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Journal articles on the topic 'Flower Pollination Optimization'

Author: Grafiati

Published: 3 June 2025

Last updated: 20 July 2025

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Strelin, Marina M., Federico Sazatornil, Santiago Benitez-Vieyra, and Mariano Ordano. "Bee, hummingbird, or mixed-pollinated Salvia species mirror pathways to pollination optimization: a morphometric analysis based on the Pareto front concept." Botany 95, no. 2 (2017): 139–46. http://dx.doi.org/10.1139/cjb-2016-0145.

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Optimization of flower phenotypes to ensure pollination by agents differing in their match with fertile flower structures can involve fitness trade-offs if the aspects of the phenotype that enhance the fitness contribution of one pollinator are detrimental for pollination by the other agents. If these trade-offs are substantial, flower optimization for specialized pollination is expected. However, optimization for generalized pollination may also take place in trade-off scenarios, as long as the joint contribution of two or more types of pollinators to global pollination fitness is greater than each individual contribution. We used an observational approach to evaluate the role of pollination fitness trade-offs in flower trait optimization, a matter seldom addressed because of the difficulties in conducting experiments. A pattern-searching tool based on the Pareto front concept, borrowed from the fields of economics and engineering, was used to test for fitness trade-off patterns in the flower shape of four Salvia (Lamiaceae) species. Two are pollinated exclusively either by bees or by hummingbirds; the remaining species have mixed-pollination systems, with varying contributions of bee and hummingbird pollination. The patterning of flower shape in this study suggests a bee–hummingbird pollination trade-off in Salvia, and the optimization of generalized flower shapes.

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Syafruddin, Willa Ariela, Rio Mukhtarom Paweroi, and Mario Köppen. "Behavior Selection Metaheuristic Search Algorithm for the Pollination Optimization: A Simulation Case of Cocoa Flowers." Algorithms 14, no. 8 (2021): 230. http://dx.doi.org/10.3390/a14080230.

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Since nature is an excellent source of inspiration for optimization methods, many optimization algorithms have been proposed, are inspired by nature, and are modified to solve various optimization problems. This paper uses metaheuristics in a new field inspired by nature; more precisely, we use pollination optimization in cocoa plants. The cocoa plant was chosen as the object since its flower type differs from other kinds of flowers, for example, by using cross-pollination. This complex relationship between plants and pollinators also renders pollination a real-world problem for chocolate production. Therefore, this study first identified the underlying optimization problem as a deferred fitness problem, where the quality of a potential solution cannot be immediately determined. Then, the study investigates how metaheuristic algorithms derived from three well-known techniques perform when applied to the flower pollination problem. The three techniques examined here are Swarm Intelligence Algorithms, Individual Random Search, and Multi-Agent Systems search. We then compare the behavior of these various search methods based on the results of pollination simulations. The criteria are the number of pollinated flowers for the trees and the amount and fairness of nectar pickup for the pollinator. Our results show that Multi-Agent System performs notably better than other methods. The result of this study are insights into the co-evolution of behaviors for the collaborative pollination task. We also foresee that this investigation can also help farmers increase chocolate production by developing methods to attract and promote pollinators.

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Zhou, Haili, Junlang Ou, Penghao Meng, Junhua Tong, Hongbao Ye, and Zhen Li. "Reasearch on Kiwi Fruit Flower Recognition for Efficient Pollination Based on an Improved YOLOv5 Algorithm." Horticulturae 9, no. 3 (2023): 400. http://dx.doi.org/10.3390/horticulturae9030400.

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A close relationship has been observed between the growth and development of kiwi fruit and the pollination of the kiwi flower. Flower overlap, flower tilt, and other problems will affect this plant’s pollination success rate. A pollination model based on YOLOv5 was developed to improve the pollination of kiwi flowers. The K-means++ clustering method was used to cluster the anchors closer to the target size, which improved the speed of the algorithm. A convolutional block module attention mechanism was incorporated to improve the extraction accuracy with respect to kiwi flower features and effectively reduce the missed detection and error rates. The optimization of the detection function improves the recognition of flower overlap and the accuracy of flower tilt angle calculation and accurately determines flower coordinates, pollination point coordinates, and pollination angles. The experimental results show that the predicted value of the YOLOv5s model is 96.7% and that its recognition accuracy is the highest. Its mean average precision value is up to 89.1%, its F1 score ratio is 90.12%, and its memory requirements are the smallest (only 20 MB). The YOLOv5s model achieved the highest recognition accuracy as determined through a comparison experiment of the four sets of analysed models, thereby demonstrating its ability to facilitate the efficient target pollination of kiwi flowers.

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Xu, Wenhao, Zhicheng Ji, and Yan Wang. "A flower pollination algorithm for flexible job shop scheduling with fuzzy processing time." Modern Physics Letters B 32, no. 34n36 (2018): 1840113. http://dx.doi.org/10.1142/s0217984918401139.

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Considering the uncertainty in real manufacturing workshops, the fuzzy flexible job shop scheduling problem (fFJSP) is addressed, in which the triangular fuzzy number is used to represent the processing time. A discrete flower pollination algorithm (DFPA) is proposed in this paper to minimize the maximum fuzzy completion time. Flower pollination algorithm (FPA) is inspired by the pollination process of flowering plants, which realizes global search and local search by means of cross-pollination and self-pollination of flowers in nature. DFPA extends to FPA by introducing discrete operator during iterations. Simulation results on instances validate the effectiveness and feasibility of this algorithm compared with particle swarm optimization.

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Gálvez, Jorge, Erik Cuevas, and Omar Avalos. "Flower Pollination Algorithm for Multimodal Optimization." International Journal of Computational Intelligence Systems 10, no. 1 (2017): 627. http://dx.doi.org/10.2991/ijcis.2017.10.1.42.

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Hulens, Dries, Wiebe Van Ranst, Ying Cao, and Toon Goedemé. "Autonomous Visual Navigation for a Flower Pollination Drone." Machines 10, no. 5 (2022): 364. http://dx.doi.org/10.3390/machines10050364.

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In this paper, we present the development of a visual navigation capability for a small drone enabling it to autonomously approach flowers. This is a very important step towards the development of a fully autonomous flower pollinating nanodrone. The drone we developed is totally autonomous and relies for its navigation on a small on-board color camera, complemented with one simple ToF distance sensor, to detect and approach the flower. The proposed solution uses a DJI Tello drone carrying a Maix Bit processing board capable of running all deep-learning-based image processing and navigation algorithms on-board. We developed a two-stage visual servoing algorithm that first uses a highly optimized object detection CNN to localize the flowers and fly towards it. The second phase, approaching the flower, is implemented by a direct visual steering CNN. This enables the drone to detect any flower in the neighborhood, steer the drone towards the flower and make the drone’s pollinating rod touch the flower. We trained all deep learning models based on an artificial dataset with a mix of images of real flowers, artificial (synthetic) flowers and virtually rendered flowers. Our experiments demonstrate that the approach is technically feasible. The drone is able to detect, approach and touch the flowers totally autonomously. Our 10 cm sized prototype is trained on sunflowers, but the methodology presented in this paper can be retrained for any flower type.

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Srikanth, Kavirayani, and Nagesh Kumar GV. "Flower Pollination for Rotary Inverted Pendulum Stabilization with Delay." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 1 (2017): 245–53. https://doi.org/10.12928/TELKOMNIKA.v15i1.3403.

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Flower pollination is a single objective optimization technique which as a unconstrained optimization method is applied for the stabilization of the rotary inverted pendulum system. It was observed that the flower pollination method gave better sensitivity in control of the pendulum about its upright unstable equilibrium position with less time and definitely indicated that the method is an energy efficient method when compared with other methods like direct pole placement. This method yielded results under the influence of time delay and have proven that the influence of time delay is significantly felt and would cause loss of energy, however the presence of flower pollination for optimization minimizes the loss incurred due to time delay and makes the system significant in terms of sensitivity

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Franceschinelli, Edivani Villaron. "The pollination biology of two species of Helicteres (Malvaceae) with different mechanisms of pollen deposition." Flora 200 (June 5, 2005): 65–73. https://doi.org/10.5281/zenodo.13406709.

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(Uploaded by Plazi for the Bat Literature Project) Species of Helicteres are pollinated mainly by hummingbirds and bats. Most species pollinated by hummingbirds have a mechanism of depositing pollen on the top of the pollinator's head such as is shown in this work for Helicteres sacarolha. H. brevispira has an unusual mechanism of depositing pollen grains under the tail or on the abdomen of the hummingbirds. The top of the birds' head may be considered an efficient place to transport pollen grains, because it is plain, easily accessible for deposition and donation of pollen and not easily accessible for grooming, while the tail is movable and inclined. Thus, H. brevispira pollinators may carry or transfer fewer amounts of pollen grains than H. sacarolha pollinators from one flower to another. If a large amount of pollen grains is lost, a higher quantity of flower or pollen has to be produced to guarantee reproductive success. Plants of H. brevispira set higher number of flowers and pollen grains per flower than H. sacarolha and have also higher rates of fruit and flower abortion. Thus, pollination efficiency of H. brevispira may be reached by high pollen and flower production. Flower change mechanism presented in this species may be also involved with the optimization of pollinator feeding and pollination efficiency. r 2005 Elsevier GmbH. All rights reserved.

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Mehta, Ishita, Geetika Singh, Yogita Gigras, Anuradha Dhull, and Priyanka Rastogi. "Robotic Path Planning Using Flower Pollination Algorithm." Recent Advances in Computer Science and Communications 13, no. 2 (2020): 191–99. http://dx.doi.org/10.2174/2213275911666190320160837.

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Background: Robotic path planning is an important facet of robotics. Its purpose is to make robots move independently in their work environment from a source to a destination whilst satisfying certain constraints. Constraint conditions are as follows: avoiding collision with obstacles, staying as far as possible from the obstacles, traversing the shortest path, taking minimum time, consuming minimum energy and so on. Hence, the robotic path planning problem is a conditional constraint optimization problem. Methods: To overcome this problem, the Flower Pollination Algorithm, which is a metaheuristic approach is employed. The effectiveness of Flower Pollination Algorithm is showcased by using diverse maps. These maps are composed of several fixed obstacles in different positions, a source and a target position. Initially, the pollinators carrying pollen (candidate solutions) are at the source location. Subsequently, the pollinators must pave a way towards the target location while simultaneously averting any obstacles that are encountered enroute. The pollinators should also do so with the minimum cost possible in terms of distance. The performance of the algorithm in terms of CPU time is evaluated. Flower Pollination Algorithm was also compared to the Particle Swarm Optimization algorithm and Ant Colony Optimization algorithm. Results: It was observed that Flower Pollination Algorithm is faster than Particle Swarm Optimization and Ant Colony Optimization in terms of CPU time for the same number of iterations to find an optimized solution for robotic path planning. Conclusion: The Flower Pollination Algorithm can be effectively applied for solving robotic path planning problem with static obstacles.

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Afiahayati, Yap Bee Wah, Sri Hartati, et al. "Forecasting the Cumulative COVID-19 Cases in Indonesia Using Flower Pollination Algorithm." Computation 10, no. 12 (2022): 214. http://dx.doi.org/10.3390/computation10120214.

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Coronavirus disease 2019 (COVID-19) was declared as a global pandemic by the World Health Organization (WHO) on 12 March 2020. Indonesia is reported to have the highest number of cases in Southeast Asia. Accurate prediction of the number of COVID-19 cases in the upcoming few days is required as one of the considerations in making decisions to provide appropriate recommendations in the process of mitigating global pandemic infectious diseases. In this research, a metaheuristics optimization algorithm, the flower pollination algorithm, is used to forecast the cumulative confirmed COVID-19 cases in Indonesia. The flower pollination algorithm is a robust and adaptive method to perform optimization for curve fitting of COVID-19 cases. The performance of the flower pollination algorithm was evaluated and compared with a machine learning method which is popular for forecasting, the recurrent neural network. A comprehensive experiment was carried out to determine the optimal hyperparameters for the flower pollination algorithm and recurrent neural network. There were 24 and 72 combinations of hyperparameters for the flower pollination algorithm and recurrent neural network, respectively. The best hyperparameters were used to develop the COVID-19 forecasting model. Experimental results showed that the flower pollination algorithm performed better than the recurrent neural network in long-term (two weeks) and short-term (one week) forecasting of COVID-19 cases. The mean absolute percentage error (MAPE) for the flower pollination algorithm model (0.38%) was much lower than that of the recurrent neural network model (5.31%) in the last iteration for long-term forecasting. Meanwhile, the MAPE for the flower pollination algorithm model (0.74%) is also lower than the recurrent neural network model (4.8%) in the last iteration for short-term forecasting of the cumulative COVID-19 cases in Indonesia. This research provides state-of-the-art results to help the process of mitigating the global pandemic of COVID-19 in Indonesia.

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Abdel-Basset, Mohamed, Reda Mohamed, Safaa Saber, S. S. Askar, and Mohamed Abouhawwash. "Modified Flower Pollination Algorithm for Global Optimization." Mathematics 9, no. 14 (2021): 1661. http://dx.doi.org/10.3390/math9141661.

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In this paper, a modified flower pollination algorithm (MFPA) is proposed to improve the performance of the classical algorithm and to tackle the nonlinear equation systems widely used in engineering and science fields. In addition, the differential evolution (DE) is integrated with MFPA to strengthen its exploration operator in a new variant called HFPA. Those two algorithms were assessed using 23 well-known mathematical unimodal and multimodal test functions and 27 well-known nonlinear equation systems, and the obtained outcomes were extensively compared with those of eight well-known metaheuristic algorithms under various statistical analyses and the convergence curve. The experimental findings show that both MFPA and HFPA are competitive together and, compared to the others, they could be superior and competitive for most test cases.

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Al-Betar, Mohammed Azmi, Mohammed A. Awadallah, Iyad Abu Doush, Abdelaziz I. Hammouri, Majdi Mafarja, and Zaid Abdi Alkareem Alyasseri. "Island flower pollination algorithm for global optimization." Journal of Supercomputing 75, no. 8 (2019): 5280–323. http://dx.doi.org/10.1007/s11227-019-02776-y.

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Sallam, Tarek Abdel Rahman, Adel Bedair Abdel-Rahman, Masoud Alghoniemy, and Zen Kawasaki. "Flower Pollination Algorithm for Adaptive Beamforming of Phased Array Antennas." Journal of Machine Intelligence 2, no. 2 (2017): 1–5. http://dx.doi.org/10.21174/jomi.v2i2.71.

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This paper introduces the flower pollination algorithm (FPA) as an optimization technique suitable for adaptive beamforming of phased array antennas. The FPA is a new nature-inspired evolutionary computation algorithm that is based on pollinating behaviour of flowering plants. Unlike the other nature-inspired algorithms, the FPA has fewer tuning parameters to fit into different optimization problems. The FPA is used to compute the complex beamforming weights of the phased array antenna. In order to exhibit the robustness of the new technique, the FPA has been applied to a uniform linear array antenna with diﬀerent array sizes. The results reveal that the FPA leads to the optimum Wiener weights in each array size with less number of iterations compared with two other evolutionary optimization algorithms namely, particle swarm optimization and cuckoo search.

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Kamboh, M. Iqbal, Nazri Bin Mohd Nawi, Azizul Azhar Ramli, and Fanni Sukma. "An Improved Flower Pollination Algorithm for Global and Local Optimization." JOIV : International Journal on Informatics Visualization 5, no. 4 (2021): 461. http://dx.doi.org/10.30630/joiv.5.4.738.

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Meta-heuristic algorithms have emerged as a powerful optimization tool for handling non-smooth complex optimization problems and also to address engineering and medical issues. However, the traditional methods face difficulty in tackling the multimodal non-linear optimization problems within the vast search space. In this paper, the Flower Pollination Algorithm has been improved using Dynamic switch probability to enhance the balance between exploitation and exploration for increasing its search ability, and the swap operator is used to diversify the population, which will increase the exploitation in getting the optimum solution. The performance of the improved algorithm has investigated on benchmark mathematical functions, and the results have been compared with the Standard Flower pollination Algorithm (SFPA), Genetic Algorithm, Bat Algorithm, Simulated annealing, Firefly Algorithm and Modified flower pollination algorithm. The ranking of the algorithms proves that our proposed algorithm IFPDSO has outperformed the above-discussed nature-inspired heuristic algorithms.

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Yang, Wanjun, and Zengwu Sun. "GPS Position Prediction Method Based on Chaotic Map-Based Flower Pollination Algorithm." Complexity 2021 (April 24, 2021): 1–8. http://dx.doi.org/10.1155/2021/9972701.

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GPS position data prediction can effectively alleviate urban traffic, population flow, route planning, etc. It has very important research significance. Using swarm intelligence optimization algorithm to predict geographic location has important research strategies. Flower pollination algorithm (FPA) is a new swarm intelligence optimization algorithm (SIOA) and easy to implement and has other characteristics; more and more scholars have continuously improved it and applied it to more fields. Aiming at the fact that FPA leads to the local optimal value in cross-pollination, the chaotic mapping strategy is proposed to optimize related issues that the population is not rich enough in the self-pollination process. The improved flower pollination algorithm has better advantages in testing function convergence and geographic location prediction effect.

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Dr.K.Lenin. "ACTIVE POWER LOSS REDUCTION BY FLOWER POLLINATION ALGORITHM." International Journal of Research - Granthaalayah 5, no. 12 (2017): 223–31. https://doi.org/10.5281/zenodo.1134563.

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This paper presents Flower Pollination (FP) algorithm for solving the optimal reactive power problem. Minimization of real power loss is taken as key intent. Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. The biological evolution point of view, the objective of the flower pollination is the survival of the fittest and the optimal reproduction of plants in terms of numbers as well as the largely fittest. In order to evaluate the performance of the proposed Flower Pollination (FP) algorithm, it has been tested on IEEE 57 bus system and compared to other standard reported algorithms. Simulation results show that FP algorithm is better than other algorithms in reducing the real power loss and voltage profiles are within the limits.

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Virk, Amandeep K., and Kawaljeet Singh. "On Performance of Binary Flower Pollination Algorithm for Rectangular Packing Problem." Recent Advances in Computer Science and Communications 13, no. 1 (2020): 22–34. http://dx.doi.org/10.2174/2213275911666181114143239.

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Background: Metaheuristic algorithms are optimization algorithms capable of finding near-optimal solutions for real world problems. Rectangle Packing Problem is a widely used industrial problem in which a number of small rectangles are placed into a large rectangular sheet to maximize the total area usage of the rectangular sheet. Metaheuristics have been widely used to solve the Rectangle Packing Problem. Objective: A recent metaheuristic approach, Binary Flower Pollination Algorithm, has been used to solve for rectangle packing optimization problem and its performance has been assessed. Methods: A heuristic placement strategy has been used for rectangle placement. Then, the Binary Flower Pollination Algorithm searches the optimal placement order and optimal layout. Results: Benchmark datasets have been used for experimentation to test the efficacy of Binary Flower Pollination Algorithm on the basis of utilization factor and number of bins used. The simulation results obtained show that the Binary Flower Pollination Algorithm outperforms in comparison to the other well-known algorithms. Conclusion: BFPA gave superior results and outperformed the existing state-of-the-art algorithms in many instances. Thus, the potential of a new nature based metaheuristic technique has been discovered.

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Arora, Sankalap, and Priyanka Anand. "Chaos-enhanced flower pollination algorithms for global optimization." Journal of Intelligent & Fuzzy Systems 33, no. 6 (2017): 3853–69. http://dx.doi.org/10.3233/jifs-17708.

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Nabil, Emad. "A Modified Flower Pollination Algorithm for Global Optimization." Expert Systems with Applications 57 (September 2016): 192–203. http://dx.doi.org/10.1016/j.eswa.2016.03.047.

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Lei, Mengyi, Yongquan Zhou, and Qifang Luo. "Enhanced Metaheuristic Optimization: Wind-Driven Flower Pollination Algorithm." IEEE Access 7 (2019): 111439–65. http://dx.doi.org/10.1109/access.2019.2934733.

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Zhou, Yongquan, and Rui Wang. "An Improved Flower Pollination Algorithm for Optimal Unmanned Undersea Vehicle Path Planning Problem." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 04 (2016): 1659010. http://dx.doi.org/10.1142/s0218001416590102.

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Path planning of Unmanned Undersea Vehicle (UUV) is a rather complicated global optimum problem which is about seeking a superior sailing route considering the different kinds of constrains under complex combat field environment. Flower pollination algorithm (FPA) is a new optimization method motivated by flower pollination behavior. In this paper, a variant of FPA is proposed to solve the UUV path planning problem in two-dimensional (2D) and three-dimensional (3D) space. Optimization strategies of particle swarm optimization are applied to the local search process of IFPA to enhance its search ability. In the progress of iteration of this improved algorithm, a dimension by dimension based update and evaluation strategy on solutions is used. This new approach can accelerate the global convergence speed while preserving the strong robustness of standard FPA. The realization procedure for this improved flower pollination algorithm is also presented. To prove the performance of this proposed method, it is compared with nine population-based algorithms. The experiment result shows that the proposed approach is more effective and feasible in UUV path planning in 2D and 3D space.

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Jia, Yunjian, Shankun Wang, Liang Liang, Yaxing Wei, and Yanfei Wu. "A Flower Pollination Optimization Algorithm Based on Cosine Cross-Generation Differential Evolution." Sensors 23, no. 2 (2023): 606. http://dx.doi.org/10.3390/s23020606.

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The flower pollination algorithm (FPA) is a novel heuristic optimization algorithm inspired by the pollination behavior of flowers in nature. However, the global and local search processes of the FPA are sensitive to the search direction and parameters. To solve this issue, an improved flower pollination algorithm based on cosine cross-generation differential evolution (FPA-CCDE) is proposed. The algorithm uses cross-generation differential evolution to guide the local search process, so that the optimal solution is achieved and sets cosine inertia weights to increase the search convergence speed. At the same time, the external archiving mechanism and the adaptive adjustment of parameters realize the dynamic update of scaling factor and crossover probability to enhance the population richness as well as reduce the number of local solutions. Then, it combines the cross-generation roulette wheel selection mechanism to reduce the probability of falling into the local optimal solution. In comparing to the FPA-CCDE with five state-of-the-art optimization algorithms in benchmark functions, we can observe the superiority of the FPA-CCDE in terms of stability and optimization features. Additionally, we further apply the FPA-CCDE to solve the robot path planning issue. The simulation results demonstrate that the proposed algorithm has low cost, high efficiency, and attack resistance in path planning, and it can be applied to a variety of intelligent scenarios.

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Ali, E. S., and S. M. Abd Elazim. "Optimal STATCOM Design via Flower Pollination Approach for A Multimachine Power System." WSEAS TRANSACTIONS ON POWER SYSTEMS 18 (November 28, 2023): 282–92. http://dx.doi.org/10.37394/232016.2023.18.29.

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A new metaheuristic method, the Flower Pollination Approach (FPA), based on the pollination process of flowers is proposed in this article for the optimal design of a static synchronous compensator (STATCOM) in a multimachine environment. The STATCOM parameter tuning process is converted to an optimization problem which is solved by FPA. The performance of the proposed FPA-based STATCOM (FPASTATCOM) is compared with Genetic Algorithm (GA) based STATCOM (GASTATCOM) under various operating conditions and disturbances. The superiority of the proposed technique in damping oscillations is confirmed via eigenvalues and time domain simulation results over the GA.

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Trisna, I. Nyoman Prayana, Afiahayati Afiahayati, and Muhammad Auzan. "Flower Pollination Inspired Algorithm on Exchange Rates Prediction Case." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 17, no. 3 (2023): 249. http://dx.doi.org/10.22146/ijccs.84223.

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Flower pollination algorithm is a bio-inspired system that adapts a similar process to genetic algorithm, that aims for optimization problems. In this research, we examine the utilization of the flower pollination algorithm in linear regression for currency exchange cases. The solutions are represented as a set that contains regression coefficients. Population size for the candidate solutions and the switch probability between global pollination and local pollination have been experimented with in this research. Our result shows that the final solution is better when a higher size population and higher switch probability are employed. Furthermore, our result shows the higher size of the population leads to considerable running time, where the leaning probability of global pollination slightly increases the running time.

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Jing-Sen Liu, Jing-Sen Liu, Qing-Qing Liu Jing-Sen Liu, and Fang Zuo Qing-Qing Liu. "A Guided Mutation Grey Wolf Optimizer Algorithm Integrating Flower Pollination Mechanism." 電腦學刊 33, no. 2 (2022): 051–67. http://dx.doi.org/10.53106/199115992022043302005.

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<p>The basic Grey Wolf Optimizer (GWO) has some shortcomings, for example, the convergence speed is slow, it is easy to fall into local extremum, and high-dimensional optimization ability is poor and so on. In response to these shortcomings, an improved grey wolf algorithm which combines flower pollination mechanism, teaching mechanism and polynomial variation is proposed in this study. The flower pollination mechanism is integrated with GWO algorithm, Levy distribution is introduced into the global search of grey wolf population. And the double random mechanism is added in the local search, for these improvements, this algorithm&rsquo;s overall optimization performance is improved. The teaching mechanism is added to wolf to improve the algorithm&rsquo;s convergence speed. Polynomial mutation is applied to the individuals with poor optimization effect to improve the algorithm&rsquo;s accuracy and its ability to jump out of local extremum. Theoretical analysis shows that the time complexity of the improved algorithm is the same as that of the basic algorithm. The test results of five representative comparison algorithms on multiple different characteristics and different dimensions of CEC2017 benchmark functions and two classical engineering problems show that FMGWO algorithm has high optimization accuracy, convergence speed and solution stability. Therefore, it has obvious advantages in global optimization.</p> <p>&nbsp;</p>

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Rice, Chapel Reid, Spencer Thomas McDonald, Yang Shi, et al. "Perception, Path Planning, and Flight Control for a Drone-Enabled Autonomous Pollination System." Robotics 11, no. 6 (2022): 144. http://dx.doi.org/10.3390/robotics11060144.

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The decline of natural pollinators necessitates the development of novel pollination technologies. In this work, we propose a drone-enabled autonomous pollination system (APS) that consists of five primary modules: environment sensing, flower perception, path planning, flight control, and pollination mechanisms. These modules are highly dependent upon each other, with each module relying on inputs from the other modules. In this paper, we focus on approaches to the flower perception, path planning, and flight control modules. First, we briefly introduce a flower perception method from our previous work to create a map of flower locations. With a map of flowers, APS path planning is defined as a variant of the Travelling Salesman Problem (TSP). Two path planning approaches are compared based on mixed-integer programming (MIP) and genetic algorithms (GA), respectively. The GA approach is chosen as the superior approach due to the vast computational savings with negligible loss of optimality. To accurately follow the generated path for pollination, we develop a convex optimization approach to the quadrotor flight control problem (QFCP). This approach solves two convex problems. The first problem is a convexified three degree-of-freedom QFCP. The solution to this problem is used as an initial guess to the second convex problem, which is a linearized six degree-of-freedom QFCP. It is found that changing the objective of the second convex problem to minimize the deviation from the initial guess provides improved physical feasibility and solutions similar to a general-purpose optimizer. The path planning and flight control approaches are then tested within a model predictive control (MPC) framework where significant computational savings and embedded adjustments to uncertainty are observed. Coupling the two modules together provides a simple demonstration of how the entire APS will operate in practice.

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Hidayat, Taufik, Wuryatmo Ahmad Sidik, and Jajang Jajang. "OPTIMISASI FUNGSI RASTRIGIN MENGGUNAKAN FLOWER POLLINATION ALGORITHM." Jurnal Ilmiah Matematika dan Pendidikan Matematika 14, no. 1 (2022): 81. http://dx.doi.org/10.20884/1.jmp.2022.14.1.5819.

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The Rastrigin function is a multimodal function. It is difficult to find a global minimum of the function because it has many local minimums. So, we need an effective and efficient algorithm to find a solution to the global minimum of the function without being trapped by the local minimum. The flower pollination algorithm is a metaheuristic algorithm, it is expected to be capable of solving multimodal function optimization problems. In this study flower pollination algorithm is used to find the global minimum of the Rastrigin function of two variables with MATLAB. The Rastrigin function of two variables is used as objective function for the flower pollination algorithm. The parameters are divided into three configurations based on the difference amount of pollen gamets, the probability switch, and the search domain, with two different iterations 300 and 1500. In order, to get the best results each configuration is running for 10 times. The best results from the flower pollination algorithm are obtained from the first configuration and 1500 iterations

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Ali, E. S. "Power System Stabilizers Layout via Flower Pollination Algorithm." International Journal of Electrical Engineering and Computer Science 4 (December 31, 2022): 80–87. http://dx.doi.org/10.37394/232027.2022.4.12.

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In this article, the optimum layout of Power System Stabilizers (PSSs) using Flower Pollination Algorithm (FPA) is developed in a multimachine environment. The PSSs values tuning problem is turned into an optimization task which is treated by FPA. FPA is used to check for optimum controller parameters by reducing an eigenvalues based objective function involving the damping factor, and the damping ratio of the lightly damped modes. The implementation of the developed FPA based PSSs (FPAPSS) is compared with Particle Swarm Optimization (PSO) based PSSs (PSOPSS) and the Conventional PSSs (CPSS) for various loading conditions and disturbances. The results of the developed FPAPSS are confirmed via time domain analysis, eigenvalues and some indices. Also, the results are introduced to prove the effectiveness of the developed algorithm over the PSO and conventional one.

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Qiu, Guihua. "The multilevel coordinated optimal control method of a photovoltaic distribution network based on an improved FPA algorithm." Journal of Physics: Conference Series 2592, no. 1 (2023): 012098. http://dx.doi.org/10.1088/1742-6596/2592/1/012098.

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Abstract The control method of the photovoltaic distribution network has problems of low control accuracy and high line loss of the photovoltaic distribution network. Therefore, this paper proposes a multilevel coordinated optimization control method of a photovoltaic distribution network based on an improved FPA algorithm. First, we analyze the FCM algorithm, use the Euclidean distance to calculate the number of clusters, analyze the basic principle of the flower pollination algorithm, build the coordinated optimization control model of the photovoltaic distribution network, calculate the power flow result of the photovoltaic distribution network by improving the FPA algorithm, determine the global pollination behavior according to the flower pollination algorithm, and use the greedy strategy to obtain the optimal candidate solution. At this time, the obtained solution is the multi-level coordinated optimization control result of the photovoltaic distribution network. The experimental results show that this method can effectively improve the multi-level coordination and optimization control effect of the photovoltaic distribution network and reduce the line loss of the photovoltaic distribution network.

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Rao, D. S. Naga Malleswara, Dogga Raveendhra, Devineni Gireesh Kumar, Bharat Kumar Narukullapati, Davu Srinivasa Rao, and Srividya Devi Palakaluri. "Comparison Investigation into Power System Optimization and Constraint-Based Generator Load Scheduling Using Metaheuristic Algorithms." ECTI Transactions on Electrical Engineering, Electronics, and Communications 19, no. 2 (2021): 200–208. http://dx.doi.org/10.37936/ecti-eec.2021192.222310.

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In this paper, a novel flower pollination algorithm (FPA) is implemented to solve the problem of combined economic emission dispatch (CEED) in the power system. The FPA is a new metaheuristic optimization technique, which takes a biological approach to flower pollination. The FPA mimics the characteristics of flower pollination according to the survival of the fittest concept. CEED represents a combination of the emission and economic dispatch functions, formulated into a single function using the penalty factor. In this paper, the effect of valve point loading in the power system network is considered to obtain minimum fuel cost, minimum emissions, and optimum power generation. The performance of the proposed algorithm is evaluated using two test systems, namely 10 and 14 generating units by contemplating the valve point loading effect as well as transmission loss. The results of the 10 and 14 system units are compared with a learning-based optimization technique to demonstrate the effectiveness of the FPA. The findings reveal that the proposed FPA gives better performance than other algorithms with minimum fuel cost and emissions.

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Et. al., V. V. D. Sahithi,. "FLOWER POLLINATION ALGORITHM FOR MULTI LEVEL LOT SIZING OPTIMIZATION." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (2021): 274–80. http://dx.doi.org/10.17762/itii.v9i2.344.

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In this competitive and constantly changing world, meeting the customer requirements within less time by providing less cost is extremely tricky task. This is only possible by optimizing all the different parameters in its life cycle. Here Optimizing the inventory plays a major role.Maintaining the exact amount of inventory, at proper place, in appropriate level is a challenging task for production managers. When we work on Multi level environments this problem becomes even more complex.So, to optimize this kind of problems we applied binary form of Flower Pollination algorithm to solve this complex problem. we solved different inventory lot sizing problems with this FP algorithm and compared the results with genetic algorithm and other algorithms. In all the scenarios our simulation results shown that FP algorithm is better than other algorithms

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C. S. P. Rao, V. V. D. Sahithi, M. Srinivasa Rao,. "FLOWER POLLINATION ALGORITHM FOR MULTI LEVEL LOT SIZING OPTIMIZATION." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (2021): 127–33. http://dx.doi.org/10.17762/itii.v9i1.110.

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Yang, Xin-She, Mehmet Karamanoglu, and Xingshi He. "Flower pollination algorithm: A novel approach for multiobjective optimization." Engineering Optimization 46, no. 9 (2013): 1222–37. http://dx.doi.org/10.1080/0305215x.2013.832237.

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Bekdaş, Gebrail, Sinan Melih Nigdeli, and Xin-She Yang. "Sizing optimization of truss structures using flower pollination algorithm." Applied Soft Computing 37 (December 2015): 322–31. http://dx.doi.org/10.1016/j.asoc.2015.08.037.

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Abdel-Baset, Mohamed, and Ibrahim Hezam. "A Hybrid Flower Pollination Algorithm for Engineering Optimization Problems." International Journal of Computer Applications 140, no. 12 (2016): 10–23. http://dx.doi.org/10.5120/ijca2016909119.

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Fouad, Allouani, and Xiao-Zhi Gao. "A novel modified flower pollination algorithm for global optimization." Neural Computing and Applications 31, no. 8 (2018): 3875–908. http://dx.doi.org/10.1007/s00521-017-3313-0.

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Chen, Yang, and Dechang Pi. "An innovative flower pollination algorithm for continuous optimization problem." Applied Mathematical Modelling 83 (July 2020): 237–65. http://dx.doi.org/10.1016/j.apm.2020.02.023.

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Cui, Weijia, and Yuzhu He. "Biological Flower Pollination Algorithm with Orthogonal Learning Strategy and Catfish Effect Mechanism for Global Optimization Problems." Mathematical Problems in Engineering 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/6906295.

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The flower pollination algorithm (FPA) is a novel optimization technique derived from the pollination behavior of flowers. However, the shortcomings of the FPA, such as a tendency towards premature convergence and poor exploitation ability, confine its application in engineering problems. To further strengthen FPA optimization performance, an orthogonal learning (OL) strategy based on orthogonal experiment design (OED) is embedded into the local pollination operator. OED can predict the optimal factor level combination by constructing a smaller but representative test set based on an orthogonal array. Using this characteristic of OED, the OL strategy can extract a promising solution from various sources of experience information, which leads the population to a potentially reasonable search direction. Moreover, the catfish effect mechanism is introduced to focus on the worst individuals during the iteration process. This mechanism explores new valuable information and maintains superior population diversity. The experimental results on benchmark functions show that our proposed algorithm significantly enhances the performance of the basic FPA and offers stronger competitiveness than several state-of-the-art algorithms.

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Sudha, T. "Multi-Objective Optimization Based Multi-Objective Controller Tuning Method with Robust Stabilization of Fractional Calculus CSTR." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 16 (July 8, 2021): 375–82. http://dx.doi.org/10.37394/23203.2021.16.32.

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In Continuous Stirred Tank Reactor (CSTR) have Fractional order PID with the nominal order PID controller has been used to Multi-Criteria Decision Making (MCDM) and EMO (Evolutionary Multi-objective Optimization) by adjustment of control parameters like Hybrid methods in Multi objective optimization. But, this Fractional order PID with the nominal PID controller has maximum performance estimation. Proposed research work focused the Flower Pollination Algorithm based on Multi objective optimization with Genetic evaluation and Fractional order PID with the nominal PID controller is provides CSTR results. When a flower is displayed to maximum variations in this practical state, the Genetic evaluation has been used to identify the variations. The FPID (Flower Pollination Integral Derivative) is used for tuning the parameters of a Fractional order PID with the nominal PID controller for each region to improve the multi-criteria decision making. FPID also denoted as Flower Optimization Integral Derivative (FOID). The Genetic evaluation scheduler has been combined with multiple local linear Fractional order PID with the nominal PID controller to check the stability of loop for entire regions with various levels of temperatures. MATLAB results demonstrate that the feasibility of using the proposed Fractional order PID with the nominal PID controller compared than the existing PID controller, and it shows the FOID attained better results.

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Li, Shilong, Hua Zhang, Lijie Ding, Yufan Chen, and Ao Li. "Research of Distributed Photovoltaic Output Fluctuation Suppression Method Based on Improved FPA." Journal of Physics: Conference Series 2418, no. 1 (2023): 012005. http://dx.doi.org/10.1088/1742-6596/2418/1/012005.

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Abstract Due to weather changes, the distributed photovoltaic (PV) output power has fluctuations, seriously affecting the power quality of the distribution network. This paper proposes a distributed PV power output fluctuation suppression method based on the improved Flower Pollination Algorithm (FPA) to solve the above problem. This method uses an annihilation perturbation mechanism and linear weights to update the pollen position, which mainly affects the output power of photovoltaic power plants. The time constant of the first-order low-pass filter is optimized, and the improved flower pollination algorithm is utilized for optimization to obtain the optimal time constant of the first-order low-pass filter, suppressing the fluctuation of distributed photovoltaic output and integrating the photovoltaic power station’s output. The simulations indicate that the improved flower pollination algorithm is better than other evaluated algorithms and has an improvement of about 11% over the particle swarm algorithm.

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Najafi Mohsenabad, Hadi, and Mehmet Ali Tut. "Optimizing Cybersecurity Attack Detection in Computer Networks: A Comparative Analysis of Bio-Inspired Optimization Algorithms Using the CSE-CIC-IDS 2018 Dataset." Applied Sciences 14, no. 3 (2024): 1044. http://dx.doi.org/10.3390/app14031044.

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In computer network security, the escalating use of computer networks and the corresponding increase in cyberattacks have propelled Intrusion Detection Systems (IDSs) to the forefront of research in computer science. IDSs are a crucial security technology that diligently monitor network traffic and host activities to identify unauthorized or malicious behavior. This study develops highly accurate models for detecting a diverse range of cyberattacks using the fewest possible features, achieved via a meticulous selection of features. We chose 5, 9, and 10 features, respectively, using the Artificial Bee Colony (ABC), Flower Pollination Algorithm (FPA), and Ant Colony Optimization (ACO) feature-selection techniques. We successfully constructed different models with a remarkable detection accuracy of over 98.8% (approximately 99.0%) with Ant Colony Optimization (ACO), an accuracy of 98.7% with the Flower Pollination Algorithm (FPA), and an accuracy of 98.6% with the Artificial Bee Colony (ABC). Another achievement of this study is the minimum model building time achieved in intrusion detection, which was equal to 1 s using the Flower Pollination Algorithm (FPA), 2 s using the Artificial Bee Colony (ABC), and 3 s using Ant Colony Optimization (ACO). Our research leverages the comprehensive and up-to-date CSE-CIC-IDS2018 dataset and uses the preprocessing Discretize technique to discretize data. Furthermore, our research provides valuable recommendations to network administrators, aiding them in selecting appropriate machine learning algorithms tailored to specific requirements.

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Dao, Thi-Kien, Trong-The Nguyen, Vinh-Tiep Nguyen, and Trinh-Dong Nguyen. "A Hybridized Flower Pollination Algorithm and Its Application on Microgrid Operations Planning." Applied Sciences 12, no. 13 (2022): 6487. http://dx.doi.org/10.3390/app12136487.

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The meta-heuristic algorithms have been applied to handle various real-world optimization problems because their approach closely resembles natural human thinking and processing relatively quickly. Flowers pollination algorithm (FPA) is one of the advanced meta-heuristic algorithms; still, it has suffered from slow convergence and insufficient accuracy when dealing with complicated problems. This study suggests hybridizing the FPA with the sine–cosine algorithm (call HSFPA) to avoid FPA drawbacks for microgrid operations planning and global optimization problems. The objective function of microgrid operations planning is constructed based on the power generation distribution system’s relevant economic costs and environmental profits. Adapting hop size, diversifying local search, and diverging agents as strategies from learning SCA are used to modify the original FPA equation for improving the HSFPA solutions in terms of diversity pollinations, increasing convergence, and preventing local optimal traps. The experimental results of the HSFPA compared with the other algorithms in the literature for the benchmark test function and microgrid operations planning problem to evaluate the proposed scheme. Compared results show that the HSFPA offers outstanding performance compared to other competitors for the testing function. The HSFPA also delivers efficient optimal performance in microgrid optimization for solving the operations planning problem.

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Mishra, Atul, and Sankha Deb. "An improved hybrid flower pollination algorithm for assembly sequence optimization." Assembly Automation 39, no. 1 (2019): 165–85. http://dx.doi.org/10.1108/aa-09-2017-112.

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PurposeAssembly sequence optimization is a difficult combinatorial optimization problem having to simultaneously satisfy various feasibility constraints and optimization criteria. Applications of evolutionary algorithms have shown a lot of promise in terms of lower computational cost and time. But there remain challenges like achieving global optimum in least number of iterations with fast convergence speed, robustness/consistency in finding global optimum, etc. With the above challenges in mind, this study aims to propose an improved flower pollination algorithm (FPA) and hybrid genetic algorithm (GA)-FPA.Design/methodology/approachIn view of slower convergence rate and more computational time required by the previous discrete FPA, this paper presents an improved hybrid FPA with different representation scheme, initial population generation strategy and modifications in local and global pollination rules. Different optimization objectives are considered like direction changes, tool changes, assembly stability, base component location and feasibility. The parameter settings of hybrid GA-FPA are also discussed.FindingsThe results, when compared with previous discrete FPA and GA, memetic algorithm (MA), harmony search and improved FPA (IFPA), the proposed hybrid GA-FPA gives promising results with respect to higher global best fitness and higher average fitness, faster convergence (especially from the previously developed variant of FPA) and most importantly improved robustness/consistency in generating global optimum solutions.Practical implicationsIt is anticipated that using the proposed approach, assembly sequence planning can be accomplished efficiently and consistently with reduced lead time for process planning, making it cost-effective for industrial applications.Originality/valueDifferent representation schemes, initial population generation strategy and modifications in local and global pollination rules are introduced in the IFPA. Moreover, hybridization with GA is proposed to improve convergence speed and robustness/consistency in finding globally optimal solutions.

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Iqbal, Zafar, Nadeem Javaid, Syed Mohsin, Syed Akber, Muhammad Afzal, and Farruh Ishmanov. "Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling." Energies 11, no. 10 (2018): 2861. http://dx.doi.org/10.3390/en11102861.

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With the emergence of the smart grid, both consumers and electricity providing companies can benefit from real-time interaction and pricing methods. In this work, a smart power system is considered, where consumers share a common energy source. Each consumer is equipped with a home energy management controller (HEMC) as scheduler and a smart meter. The HEMC keeps updating the utility with the load profile of the home. The smart meter is connected to a power grid having an advanced metering infrastructure which is responsible for two-way communication. Genetic teaching-learning based optimization, flower pollination teaching learning based optimization, flower pollination BAT and flower pollination genetic algorithm based energy consumption scheduling algorithms are proposed. These algorithms schedule the loads in order to shave the peak formation without compromising user comfort. The proposed algorithms achieve optimal energy consumption profile for the home appliances equipped with sensors to maximize the consumer benefits in a fair and efficient manner by exchanging control messages. Control messages contain energy consumption of consumer and real-time pricing information. Simulation results show that proposed algorithms reduce the peak-to-average ratio by 34.56% and help the users to reduce their energy expenses by 42.41% without compromising the comfort. The daily discomfort is reduced by 28.18%.

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Cheng, Qian, Huajuan Huang, and Minbo Chen. "A Novel Crow Search Algorithm Based on Improved Flower Pollination." Mathematical Problems in Engineering 2021 (October 26, 2021): 1–26. http://dx.doi.org/10.1155/2021/1048879.

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Crow search algorithm (CSA) is a new type of swarm intelligence optimization algorithm proposed by simulating the crows’ intelligent behavior of hiding and retrieving food. The algorithm has the characteristics of simple structure, few control parameters, and easy implementation. Like most optimization algorithms, the crow search algorithm also has the disadvantage of slow convergence and easy fall into local optimum. Therefore, a crow search algorithm based on improved flower pollination algorithm (IFCSA) is proposed to solve these problems. First, the search ability of the algorithm is balanced by the reasonable change of awareness probability, and then the convergence speed of the algorithm is improved. Second, when the leader finds himself followed, the cross-pollination strategy with Cauchy mutation is introduced to avoid the blindness of individual location update, thus improving the accuracy of the algorithm. Experiments on twenty benchmark problems and speed reducer design were conducted to compare the performance of IFCSA with that of other algorithms. The results show that IFCSA has better performance in function optimization and speed reducer design problem.

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Mishra, Atul, and Sankha Deb. "Assembly sequence optimization using a flower pollination algorithm-based approach." Journal of Intelligent Manufacturing 30, no. 2 (2016): 461–82. http://dx.doi.org/10.1007/s10845-016-1261-7.

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Mutturi, Sarma. "Dynamic optimization of fed-batch bioprocesses using flower pollination algorithm." Bioprocess and Biosystems Engineering 41, no. 11 (2018): 1679–96. http://dx.doi.org/10.1007/s00449-018-1992-2.

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Wang, Rui, and Yongquan Zhou. "Flower Pollination Algorithm with Dimension by Dimension Improvement." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/481791.

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Flower pollination algorithm (FPA) is a new nature-inspired intelligent algorithm which uses the whole update and evaluation strategy on solutions. For solving multidimension function optimization problems, this strategy may deteriorate the convergence speed and the quality of solution of algorithm due to interference phenomena among dimensions. To overcome this shortage, in this paper a dimension by dimension improvement based flower pollination algorithm is proposed. In the progress of iteration of improved algorithm, a dimension by dimension based update and evaluation strategy on solutions is used. And, in order to enhance the local searching ability, local neighborhood search strategy is also applied in this improved algorithm. The simulation experiments show that the proposed strategies can improve the convergence speed and the quality of solutions effectively.

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Singh, O. V., and M. Singh. "A Comparative Analysis on Economic Load Dispatch Problem Using Soft Computing Techniques." International Journal of Software Science and Computational Intelligence 12, no. 2 (2020): 50–73. http://dx.doi.org/10.4018/ijssci.2020040104.

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This article aims at solving economic load dispatch (ELD) problem using two algorithms. Here in this article, an implementation of Flower Pollination (FP) and the BAT Algorithm (BA) based optimization search algorithm method is applied. More than one objective is hoped to be achieve in this article. The combined economic emission dispatch (CEED) problem which considers environmental impacts as well as the cost is also solved using the two algorithms. Practical problems in economic dispatch (ED) include both nonsmooth cost functions having equality and inequality constraints which make it difficult to find the global optimal solution using any mathematical optimization. In this article, the ELD problem is expressed as a nonlinear constrained optimization problem which includes equality and inequality constraints. The attainability of the discussed methods is shown for four different systems with emission and without emission and the results achieved with FP and BAT algorithms are matched with other optimization techniques. The experimental results show that conferred Flower Pollination Algorithm (FPA) outlasts other techniques in finding better solutions proficiently in ELD problems.

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