The flower pollination algorithm is a new algorithm inspired by nature, which is a population based on flowering plant *** this paper, we extend this flower algorithm to solve problems in engineering for reactive power optimization. The Truth devices known as the Unified Power Flow Controller are used to mitigate actual power losses. This UPFC is a mixture of SVC and TCSC that is used in the system as a shunt and series controller. The testing system used here is the IEEE 57 Bus Test System.

关键词： flower pollination algorithm Reactive power dispatch UPFC IEEE 57 Bus test system

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flower pollination algorithm to solve combined economic and emission dispatch problems

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2016年第2期19卷 980-990页

作者： Abdelaziz, A. Y. Ali, E. S. Abd Elazim, S. M. Ain Shams Univ Elect Power & Machine Dept Fac Engn Cairo Egypt Zagazig Univ Elect Power & Machine Dept Fac Engn Zagazig Egypt

Economic Load Dispatch (ELD) is the process of allocating the required load between the available generation units such that the cost of operation is minimized. The ELD problem is formulated as a nonlinear constrained optimization problem with both equality and inequality constraints. The dual-objective Combined Economic Emission Dispatch (CEED) problem is considering the environmental impacts that accumulated from emission of gaseous pollutants of fossil-fuelled power plants. In this paper, an implementation of flower pollination algorithm (FPA) to solve ELD and CEED problems in power systems is discussed. Results obtained by the proposed FPA are compared with other optimization algorithms for various power systems. The results introduced in this paper show that the proposed FPA outlasts other techniques even for large scale power system considering valve point effect in terms of total cost and computational time. (C) 2016, Karabuk University. Publishing services by Elsevier B.V.

关键词： flower pollination algorithm Economic load dispatch Combined economic emission dispatch Emission constraints Valve point loading effect Swarm intelligence

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flower pollination algorithm for the optimization of stair casting parameter for the preparation of AMC

Flower Pollination Algorithm for the optimization of stair c...

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International Conference on Recent Trends in Nanomaterials for Energy, Environmental and Engineering Applications (ICONEEEA)

作者： Adithiyaa, T. Chandramohan, D. Sathish, T. St Peters Inst Higher Educ & Res Dept Mech Engn Chennai 600054 Tamil Nadu India SMR East Coast Coll Engn & Technol Dept Mech Engn Thanjavur 614612 Tamil Nadu India

Aluminium Matrix Composite (AMC) is an encouraging metal matrix composition method for the preparation of most of the commercial metal. The AMC preparation with nitride or oxide nano particle has advantage to boost the hardness and strength of the metal. But the preparation of actual or exact composition of reinforcement material is challenging task. Hence optimization is employed in this paper to select the optimal parameter of the stair casting process of AMC preparation. In the proposed work the flower pollination algorithm (FPA) is used for the optimal parameter selection. The optimization is employed based on the hardness and strength. Ultimately the performance of the proposed technique proves the effectiveness in the stair casting parameter optimization. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： flower pollination algorithm Parameter optimization Stair casting Aluminium matrix composite Metal matrix composite

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flower pollination algorithm based on triple differential evolution and its application in Research on the classification and leveling of power cloud data 24

Flower pollination Algorithm based on triple differential ev...

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16th International Conference on Machine Learning and Computing (ICMLC)

作者： Han, Jiamao Wei, Yaxing Zhen, Yingchao Shi, Ji Wang, Lei Zhang, Yu Sun, Wenbo Wu, Rong DIGITAL Res Co INNER MONGOLIA POWER Hohhot 010000 Peoples R China

ISBN: (纸本)9798400709234

For the clustering problem of data mining, this paper designs a flower pollination algorithm based on triple differential evolution (FPA-TDE). FPA-TDE uses DE/best/1, DE/best/2 and DE/rand/2 to enhance the search orientation and population richness of global search process. In local search process, DE/rand/1 and DE/current-to-rand/1 are used to improve the optimization accuracy, and the MDE strategy is designed to improve the convergence speed while maintaining the searching continuity. In addition, the external archive is applied to dynamically update the scaling factors of all differential evolution strategies. It forces the search process and guides individuals to search for potential high-quality solution subregions, so as to avoid trapping into local optimum. The extensible benchmark functions are used to verify the optimization performance of FPA-TDE. The results show that FPA-TDE can maintain good optimization performance and operation efficiency under different dimensional benchmark functions. Moreover, FPA-TDE is applied to solve clustering problem of data mining, and the experimental results show that the algorithm can satisfy the clustering requirements of high accuracy and stability.

关键词： flower pollination algorithm differential evolution power cloud data clustering

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A Differential Evolution flower pollination algorithm with Dynamic Switch Probability

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Chinese Journal of Electronics 2019年第4期28卷 737-747页

作者： LIU Jingsen LIU Li LI Yu Institute of Intelligent Network system Henan University College of Software Henan University Institute of Management Science and Engineering Henan University

For the shortcomings of the basic flower pollination algorithm, this paper proposes a differential evolution flower pollination algorithm with dynamic switch probability based on the Weibull *** new algorithm improved the convergence rate and precision. The switch probability is improved by Weibull distribution function combined with the number of iterations. It can balance the relationship between the global pollination and the local pollination to improve the overall optimization performance of the *** mutation operator is merged into the global pollination process to increase diversity of the population,enhance the ability of the algorithm’s global search and avoid premature convergence. In the process of local pollination, directed mutation and crossover operation of the differential evolution are incorporated, it makes the individual flower position update with the memory function, which can choose the direction of variation reasonably. The use of cross-operation can avoid new solutions crossing the boundary. Convergence rate is improved and the algorithm can approach the global optimal solution continuously. Theoretical analysis proved the convergence and time complexity of the improved algorithm. The simulation results based on the function optimization problem show that the improved algorithm has better performance of optimization, faster convergence speed and higher convergence accuracy.

关键词： flower pollination algorithm Dynamic switch probability Random mutation operator Differential evolution Optimization accuracy Convergence rate

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Binary Hybrid Artificial Hummingbird with flower pollination algorithm for Feature Selection in Parkinson’s Disease Diagnosis

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Journal of Bionic Engineering 2024年第2期21卷 1003-1021页

作者： Liuyan Feng Yongquan Zhou Qifang Luo College of Artificial Intelligence Guangxi University for NationalitiesNanning530006China Xiangsihu College of Guangxi University for Nationalities Nanning532100GuangxiChina Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis Nanning530006China

Parkinson’s disease is a neurodegenerative disorder that inflicts irreversible damage on *** experimental data regarding Parkinson’s patients are redundant and irrelevant,posing significant challenges for disease ***,there is a need to devise an effective method for the selective extraction of disease-specific information,ensuring both accuracy and the utilization of fewer *** this paper,a Binary Hybrid Artificial Hummingbird and flower pollination algorithm(FPA),called BFAHA,is proposed to solve the problem of Parkinson’s disease diagnosis based on speech ***,combining FPA with Artificial Hummingbird algorithm(AHA)can take advantage of the strong global exploration ability possessed by FPA to improve the disadvantages of AHA,such as premature convergence and easy falling into local ***,the Hemming distance is used to determine the difference between the other individuals in the population and the optimal individual after each iteration,if the difference is too significant,the cross-mutation strategy in the genetic algorithm(GA)is used to induce the population individuals to keep approaching the optimal individual in the random search process to speed up finding the optimal ***,an S-shaped function converts the improved algorithm into a binary version to suit the characteristics of the feature selection(FS)*** this paper,10 high-dimensional datasets from UCI and the ASU are used to test the performance of BFAHA and apply it to Parkinson’s disease *** with other state-of-the-art algorithms,BFAHA shows excellent competitiveness in both the test datasets and the classification problem,indicating that the algorithm proposed in this study has apparent advantages in the field of feature selection.

关键词： Artificial Hummingbird algorithm flower pollination algorithm Feature selection Parkinson’s disease Meta-heuristic

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A binary clonal flower pollination algorithm for feature selection

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PATTERN RECOGNITION LETTERS 2016年 77卷 21-27页

作者： Sayed, Safinaz AbdEl-Fattah Nabil, Emad Badr, Amr Cairo Univ Dept Comp Sci Cairo Egypt

Feature selection problem has been detected essentially in the last years. It is a step that is considered the prerequisite of the classification step. For the feature selection problem, the goal is to find out the most important subset of features that represent the original features in a certain domain. The selected features are used in optimization of a certain fitness function, so the feature selection problem can be seen as an optimization problem. This paper presents a new hybrid algorithm that combines Clonal Selection algorithm (CSA) with flower pollination algorithm (FPA) to compose Binary Clonal flower pollination algorithm (BCFA) to solve the feature selection problem. The accuracy of the Optimum-Path Forest (OPF) classifier is used as an objective function. The experiments were implemented on three public datasets and demonstrated that the proposed hybrid algorithm achieved remarkable results in comparison with other well-known algorithms such as Binary Cuckoo Search algorithm (BCSA), Binary Bat algorithm (BBA), Binary Differential Evolution algorithm (BDEA) and Binary flower pollination algorithm (BFPA). (C) 2016 Elsevier B.V. All rights reserved.

关键词： Feature selection Clonal Selection algorithm flower pollination algorithm Optimum Path Forest

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