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Fractional-order calculus-based flower pollination algorithm with local search for global optimization and image segmentation

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KNOWLEDGE-BASED SYSTEMS 2020年 197卷 105889-105889页

作者： Yousri, Dalia Abd Elaziz, Mohamed Mirjalili, Seyedali Fayoum Univ Fac Engn Dept Elect Engn Al Fayyum Egypt Zagazig Univ Fac Sci Dept Math Zagazig Egypt Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia

Introducing a novel approach to enhance the diversification and intensification propensities of the flower pollination algorithm (FPA) is the main aim of this paper. Therefore, the fractional-order (FO) calculus features are adopted to enhance the basic FPA local search ability and adaptive modify the harmonization coefficient among the FPA exploration and exploitation cores. The proposed Fractional-order FPA (FO-FPA) is examined in a number of experiments. Firstly, FO-FPA is tested with thirty-six benchmark functions with several dimensions. The proposed FO-FPA is compared with recent proved techniques based on several statistical measures and non parametric tests. Secondly, FO-FPA is implemented for a real application of the image segmentation and its results compared with state-of-the-art multi-level thresholding algorithms. The comparisons divulge the remarkable influence of merging FO with the basic FPA in improving the quality of the solutions and the acceleration of the convergence speed. (C) 2020 Elsevier B.V. All rights reserved.

关键词： flower pollination algorithm Local search Meta-heuristic Image segmentation Fractional calculus Optimization algorithm Benchmark

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Improved Q-Learning algorithm Based on flower pollination algorithm and Tabulation Method for Unmanned Aerial Vehicle Path Planning

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IEEE ACCESS 2024年 12卷 104429-104444页

作者： Bo, Lan Zhang, Tiezhu Zhang, Hongxin Yang, Jian Zhang, Zhen Zhang, Caihong Liu, Mingjie Qingdao Univ Coll Mech & Elect Engn Qingdao 266071 Peoples R China Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China

Planning a path is crucial for safe and efficient Unmanned aerial vehicle flights, especially in complex environments. While the Q-learning algorithm in reinforcement learning performs better in handling such environments, it suffers from slow convergence speed and limited real-time capability. To address these problems, this study proposes an enhanced initialization process using the flower pollination algorithm and employs a tabulation method to improve local obstacle avoidance ability. An improved Q-learning algorithm based on the flower pollination algorithm and tabulation method (IQ-FAT) is proposed, which can perform both global and local path planning, enhance the convergence time of Q-learning, and expedite obstacle avoidance. Evaluation results on various obstacle maps demonstrate that the modified algorithm has a significant improvement convergence speed of approximately 40% compared to the original algorithm while enabling global path planning and local obstacle avoidance. Furthermore, the algorithm demonstrates superior path-planning capabilities in complex environments and enhances the dynamic response time of UAVs by approximately 90% compared to the artificial potential field method.

关键词： Path planning Q-learning flowering plants Collision avoidance Heuristic algorithms Autonomous aerial vehicles Convergence unmanned aerial vehicle obstacle avoidance reinforcement learning flower pollination algorithm

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Solving 0-1 knapsack problem by binary flower pollination algorithm

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NEURAL COMPUTING & APPLICATIONS 2019年第9期31卷 5477-5495页

作者： Abdel-Basset, Mohamed El-Shahat, Doaa El-Henawy, Ibrahim Zagazig Univ Fac Comp & Informat Dept Operat Res Zagazig Egypt Zagazig Univ Fac Comp & Informat Comp Sci Dept El Zera Sq Zagazig Sharqiyah Egypt

In this paper, we propose a new binary version of the flower pollination algorithm (BFPA) for solving 0-1 knapsack problem. The standard flower pollination algorithm (FPA) is used for the continuous optimization problems. So, a transformation function is used to convert the continuous values generated from FPA into binary ones. A penalty function is added to the evaluation function to give negative values for the infeasible solutions. The infeasible solutions are treated by using a two-stage repair operator called flower repair. Experimental results have proved the superiority of BFPA over other algorithms.

关键词： flower pollination algorithm Optimization Penalty function 0-1 knapsack problem

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On the efficiency of the binary flower pollination algorithm: Application on the antenna positioning problem

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APPLIED SOFT COMPUTING 2016年第0期47卷 395-414页

作者： Dahi, Zakaria Abd El Moiz Mezioud, Chaker Draa, Amer Constantine 2 Univ Abdelhamid Mehri MISC Lab Constantine Algeria

The flower pollination algorithm (FPA) is a recently proposed continuous metaheuristic that was claimed to give promising results. However, its potential in binary problems has been vaguely investigated. The use of mapping techniques to adapt metaheuristics to handle binary optimisation problems is a widely used approach, but these techniques are still fuzzy and misunderstood, since no work thoroughly studied them for a given problem or algorithm. This paper conducts a consistent and systematic study to assess the efficiency of the FPA and the common mapping techniques. This is done through proposing four Binary variants of the FPA (BFPA) that have been got by applying the principal mapping techniques existing in the literature. As benchmark problem;an NP-hard binary one in advanced cellular networks, the Antenna Positioning Problem (APP), is used. In order to assess the scalability, efficiency and robustness of the proposed BFPAs, the experiments have been carried out on realistic, synthetic and random data with different dimensions, and several statistical tests have been carried. Two of the top-ranked algorithms designed to solve the APP;the Population-Based Incremental Learning (PBIL) and the Differential Evolution algorithm (DE), are taken as a comparison basis. The results showed that the normalisation and angle modulation are the best mapping techniques. The experiments also showed that the BFPAs have some shortcomings but, they could outperform the PBIL in 4 out of 13 instances and the DE in 6 out of 13 instances and no statistical difference was found in the remaining instances. Besides, the BFPAs outperformed or gave competitive technical results compared to the PBIL and DE in all problem instances. (C) 2016 Elsevier B.V. All rights reserved.

关键词： flower pollination algorithm Mapping techniques Antenna positioning problem Cellular networks

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Optimizing of phase plan, sequence and signal timing based on flower pollination algorithm for signalized intersections

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SOFT COMPUTING 2021年第6期25卷 4243-4259页

作者： Korkmaz, Ersin Akgungor, Ali Payidar Kirikkale Univ Engn Fac Dept Civil Engn TR-71451 Kirikkale Turkey

The purpose of this study is to develop a control system that optimizes the phase plan, sequence and signal timing using the flower pollination algorithm (FPA). At the same time, it is aimed to improve the fixed-time control system with the optimum cycle length search approach based on the differential evolution algorithm. The applicability and performances of these two control systems were examined in 15 different traffic situations according to 4 different intersection geometries. Fixed-time and optimized fuzzy logic traffic controller (FLC) developed by Dogan were used as the reference control systems in performance comparison. The optimum cycle length search system can achieve approximately 18% improvement over the fixed-time system, but showed lower performance than the FPA and FLC control systems. The FPA system has proven its applicability by achieving the best performance with about a 30% improvement compared to the fixed-time system and about 3% improvement compared to the FLC system. The FPA approach, which has a fast and effective performance, has been found to be an alternative method for intersection control, and it is foreseen that it can increase the intersection capacity and reduce the negative effects such as delay and fuel consumption.

关键词： Traffic signal controller flower pollination algorithm Fuzzy logic Differential evolution algorithm Phase and signal optimization

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Enhanced global flower pollination algorithm for parameter identification of chaotic and hyper-chaotic system

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NONLINEAR DYNAMICS 2019年第2期97卷 1343-1358页

作者： Chen, Yang Pi, DeChang Wang, Bi Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Jiangsu Peoples R China Southeast Univ Comp Sci & Engn Nanjing 211189 Jiangsu Peoples R China

The problem of system parameter identification is a fundamental problem in the field of nonlinear science, which can be described as a multidimensional optimization problem. In this paper, an enhanced global flower pollination algorithm (GFPA) is proposed for parameter identification of chaotic and hyper-chaotic systems. The motion trajectory of the flower pollination algorithm is analyzed for the first time, and the equation of the algorithm exploration phase is improved by the chaotic mapping method to ensure the convergence of the algorithm in the exploration phase. In addition, in order to improve the convergence speed of the algorithm, the update method of the exploitation phase is reset by using the best information to guide the searching. Through analysis, the proposed new algorithm can guarantee the convergence of the algorithm without increasing the time complexity. Finally, we identify and validate the system of the Lorenz, Rossler, Chen and the system of the Rossler hyper-chaotic, Chen hyper-chaotic. The experimental results show that GFPA has better identification effect.

关键词： flower pollination algorithm Chaotic system Hyper-chaotic system Parameter identification Optimization problem

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Vision-based path detection of an automated guided vehicle using flower pollination algorithm

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AIN SHAMS ENGINEERING JOURNAL 2021年第2期12卷 2263-2274页

作者： Ong, Pauline Tan, Winson Kar Shen Low, Ee Soong Univ Tun Hussein Onn Malaysia Fac Mech & Mfg Engn Batu Pahat 86400 Johor Malaysia

The automated guided vehicle (AGV) with sensor recognition method is having shortcomings such as high cost and noise. In this regard, the flower pollination algorithm (FPA) is applied in the path detection system of an AGV, in combination with computer vision. The path detection system starts with image acquisition using an onboard camera. The captured image is then preprocessed to obtain simple contrast of the path. Subsequently, the FPA is used to find a set of solution points fall inside the path zone. A regression model is formed as path guidance for AGV to travel accordingly. The performance of FPA is analyzed via simulation and real-world experiment using a robotic platform and tested on commonly seen line patterns in the industrial environment. The effectiveness of FPA in path detection is also compared with particle swarm optimization. The obtained results demonstrate the promising feasibility of the proposed FPA-based path detection system. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.

关键词： Automated guided vehicle flower pollination algorithm Line detection Line follower Machine vision

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Chaotic flower pollination algorithm based optimal PID controller design for a buck converter

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ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2021年第2期107卷 281-298页

作者： Cimen, Murat Erhan Garip, Zeynep B. Boz, Ali Fuat Sakarya Univ Appl Sci Elect & Elect Engn Dept TR-54050 Serdivan Sakarya Turkey Sakarya Univ Appl Sci Comp Engn Dept TR-54050 Serdivan Sakarya Turkey

This paper presents a solution based on optimal PID coefficients including anti-wind up for buck converter presents using meta-heuristic algorithm and chaos theory. A hybrid algorithm is called chaotic based flower pollination algorithm is provided by combining flower pollination algorithm and chaos theory with different maps. Five different choatic maps are used in the aim of increasing the efficacy and efficiency of flower pollination algorithm. In order to adjust the parameters combined in the flower pollination algorithm, random number sequences from Henon, Logistic, Sine, Tent and Tinkerbell chaotic maps are used. The success of the developed algorithm is evaluated by solving 23 different benchmark problems which are very common in the literature. The results of comparative experiments show that henon chaotic map based flower pollination algorithm is sufficiently effective in solving these benchmark problems. Thus desinging optimal PID with anti-windup for buck converter, henon chaotic map based flower pollination algorithm is used. Also henon chaotic map based flower pollination algorithm has better performance than firelfy algorithm and whale optimization algorithm existing in literature.

关键词： flower pollination algorithm Chaotic maps PID design Buck Converter

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Research on the searching performance of flower pollination algorithm with three random walks

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第1期35卷 333-341页

作者： Gao, Yang Jun Zhang, Feng Ming Guo, Qing Li, Chao Air Force Engn Univ Equipment Management & Safety Engn Coll Xian 710038 Shaanxi Peoples R China

flower pollination algorithm is a new type of heuristic algorithm, which uses Levy random walk as the key element for high efficiency of global searching. In order to explore the search performance of pollination algorithms under different random walk models, three random walk models are taken into account, including levy random walk model used by original flower pollination algorithm and two new random walk models based on McCulloch algorithm introduced in this paper. The analysis of searching performance and adaptive of flower pollination algorithm with three random walks from two aspects of the model structure and the numerical simulation is given. The result shows that Cauchy random has a great competitive advantage for the low dimensional searching problem, and the Gauss random is more suitable for dealing with the multi-dimension unimodal case, while the Levy random is able to provide better performance of solving the multi-dimension multimodal case. Simulation results and analysis will have a significant impact on the design of the randomness mechanism of the meta-heuristics algorithm and the improvement of Classical algorithms.

关键词： flower pollination algorithm meta-heuristic algorithm random walk multi-dimension multimodal

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Multi-objective flower pollination algorithm: a new technique for EEG signal denoising

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NEURAL COMPUTING & APPLICATIONS 2023年第11期35卷 7943-7962页

作者： Alyasseri, Zaid Abdi Alkareem Khader, Ahamad Tajudin Al-Betar, Mohammed Azmi Yang, Xin-She Mohammed, Mazin Abed Abdulkareem, Karrar Hameed Kadry, Seifedine Razzak, Imran Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Bangi 43600 Selangor Malaysia Univ Kufa ECE Dept Fac Engn POB 21 Najaf Iraq Univ Sains Malaysia Sch Comp Sci George Town Malaysia Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol POB 50 Irbid Jordan Middlesex Univ Sch Sci & Technol London NW4 4BT England Univ Anbar Coll Comp Sci & Informat Technol Anbar 31001 Iraq Al Muthanna Univ Coll Agr Samawah 66001 Iraq Norrof Univ Coll Dept Appl Data Sci N-4608 Kristiansand Norway Deakin Univ Sch Informat Technol Geelong Vic Australia

The electroencephalogram (EEG) signal denoising problem has been considered a challenging task because of several artifact noises, such as eye blinking, eye movement, muscle activity, and power line interference, which can corrupt the original EEG signal during the recording time. Therefore, to remove these noises, the EEG signals must be processed to obtain efficient EEG features. Accordingly, several techniques have been proposed to reduce EEG noises, such as EEG signal denoising using wavelet transform (WT). The success of WT depends on the best configuration of its control parameters, which are often experimentally set. In this study, a multi-objective flower pollination algorithm (MOFPA) with WT (MOFPA-WT) is proposed to solve the EEG signal denoising problem. The novelty of this study is to find optimal EEG signal denoising parameters using MOFPA based on two measurement criteria for the denoised signals, namely minimum mean squared error (MSE) and maximum signal-to-noise ratio (SNR). The MOFPA-WT is tested using a standard EEG signal processing dataset, namely the EEG motor movement/imagery dataset. The performance of MOFPA-WT is evaluated using five criteria, namely SNR, SNR improvement, MSE, root mean squared error (RMSE), and percentage root mean square difference (PRD). Experiments are conducted using FPA with MSE, SNR, and MSE and SNR to show the effect of the multi-objective aspects on the performance of the proposed MOFPA-WT. Results show that FPA with MSE and SNR exhibits more subjective results than FPA with MSE and FPA with SNR. The convergence rate and Pareto front are also studied for the proposed MOFPA-WT.

关键词： Electroencephalogram Signal denoising Wavelet transform Multi-objective flower pollination algorithm

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