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dandelion algorithm With Probability-Based Mutation

IEEE ACCESS 2019年 7卷 97974-97985页

作者： Zhu, Honghao Liu, Guanjun Zhou, Mengchu Xie, Yu Kang, Qi Tongji Univ Key Lab Embedded Syst & Serv Comp Minist Educ Shanghai 201804 Peoples R China Tongji Univ Dept Comp Sci Shanghai 201804 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China

A dandelion algorithm (DA) is a recently-proposed intelligent optimization algorithm and shows an excellent performance in solving function optimization problems. However, like other intelligent algorithms, it converges slowly and falls into local optima easily. To overcome these two flaws, a dandelion algorithm with probability-based mutation (DAPM) is proposed in this paper. In DAPM, both Gaussian and Levy mutations can be used interchangeably according to a given probability model. In this paper, three probability models are discussed, namely linear, binomial, and exponential models. The experiments show that DAPM achieves better overall performance on standard test functions than DA.

关键词： dandelion algorithm Gaussian mutation Levy mutation probability-based mutation

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A probability-based core dandelion guided dandelion algorithm and application to traffic flow prediction

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 96卷

作者： Liu, Xiaojing Qin, Xiaolin Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Jiangsu Peoples R China

The dandelion algorithm (DA) is a recently proposed intelligent optimization algorithm inspired by dandelion sowing. For enhancing its exploitation ability and speeding up its convergence, this work proposes a probability-based core dandelion guided dandelion algorithm (GDA). Specifically, the probability of dandelions being selected is calculated firstly. Then the dandelions need to learn from the core dandelion based on previously calculated probability in the process of sowing. Meanwhile, a greedy selection strategy is applied to GDA. Experimental results show that the proposed algorithm not only outperforms DA and its variants, but also outperforms eight state-of-the-art intelligent optimization algorithms on most functions and three real world problems. In addition, the proposed algorithm is applied to optimize kernel extreme learning machine (KELM) for traffic flow prediction, and the results show that the proposed model has a smaller prediction error than its peers.

关键词： dandelion algorithm Probability Global-optimal-guidance Greedy selection strategy Traffic flow prediction

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Community-Based dandelion algorithm-Enabled Feature Selection and Broad Learning System for Traffic Flow Prediction

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第3期25卷 2508-2521页

作者： Liu, Xiaojing Qin, Xiaolin Zhou, MengChu Sun, Hao Han, Shoufei Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Zhejiang Gongshang Univ Sch Informat & Elect Engn Hangzhou 310018 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

In an intelligent transportation system, accurate traffic flow prediction can provide significant help for travel planning. Even though some methods are proposed to do so, they focus on either algorithm or data level studies. This work focuses on both by proposing a Community-based dandelion algorithm-enabled Feature selection and Broad learning system (CFB). Specifically, a feature selection method is adopted to choose suitable features aiming to avoid redundant ones affecting prediction accuracy, and a neural network-based learning algorithm, namely a Broad Learning System (BLS), is used to predict traffic flow. In order to further boost its prediction performance, a Community-based dandelion algorithm (CDA) is proposed by considering an individual and its multiple offspring as a community and adopting a learning strategy for different communities. The proposed CDA is used to a) choose the suitable features as a feature selection method;and b) optimize the parameters and network structure of BLS. CDA's superiority over its competitive peers is first verified on CEC2013's benchmark functions, and then the proposed CFB is applied to handle the traffic flow prediction problems. The results indicate that it can improve the prediction accuracy by 5%-16% compared to the updated traffic flow prediction methods.

关键词： Prediction algorithms Feature extraction Learning systems Kernel Predictive models Extreme learning machines Training Traffic flow prediction broad learning system dandelion algorithm feature selection network structure

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Fusion with distance-aware selection strategy for dandelion algorithm

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KNOWLEDGE-BASED SYSTEMS 2020年 205卷

作者： Han, Shoufei Zhu, Kun Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Jiangsu Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing 211106 Jiangsu Peoples R China

The dandelion algorithm (DA) is a recently proposed swarm intelligent optimization algorithm which is inspired by the process of dandelion sowing. In DA, the selection strategy (SS) is of key importance which is responsible for choosing the dandelions to enter the next generation. However, current SS in DA only considers the function fitness value, which cannot maintain the diversity of the population and can easily fall into local optimal solutions prematurely. To address this problem, a distance-aware selection strategy (DSS) is proposed by jointly considering the function fitness value and the individual position distance. In addition, inspired by ensemble learning, a fusion based adaptive selection strategy (FSS) is proposed to further improve the performance of DA. Specifically, a fusion pool including several famous SSs and our proposed DSS is established. And in each iteration, the best SS in terms of overall reward is selected. Experimental results show that the proposed algorithms are powerful in terms of accuracy in the 28 standard functions from the CEC2O13. For DSS, it outperforms several famous SSs in DA. For FSS, it is superior or competitive to 11 participating algorithms benchmarked on CEC2O13 and five recently proposed algorithms improved by new selection strategy. Since FSS is a universal framework, it can be applied to other swarm intelligence algorithms with selection strategy. (C) 2020 Elsevier B.V. All rights reserved.

关键词： dandelion algorithm Selection strategy Position distance Fusion pool Overall reward

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Parameter Estimation of an unmanned aerial vehicle using dandelion algorithm 26

Parameter Estimation of an unmanned aerial vehicle using dan...

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Conference on Unmanned Systems Technology XXVI

作者： AlShabi, Mohammad Hassan, Omar Saleh Osman Obaideen, Khaled Gadsden, S. Andrew Bettayeb, Maamar Univ Sharjah Dept Mech & Nucl Engn Sharjah U Arab Emirates RISE Biosensing & Biosensors Grp Smart Automat & Commun Technol Sharjah U Arab Emirates McMaster Univ Dept Mech Engn Hamilton ON Canada Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates

ISBN: (纸本)9781510674295;9781510674288

In this study, we introduce a novel approach to the parameter estimation of Unmanned Aerial Vehicles (UAVs) utilizing the dandelion algorithm, a bio-inspired optimization technique that simulates the seed dispersal mechanism of dandelions. With UAVs increasingly becoming integral to various sectors, accurate parameter estimation emerges as a critical factor in ensuring their optimal performance and safety. Traditional parameter estimation methods often fall short, plagued by computational inefficiencies and a propensity for local optima, which can significantly hinder UAV operations. The dandelion algorithm, with its unique global search capabilities and adeptness in navigating multidimensional spaces, presents a solution that markedly enhances the precision and speed of parameter estimation. Through a series of simulations involving diverse UAV models, this study compares the performance of the dandelion algorithm against the conventional technique;the Particle Swarm Optimization (PSO), demonstrating its superior ability in achieving rapid convergence, higher accuracy, and an exceptional aptitude for avoiding local optima. Our findings not only underscore the algorithm's potential to revolutionize UAV parameter estimation but also highlight its applicability in advancing UAV technology and bio-inspired computational algorithms. This research contributes to the aerospace engineering field by offering an innovative, efficient alternative to existing parameter estimation methods, promising significant improvements in the design, operation, and safety of UAV systems across a spectrum of applications.

关键词： Unmanned Aerial Vehicle dandelion algorithm UAVs Parameter Estimation

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A binary dandelion algorithm using seeding and chaos population strategies for feature selection

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APPLIED SOFT COMPUTING 2022年 125卷

作者： Zhao, Yuxin Dong, Junwei Li, Xiaobo Chen, Hui Li, Shaolang Zhejiang Normal Univ Coll Math & Comp Sci Jinhua 321004 Peoples R China

Feature selection (FS) is an important pre-processing step in data mining and pattern recognition. It can effectively compress the dimensionality of the feature space to reduce computation time and improve classification performance. The meta-heuristic algorithm-based feature selection method by finding the optimal set of features in the solution space has been widely used. However, this method is prone to trap into local optimality in a sufficiently large solution space. In this paper, we first propose a binary dandelion algorithm (BDA) to improve classification accuracy. In addition, to improve the performance of the algorithm, a binary dandelion algorithm using an improved seeding strategy and chaotic populations (SBDA) is proposed in this paper. Firstly, the strategy of optimizing the seeding radius by using the vibrational function and the historical optimal population increases the complexity of the search process and improves the search performance of the algorithm in the solution space. Secondly, when generating seeds, chaotic populations are generated using chaotic operators, which improves the ability of the algorithm to jump out of the local optimum and improves the stability of the algorithm. In this paper, 15 well-established datasets collected from the UCI machine learning database were adopted to compare four variants of BDA using only chaotic population improvement and in the next experiments, both mechanisms are verified to be effective in improving the performance of the algorithm. In addition, this paper compares the proposed BDA algorithm and SBDA algorithm with eight other classical algorithms. The experimental results show that SBDA can obtain fewer features with higher classification accuracy in most cases. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Metaheuristic algorithm dandelion algorithm Feature selection Seeding strategy Chaos algorithm

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A new dandelion algorithm and optimization for extreme learning machine

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2018年第1期30卷 39-52页

作者： Gong, Changqing Han, Shoufei Li, Xiguang Zhao, Liang Liu, Xiaojing Shenyang Aerosp Univ Sch Comp Shenyang Liaoning Peoples R China

In this paper, we proposed the dandelion algorithm (DA), based on the behaviour of dandelion sowing. In DA, the dandelion is sown in a certain range based on dynamic radius. Meanwhile the dandelion has self-learning ability;it could select a number of excellent seeds to learn. We compare the proposed algorithm with other existing algorithms. Simulations show that the proposed algorithm seems much superior to other algorithms. Moreover, the proposed algorithm can be applied to optimise extreme learning machine (ELM), which has a very good classification and prediction capability.

关键词： dandelion algorithm dandelion sowing global optimisation self-learning extreme learning machine

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Improvement of evolution process of dandelion algorithm with extreme learning machine for global optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 163卷

作者： Han, Shoufei Zhu, Kun Wang, Ran Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing 211106 Peoples R China

dandelion algorithm (DA) is a novel swarm intelligent optimization algorithm. In evolutionary process of DA, the quality of the seeds generated by dandelions is uneven, and the excellent seeds are expected to be retained and evaluated, while the poor seeds should be discarded without evaluation. In order to determine whether a seed is excellent or not, an improvement of evolution process of dandelion algorithm with extreme learning machine (ELMDA) is proposed in this paper. In ELMDA, firstly, the dandelion population can be partitioned into excellent dandelions and poor dandelions based on fitness values. Then, the excellent dandelions and poor dandelions are assigned corresponding labels (i.e. +1 if excellent or -1 if poor), which can be regarded as a training set, and the training model is built based on ELM. Finally, the model is applied to classify the seeds as excellent or poor, and the excellent seeds are chosen to participate in evolution process. Meanwhile, the robustness of the proposed algorithm is analyzed in this paper. Experimental results performed on test functions show that the proposed algorithm is competitive to its peers. Moreover, the proposed algorithm is demonstrated on three engineering designed problems, and the results indicate that the proposed algorithm has better performance in solving them.

关键词： dandelion algorithm Extreme learning machine Excellent seeds Poor seeds Training set Training model

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Fusion of transfer learning with nature-inspired dandelion algorithm for autism spectrum disorder detection and classification using facial features

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SCIENTIFIC REPORTS 2024年第1期14卷 1-19页

作者： Elangovan, G. Kumar, N. Jagadish Shobana, J. Ramprasath, M. Joshi, Gyanendra Prasad Cho, Woong SRM Inst Sci & Technol Sch Comp Dept Data Sci & Business Syst Chennai 603203 India Kangwon Natl Univ Dept AI Software Samcheok 25913 South Korea Kangwon Natl Univ Dept Elect Informat & Commun Engn Samcheok 25913 South Korea

Autism spectrum disorder (ASD) is a neurologic disorder considered to cause discrepancies in physical activities, social skills, and cognition. There is no specific medicine for treating this disorder;early intervention is critical to improving brain function. Additionally, the lack of a clinical test for detecting ASD makes diagnosis challenging. To regulate identification, physicians entertain the children's activities and growing histories. The human face is employed as a biological signature as it has the potential reflections of the brain. It is utilized as a simpler and more helpful tool for early detection. Artificial intelligence (AI) algorithms in medicinal rehabilitation and diagnosis can help specialists identify various illnesses more successfully. However, owing to its particular heterogeneous symptoms and complex nature, diagnosis of ASD remains to be challenging for investigators. This work presents a Fusion of Transfer Learning (TL) with the dandelion algorithm for Accurate Autism Spectrum Disorder Detection and Classification (FTLDA-AASDDC) method. The FTLDA-AASDDC technique detects and classifies autism and non-autism samples using facial images. To accomplish this, the FTLDA-AASDDC technique utilizes a bilateral filter (BF) approach for noise elimination. Next, the FTLDA-AASDDC technique employs a fusion-based TL process comprising three models, namely MobileNetV2, DenseNet201, and ResNet50. Moreover, the attention-based bi-directional long short-term memory (A-BiLSTM) method is used to classify and recognize ASD. Finally, the dandelion algorithm (DA) is employed to optimize the parameter tuning process, improving the efficacy of the A-BiLSTM technique. A wide range of simulation analyses is performed to highlight the ASD classification performance of the FTLDA-AASDDC technique. The experimental validation of the FTLDA-AASDDC technique portrayed a superior accuracy value of 97.50% over existing techniques.

关键词： Autism spectrum disorder Transfer learning Bilateral filter dandelion algorithm Fusion process Facial image

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Joint Deployment Optimization and Flight Trajectory Planning for UAV Assisted IoT Data Collection: A Bilevel Optimization Approach

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第11期23卷 21492-21504页

作者： Han, Shoufei Zhu, Kun Zhou, MengChu Liu, Xiaojing Nanjing Univ Aeronaut & Astronaut Collaborat Innovat Ctr Novel Software Technol & I Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China Macau Univ Sci & Technol Collaborat Lab Intelligent Sci & Syst Macau Peoples R China

This work investigates an unmanned aerial vehicle (UAV) assisted IoT system, where a UAV flies to each foothold to collect data from IoT devices, and then return to its start point. For such a system, we aim to minimize the energy consumption by jointly optimizing the deployment and flight trajectory of UAV. It is a mixed-integer non-convex and NP-hard problem. In order to address it, a bilevel optimization approach is proposed, where an upper-level method aims to optimize the deployment of UAV and a lower-level one aims to plan UAV flight trajectory. Specifically, the former optimizes the number and locations of footholds of UAV. This work proposes an improved dandelion algorithm with a novel encoding strategy, in which each dandelion represents a foothold of UAV and the entire dandelion population is seen as an entire deployment. Then, two mutation strategies are designed to adjust the number and locations of footholds. Based on the footholds of the UAV provided by the former, the latter transforms flight trajectory planning into a traveling salesman problem (TSP). This work proposes an iterated greedy algorithm to solve it efficiently. The effectiveness of the proposed bilevel optimization approach is verified on ten instances, and the experimental results show that it significantly outperforms other benchmark approaches.

关键词： Internet of Things Energy consumption Data collection Trajectory Optimization Autonomous aerial vehicles Trajectory planning Bilevel optimization deployment optimization dandelion algorithm flight trajectory planning iterated greedy algorithm machine learning UAV

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