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2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE)

作者： Zhu, Changming Yan, Yan Haierhan Ni, Jun China Acad Launch Vehicle Technol Res & Dev Ctr Beijing 100076 Peoples R China Shanghai Sanda Univ Shanghai Peoples R China

ISBN: (纸本)9781509004546

differential evolution (DE) is one of the current best evolutionary algorithms. It becomes the popular research topic in many fields such as evolutionary computing and intelligent optimization. At present, DE has successfully been applied to diverse domains of science and engineering, such as signal processing, neural network optimization, pattern recognition, machine intelligence, chemical engineering and medical science. However, almost all the evolutionary algorithms, including DE, still suffer from the problems of premature convergence, slow convergence rate and difficult parameter setting. To overcome these drawbacks, we propose a novel Teaching-Learning-Based differential evolution algorithm(TLDE), in which the pheromone and the sensitivity model in free search algorithm to replace the traditional roulette wheel selection model, and introduces OBL to present an improved artificial bee colony algorithm. Experimental results confirm the superiority of Teaching-Learning-Based differential evolution algorithm over several state-of-the-art evolutionary optimizers.

关键词： differential evolution algorithm Teaching-Learning Optimization problem

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Gray-scale Image Enhancement Using differential evolution Optimization algorithm

Gray-scale Image Enhancement Using Differential Evolution Op...

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International Conference on Information Technology (InCITe) - The Next Generation IT Summit on the Theme - Internet of Things - Connect Your Worlds

作者： Gupta, Rohan Sehgal, Smriti Amity Univ Dept CSE Noida India

ISBN: (纸本)9781509026128

differential evolution algorithm (DE) is a search method that iteratively searches for the solutions of machine learning and engineering problems that involve optimization. This paper aims at displaying the effectiveness and adaptability of the DE algorithm to find the global optimal solutions iteratively for contrast enhancement of grayscale images. An image's contrast can be modified by gray-level adjustments to the pixel intensities of the original image with the help of a parameterized intensity transformation function. A quality function is used to judge the quality of the enhanced images which incorporates various conditions of image enhancement and is used as the fitness criterion. The differential evolution algorithm aims at maximizing the fitness function through adjustments to variables of the pixel intensity transformation function.

关键词： Gray-scale images image enhancement differential evolution algorithm histogram equalization local enhancement

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An Improved Selection Strategy differential evolution algorithm for Reconfiguration of Shipboard Power System 5

An Improved Selection Strategy Differential Evolution Algori...

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5th International Conference on Information Science and Technology (ICIST)

作者： Ma, Lisheng Zhang, Jundong Ren, Guang Dalian Maritime Univ Coll Marine Engn Dalian Liaoning Provin Peoples R China

ISBN: (纸本)9781479974894

In allusion to oneness problem of optimal solution in shipboard power system (SPS) network reconfiguration, an improved elitist selection strategy of differential evolution algorithm is proposed. The improved selection strategy which based on nondominated front and crowding distance is introduced into the differential evolution (DE) algorithm. Which enhance the uniformity and diversity of optimal solution. In order to enhance the quality of the population, the chaotic initialization based on Tent map is adopted to initialize the population. Adaptive mutation and crossover operator are introduced to accelerate the convergence of solution. Experiment results show that the improved algorithm not only keep the speed of convergence, but also improve the diversity and uniformity of optimal solution.

关键词： shipboard power system differential evolution algorithm improved selection strategy reconfiguration

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A Novel differential evolution algorithm for Threat-Oriented Weapon System Planning 9

A Novel Differential Evolution Algorithm for Threat-Oriented...

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9th Annual IEEE International Systems Conference (SysCon)

作者： Liu, Can Ge, Bingfeng Yang, Kewei Jiang, Jiang Li, Mengjun Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781479959273

Weapon system planning is of vital importance to a country's national security. Currently, a number of countries are under threat from potential hostile countries, especially those with advanced weapons. In this regard, weapon systems must be developed as the countermeasures that eliminate potential threats. A model of threat-oriented weapon system planning is first proposed, wherein countries must decide how much and when to develop weapons under a number of certain constrains to mitigate or even neutralize threats as much as possible. Then, a novel differential evolution with neighborhood revision algorithm is presented to optimize this problem. Finally, the experimental results illustrate that the proposed differential evolution with neighborhood revision algorithm has a more outstanding performance than two other algorithms.

关键词： weapon system planning differential evolution algorithm threat neighborhood revision

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A differential evolution-based single-level algorithm for jointly optimizing the deployment and flight trajectory of UAV-assisted data collection system

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APPLIED SOFT COMPUTING 2025年 171卷

作者： Lin, Lianhai Tian, Liqin Wang, Zhigang Yang, Wenguang Qinghai Normal Univ Sch Comp Sci Xining 810001 Qinghai Peoples R China North China Inst Sci & Technol Sch Comp Sci Langfang 065201 Hebei Peoples R China North China Inst Sci & Technol Sch Sci Langfang 065201 Hebei Peoples R China

This paper presents an unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) data collection system, where a UAV collects data from IoT devices at various stop points and returns to its starting point. Our goal is to minimize energy consumption by jointly optimizing the UAV's deployment and flight trajectory. This problem is complex and NP-hard. To address this, this paper proposes the differential evolution with Variable Population Size and Route (DEVIPSR) algorithm, a single-level model that improves upon traditional differential evolution (DE). Each individual in the population represents both the position and order of stop points in the UAV's trajectory, allowing comprehensive optimization of deployment and flight planning. This paper also introduces a double replacement (DR) strategy and an initialization strategy to enhance convergence speed. The LKH algorithm is used to finalize the trajectory optimization. Experimental results show that DEVIPSR algorithm outperforms multi-level optimization models by reducing total energy consumption by approximately 18.26%.

关键词： differential evolution algorithm Deployment optimization UAV Data collection LKH algorithm

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A recursive framework for improving the performance of multi-objective differential evolution algorithms for gene selection

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SWARM AND evolutionARY COMPUTATION 2024年 87卷

作者： Li, Min Zhao, Yangfan Cao, Rutun Wang, Junke Wu, Depeng Nanchang Inst Technol Sch Informat Engn 289 Tianxiang Rd Nanchang Jiangxi Peoples R China

Gene selection is a pivotal process in machine-learning-driven medical diagnostics, where the goal is to identify a subset of genes from microarray expression profiles that can enhance the predictive accuracy of classifiers for disease diagnosis. The two key objectives of gene selection are to reduce the dimensionality of the data and to improve the accuracy of disease diagnosis, which is typically a multi-objective optimization problem. In recent years, multi-objective evolutionary algorithms (MOEAs) have gained wide attention in feature selection research, and several related algorithms have been produced. However, most algorithms tend to get stuck in local optimality when searching for solutions from a high-dimensional space. To solve the gene selection problem effectively, this study introduces a recursive multi-objective differential evolution algorithm with elite recursive strategy (RMODE-E) and a recursive multi-objective differential evolution algorithm with Pareto front recursive strategy (RMODE-P). RMODE-E amalgamates the features selected by the top E elite individuals, RMODE-P consolidates the features selected by the Pareto front set, and the combined features then serve as the foundation for subsequent recursive rounds of searching. The proposed feature subspace combination strategy not only reduces the recursive search space but also improves the capacity to find globally optimal feature subsets. Extensive experiments were conducted to compare our proposed algorithms with eight state-of-the-art evolutionary algorithms to validate their effectiveness. Experimental results demonstrate that RMODE-P has better global search capability as it achieves better best classification accuracy, mean classification accuracy, and minimal gene subset size.

关键词： Gene selection Feature selection Microarray data differential evolution algorithm Multi-objection optimization

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Optimization of Electrocardiogram Classification Using Dipper Throated algorithm and differential evolution

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Computers, Materials & Continua 2023年第2期74卷 2379-2395页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Electrocardiogram(ECG)signal is a measure of the heart’s electrical ***,ECG detection and classification have benefited from the use of computer-aided systems by *** goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and differential evolution algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification *** addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall *** prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing ***,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA *** results confirmed the superiority and effectiveness of the proposed *** classification accuracy achieved by the proposed approach is(99.98%).

关键词： Electrocardiogram differential evolution algorithm dipper throated optimization neural networks

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Layout Optimization of Braced Frames Using differential evolution algorithm and Dolphin Echolocation Optimization

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2015年第3期59卷 441-449页

作者： Kaveh, Ali Farhoudi, Neda Iran Univ Sci & Technol Sch Civil Engn Ctr Excellence Fundamental Studies Struct Engn Narmak Iran

In this study, topology optimization is applied to concentrically braced frames in order to find economical solutions for conventional structural steel frames. differential evolution algorithm and Dolphin Echolocation Optimization are applied for structural optimization. Numerical examples are studied and results of comparison with other meta-heuristic algorithms, including Genetic algorithm, Ant colony optimization, Particle Swarm, and Big Bang-Big Crunch are presented.

关键词： differential evolution algorithm Dolphin Echolocation Optimization Layout optimization Steel braced frames Meta-heuristic algorithms

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Automatic SWMM Parameter Calibration Method Based on the differential evolution and Bayesian Optimization algorithm

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WATER 2023年第20期15卷 3582-3582页

作者： Gao, Jiawei Liang, Ji Lu, Yu Zhou, Ruilong Lu, Xin Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China Sichuan Hydraul Res Inst Chengdu 610072 Peoples R China

In response to the low accuracy exhibited by the Storm Water Management Model (SWMM), we propose an enhanced differential evolution and Bayesian Optimization algorithm (DE-BOA). This algorithm integrates the global search capability of the differential evolution algorithm with the local search capability of the Bayesian optimization algorithm, which enables a more comprehensive exploration of the vector solution space. A comparative analysis of various types of rainfall events is conducted. For model calibration and validation, a drainage subzone in Jinshazhou, Guangzhou City, is selected as the research subject. In total, 20 specific rainfall events are selected, and the DE-BOA algorithm outperforms the manual calibration, the differential evolution algorithm, and the Bayesian optimization algorithm regarding model calibration accuracy. Furthermore, the DE-BOA algorithm exhibits robust adaptability to rainfall events characterized by multiple peaks and higher precipitation levels, with the Nash-Sutcliffe efficiency coefficient values surpassing 0.90. This study's findings could hold significant reference value for dynamically updating model parameters, thereby enhancing the model simulation performance and improving the accuracy of the urban intelligent water management platform.

关键词： SWMM differential evolution algorithm Bayesian optimization algorithm automatic parameter calibration urban drainage

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Intelligent Indoor Layout Design Based on Interactive Genetic and differential evolution algorithms

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2024年第4期28卷 929-938页

作者： Li, Shicheng Chen, Shufang Zheng, Zhonghui Changsha Univ Sci & Technol Sch Design Art 960 2nd SectWanjiali RD S Changsha 410004 Hunan Peoples R China Changsha Univ Sci & Technol Sch Math & Stat 960 2nd SectWanjiali RD S Changsha 410004 Hunan Peoples R China

As the real estate industry expands with time, the personalized needs of users for indoor space layouts have become increasingly complex. Traditional indoor space layout design methods can no longer meet the needs of large market groups because of their complex steps and low levels of specialization. Therefore, this study first analyzes the problematic factors in indoor space layout design. Second, an interactive genetic algorithm is introduced to solve the multifactor optimal selection problem;the process is optimized and improved using a differential evolution algorithm. A comprehensive spatial layout model combining interactive genetic and differential evolution algorithms is proposed. The experimental results show that the model performs best with uniform variation, and its average number of iterations to find the optimal individual is 57. In addition, compared with similar layout models, the proposed model achieved the highest space utilization value of 79%, which is approximately 19% higher than that for the stacking layout model;it also required the shortest time, that is, 15 min. In summary, the proposed model provides a new intelligent method for indoor layout design, which is expected to improve the satisfaction of designers and users.

关键词： indoor layout interactive genetic algorithm differential evolution algorithm optimization problem space utilization rate

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