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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Yunwei Pu Lanyu Ma Wentai Hou Yuanpu Guo Computer Center Kunming Univ. of Science and Tech. Kunming China Faculty of Information Engineering and Automation Kunming Univ. of Science and Tech.

ISBN: (纸本)9781538653746

The ambiguity function is an effective tool to solve the problem of radar emitter signals sorting in complex systems. To improve the real-time and accuracy of the ambiguity function main ridge (AFMR), this paper proposes an intelligent method to search AFMR based on adaptive differential evolution algorithm. The signal is pre-identified by the instantaneous frequency (IF) features to reduce the AFMR search range and the complexity of the problem. In this way, the differential evolution algorithm parameters can be adaptively adjusted according to the pre-recognition results to accelerate the convergence of the algorithm. Then the statistical hypothesis testing theory is used to analyze and test the search results. The experiment results show that the proposed method reduces the time of searching AFMR to about 4% compared with the exhaustive method with higher accuracy and stability.

关键词： Radar emitter signal adaptive differential evolution algorithm Ambiguity function main ridge Signal sorting and recognition differential evolution algorithms Antiferromagnetic resonance ambiguity function evolutionary algorithm ambiguity Intelligent retrieval instantaneous frequency ridges Crest

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Optimal sub-band adaptive thresholding based edge preserved satellite image denoising using adaptive differential evolution algorithm

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NEUROCOMPUTING 2016年第PartB期174卷 698-721页

作者： Bhandari, A. K. Kumar, D. Kumar, A. Singh, G. K. PDPM Indian Inst Informat Technol Design & Mfg Jabalpur 482005 Madhya Pradesh India Natl Inst Technol Dept Elect & Commun Engn Patna 800005 Bihar India Indian Inst Technol Roorkee Dept Elect Engn Roorkee 247667 Uttarakhand India

An image is often corrupted by different kinds of noise during its acquisition and transmission. Conventional denoising methods can suppress the Gaussian noise effectively, but fail to maintain the quality of denoised images and may blur edges in an image. To address these short comings, this paper aims to develop an optimized adaptive thresholding function based framework for edge preserved satellite image denoising using different nature inspired algorithms which is capable of effectively removing the Gaussian noise from images without over smoothing edge details. Image denoising using adaptive thresholding functions selects the suitable threshold values to separate noise from the actual image without affecting the actual features of the image. In this approach, most widely used nature inspired optimization algorithms are exploited for learning the parameters of adaptive thresholding function required for optimum performance. It was found that the proposed adaptive differential evolution algorithm (JADE) algorithm based denoising approach has superior features and give better performance in terms of PSNR, MSE, SSIM and FSIM as compared to other methods. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Image denoising DWT Optimization algorithms adaptive thresholding function adaptive differential evolution algorithm

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An effective multivariate time series classification approach using echo state network and adaptive differential evolution algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 43卷 237-249页

作者： Wang, Lin Wang, Zhigang Liu, Shan Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China Xi An Jiao Tong Univ Sch Management Xian 710049 Peoples R China

The multivariate time series (MIS) classification is a very difficult process because of the complexity of the MIS data type. Among all the methods to resolve this problem, the attribute-value representation classification approaches are the most popular. Despite their proven effectiveness of these however, these approaches are time consuming, sensitive to noise, or prone to damage of inner data properties as well as capable of producing undesirable accuracy. In this paper, we propose a new approach (CADS) for MIS classification that utilizes recurrent neural network (RNN) and adaptive differential evolution (ADE) algorithm. The approach can effectively overcome specific shortcomings of the attribute-value representation approaches. The principle of this approach adheres to three steps. First, an RNN is used to project the training MIS samples into different state clouds (samples in the same class are projected into a state cloud). Second, classifiers from these state clouds are induced for different classes. Third, the final MIS classifiers are obtained using ADE for parameter optimization. This approach makes full use of the network state space of a given RNN to induce classifiers rather than to train the network. Experimental results performed on 18 data sets demonstrate the accuracy and robustness of the proposed approach for MIS classification. As a new and universal approach, CADS can be very effective and stable for handling a variety of complex classification problems. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Multivariate time series classification Recurrent neural network adaptive differential evolution algorithm

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A differential evolution algorithm with adaptive Population Size Reduction Strategy 1

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7th International Symposium on Artificial Intelligence and Robotics (ISAIR)

作者： Zhang, Xiaoyan Duan, Zhengyu Liu, Qianqian Xian Univ Sci & Technol Xian 710000 Peoples R China

ISBN: (数字)9789811979439

ISBN: (纸本)9789811979422;9789811979439

Aiming at the problem that the differential evolution algorithm easily falls into a local optimum and results in premature convergence, a new differential evolution algorithm with an adaptive population size reduction strategy (APRDE) is proposed. Firstly, in the mutation and crossover operation, to balance the local exploitation and global exploration capabilities of the algorithm, a parameter adaptive tunning scheme based on the hyperbolic tangent function and Cauchy distribution is proposed to adaptively adjust the parameter factors. Secondly, an ordered mutation strategy is adopted to guide the direction of mutating and enrich the diversity of the population. Lastly, after each evolution iteration, adaptively reducing the population size according to the error between the fitness values of individuals and the current optimal. The proposed algorithm is compared with 5 other optimization algorithms on 8 typical benchmark functions. The results show that the algorithm has a great improvement in solution accuracy, stability and convergence speed.

关键词： adaptive differential evolution algorithm Parameters tunning scheme Ordered mutation strategy Population size reduction strategy

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No-reference Path Receding Horizon Control for Multi-UAV Formation Reconfiguration Based on adaptive differential evolution

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2024年第8期22卷 2633-2643页

作者： Liu, Xin Chen, Yong Zhang, Siweihua Fu, Pengcheng Xiangtan Univ Yanggutang St Xiangtan 411105 Hunan Peoples R China

As unmanned aerial vehicles (UAVs) have limited energy resources and diverse constraints, the reconfiguration of their formation encounters substantial challenges. In this paper, we employ a leader-follower method. In order to minimize flight distance and resource consumption, a greedy algorithm is used to allocate leader and follower positions. Based on the limitations of the receding horizon control (RHC) method and the control parameterization and time discretization (CPTD) method, we propose the no reference path RHC (NRPRHC) method. The proposed method transforms the formation reconfiguration into smaller local optimization problems, leading to a reduction in the size of the optimization stages and computational complexity. For each local optimization problem, we propose the adaptive population differential evolution (APDE) algorithm to optimize the control inputs. Finally, the results are provided to illustrate the feasibility and effectiveness of the proposed method.

关键词： adaptive differential evolution algorithm formation reconfiguration receding horizon control unmanned aerial vehicle

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An enhanced distributed differential evolution algorithm for portfolio optimization problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期121卷

作者： Song, Yingjie Zhao, Gaoyang Zhang, Bin Chen, Huayue Deng, Wuquan Deng, Wu Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Peoples R China Shandong Technol & Business Univ Sch Stat Yantai 264005 Peoples R China China West Normal Univ Sch Comp Sci Nanchong 637002 Peoples R China Chongqing Univ Cent Hosp Dept Endocrinol Key Lab Biorheol Sci & TechnolMinist Educ Chongqing 400014 Peoples R China Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Peoples R China

The population structure of differential evolution (DE) algorithm cannot maintain the diversity of the population to the greatest extent and help the population avoid to fall into the local optima in time. In this paper, a co-evolutionary multi-swarm adaptive differential evolution algorithm, namely ECMADE is proposed to solve the premature convergence and search stagnation. First of all, in terms of population structure, based on the parallel distributed framework, ECMADE randomly and evenly divides the population into exploration subpopulation, development subpopulation, and auxiliary subpopulation, and introduces an adaptive information exchange mechanism so that subpopulations can escape local optima in time. Then, a multi-operator parallel search strategy is proposed to keep population diversity and meet the optimization needs of different problems. Finally, an adaptive adjustment mechanism of control parameters is developed, through recent elite parameter archive and weight distribution to fully mine successful parameter information, and generate control parameters with a high success rate for the current evolutionary stage. In order to prove the effectiveness of the ECMADE, 10 test functions and portfolio optimization problem are selected in here. The experiment results show that the ECMADE can effectively solve these test functions, the accuracy and efficiency is superior to those of two classical DE algorithms. The actual application results show that the ECMADE can significantly improve the ability of portfolio to resist extreme losses, which proves the effectiveness and feasibility of the ECMADE once again. The ECMADE has better optimization performance by comparing with some well-known algorithms in term of the solution quality, robustness and space distribution. It provides a new algorithm for solving complex optimization problems.

关键词： adaptive differential evolution algorithm Multi-population parallel distribution structure Parallel search Portfolio optimization Fuzzy c-means clustering algorithm

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A Unified Model for Multi-Satellite Imaging Mission Planning in Various Scenarios

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IEEE ACCESS 2023年 11卷 46144-46163页

作者： Yang, Xueying Hu, Min Zhang, Rui Huang, Gang Space Engn Univ Dept Aerosp Sci & Technol Beijing 101416 Peoples R China

Multi-satellite imaging mission planning (MSIMP) has been difficult in various scenarios due to the complex constraints of multi-satellite imaging, the wide area covered by target points, and the difficulty of achieving different mission requirements in a short period of time with limited satellite resources. In addressing this challenge, this work investigates multi-satellite imaging mission planning based on the Unified Plan Model and Improved adaptive differential evolution algorithm (UPM-IADE). First, a unified model is built based on two scenarios: a large-scale imaging mission and an emergency support mission, and then a mission assignment framework is adaptively selected based on mission priority. Second, a monorail task synthesis method based on visible time windows is created to clarify the execution relationship between the satellite and the target point. Finally, an individual weight ranking rule is developed, and the weight is used to combine the fitness value ranking and diversity ranking into a final fitness value ranking, which is used to select individuals that satisfy the mutation requirements into the mutation strategy pool for adaptive mutation strategy selection. Experiments 1, 2, 3, and 4 have demonstrated that UPM-IADE can successfully resolve the imaging satellite mission planning for both scenarios while providing remarkable performance in terms of high mission benefit and rapid response.

关键词： Satellites evolutionary computation Image resolution Task analysis Optimization Heuristic algorithms Statistics Multi-satellite imaging mission planning global large-scale imaging mission emergency support mission adaptive differential evolution algorithm individual weight ranking rule

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MR Image Denoising Based on Improved Multipath Matching Pursuit algorithm 5th

MR Image Denoising Based on Improved Multipath Matching Purs...

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5th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Li, Chenxi Luo, Yitong Yang, Jing Fan, Hong Shaanxi Normal Univ Sch Comp Sci Xian 710062 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

ISBN: (纸本)9783031189098;9783031189104

Magnetic resonance images are accompanied by random Rician noise due to the influence of uncertain factors in the process of imaging, storage, which brings a lot of inconvenience to the subsequent processing of the image and clinical diagnosis. This paper proposes an improved multipath matching pursuit algorithm based on learning Gabor pattern dictionary atom for image reconstruction and denoising. Firstly, Gabor wavelet transform based on neurophysiological constraints is used to generate dictionary atoms that match the local features of the image;Then this paper introduces adaptive differential evolution algorithm optimization to the process of solving multiple candidate atoms matching the local image features in each iteration of the multipath matching pursuit. It combines the advantages of adaptive differential evolution and multipath matching pursuit algorithm, not only avoids the genetic falling into the local optimal defect, but also obtains the best matching parameters with higher accuracy, and effectively reduces the computational complexity of the multipath matching pursuit. In the reconstruction experiment of the simulated MR images, compared with state-of-the-art denoising algorithms, our algorithm not only shows better denoising performance, but also retains more detailed information, and the running time is reduced nearly 50% than multipath matching pursuit;which fully shows the clinical application value.

关键词： Gabor pattern Multipath matching pursuit algorithm adaptive differential evolution algorithm Dictionary atom MR image denoising

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A machine learning-based adaptive heuristic for vessel scheduling problem under uncertainty via chance-constrained programming

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COMPUTERS & ELECTRICAL ENGINEERING 2024年第PartA期119卷

作者： Li, Runfo Zhang, Xinyu Wang, Chengbo Cui, Jinlong Mu, Mengfeng Dalian Maritime Univ Nav Coll Maritime Intelligent Transportat Res Team Dalian 116026 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Dept Automat Hefei Peoples R China

Efficient vessel scheduling in port is critical for enhancing navigational efficiency. However, it faces substantial challenges due to unforeseeable events. In this context, this paper addresses the vessel scheduling problem with stochastic sailing times in port. The problem is formulated into a chance-constrained programming (CCP) model and then transformed into an equivalent deterministic programming problem. A novel approach utilizing a machine learning-based adaptive differential evolution algorithm (MLDE) is proposed to address this model. In MLDE, a K-means clustering method is employed to generate initial population, aiming to enhance the population's quality and diversity while mitigating the impact of random interference. Throughout the mutation and crossover stages, we introduce a parameter adaption strategy based on Q-learning, which is established as a Markov decision process (MDP) model. The model effectively defines the state, action, and reward functions to guide the population toward selecting the optimal scaling factor and crossover probability parameters. Numerical experiments based on different instance sizes are conducted at the Comprehensive port. The obtained results reveal the superior performance of the MLDE algorithm in comparison to existing metaheuristic algorithms and traditional differential evolution (DE) variants. A statistical analysis experiment is also conducted to further confirm the superiority of MLDE.

关键词： Vessel scheduling Machine learning Stochastic sailing times Chance-constrained programming adaptive differential evolution algorithm

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Iterative Learning Control of Batch Processes Using adaptive differential evolution algorithm

Iterative Learning Control of Batch Processes Using Adaptive...

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Asia Simulation Conference/International Conference on System Simulation and Scientific Computing (AsiaSim and ICSC 2012)

作者： Zhang, Guohua Yuan, Kai Jia, Li Shanghai Univ Shanghai Key Lab Power Stn Automat Technol Dept Automat Coll Mechatron Engn & Automat Shanghai 200072 Peoples R China

ISBN: (纸本)9783642343803

Considering the potentials of iterative learning control as framework for industrial batch process control and optimization, an iterative learning control based on adaptive differential evolution algorithm is proposed in this paper. At first, quadratic criterion-iterative learning control with adaptive differential evolution algorithm is used to improve the performance of iterative learning control. In addition, the strategy of eliminating error using iterative algorithm is employed to drive the solution to the optimal point. As a result, the proposed method can avoid the problem of falling into local extreme points when solving the objective function with multiple local extreme points, which usually exists in traditional gradient-based iterative learning control. Lastly, example is used to illustrate the performance and applicability of the proposed method.

关键词： batch process adaptive differential evolution algorithm iterating learning control

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