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A Hybrid differential evolution algorithm Integrated with Particle Swarm Optimization

Journal of Donghua University(English Edition) 2014年第2期31卷 197-200页

作者：范勤勤颜学峰 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

To implement self-adaptive control parameters, a hybrid differential evolution algorithm integrated with particle swarm optimization （PSODE） is proposed. In the PSODE, control parameters are encoded to be a symbiotic individual of original individual, and each original individual has its own symbiotic individual. differential evolution （ DE） operators are used to evolve the original population. And, particle swarm optimization （PSO） is applied to co-evolving the symbiotic population. Thus, with the evolution of the original population in PSODE, the symbiotic population is dynamically and self-adaptively adjusted and the realtime optimum control parameters are obtained. The proposed algorithm is compared with some DE variants on nine functious. The results show that the average performance of PSODE is the best.

关键词： differential evolution algorithm particle swann optimization self-adaptive co-evolution

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Pattern Synthesis of Linear Antenna Array Using Improved differential evolution algorithm with SPS Framework

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SENSORS 2020年第18期20卷 5158页

作者： Zhang, Ruimeng Zhang, Yan Sun, Jinping Li, Qing Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Univ Cambridge Dept Engn Cambridge CB1 2PZ England

In this paper, an improved differential evolution (DE) algorithm with the successful-parent-selecting (SPS) framework, named SPS-JADE, is applied to the pattern synthesis of linear antenna arrays. Here, the pattern synthesis of the linear antenna arrays is viewed as an optimization problem with excitation amplitudes being the optimization variables and attaining sidelobe suppression and null depth being the optimization objectives. For this optimization problem, an improved DE algorithm named JADE is introduced, and the SPS framework is used to solve the stagnation problem of the DE algorithm, which further improves the DE algorithm's performance. Finally, the combined SPS-JADE algorithm is verified in simulation experiments of the pattern synthesis of an antenna array, and the results are compared with those obtained by other state-of-the-art random optimization algorithms. The results demonstrate that the proposed SPS-JADE algorithm is superior to other algorithms in the pattern synthesis performance with a lower sidelobe level and a more satisfactory null depth under the constraint of beamwidth requirement.

关键词： random optimization algorithm differential evolution algorithm SPS framework SPS-JADE algorithm antenna array sidelobe suppression null depth pattern synthesis

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Mechanistic Interpretation for Xanthate Adsorption onto Galena through Electrochemical Impedance Spectrum Fitting by a differential evolution algorithm

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INTERNATIONAL JOURNAL OF ELECTROCHEMICAL SCIENCE 2020年第1期15卷 666-676页

作者： Moreno-Medrano, E. D. Osuna-Enciso, Valentin Casillas, N. Pedroza-Toscano, M. A. Gutierrez-Becerra, A. Larios-Duran, E. R. Univ Guadalajara Engn & Basic Sci Dept CUTonala Tateposco 48525 Mexico Univ Guadalajara CUCEI Chem Dept Gdl 44430 Mexico Univ Ctr UTEG Gdl 44430 Mexico Univ Guadalajara CUCEI Chem Engn Dept Gdl 44430 Mexico

This work is focused on understanding the interaction of native galena minerals and xanthate as well as their influence on the process of mineral separation by flotation and leaching techniques. To obtain basic and fundamental information about this interaction, a quantitative analysis of an electrochemical impedance study based on a mechanistic approach and the obtaining of its impedance transfer function is presented. The impedance transfer function was obtained from a three-reversible-stage mechanism, which included 19 parameters, all of which were satisfactorily obtained by fitting the impedance transfer function to previously reported experimental impedance spectra of the galena/xanthate interface in a wide polarization potential range of -0.2 to 0.5 V vs. SCE. The fitting procedure consisted of the application of a differential evolution (DE) algorithm. The description of this algorithm, its application and its advantages in the electrochemical impedance field are presented. Furthermore, a detailed discussion about the quantitative values obtained and its interpretation for galena/xanthate interactions is presented.

关键词： galena xanthate impedance adsorption mechanics differential evolution algorithm

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Packet loss rate monitoring model of IoT based on differential evolution algorithm

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WEB INTELLIGENCE 2021年第3期19卷 215-226页

作者： Feng, Yinghua Yang, Wei Huainan Union Univ Sch Publ Educ Huainan 232001 Anhui Peoples R China Guangxi Vocat & Tech Inst Ind Dept Basic Course Nanning 530001 Guangxi Peoples R China

In order to overcome the problems of high energy consumption and low execution efficiency of traditional Internet of things (IoT) packet loss rate monitoring model, a new packet loss rate monitoring model based on differential evolution algorithm is proposed. The similarity between each data point in the data space of the Internet of things is set as the data gravity. On the basis of the data gravity, combined with the law of gravity in the data space, the gravity of different data is calculated. At the same time, the size of the data gravity is compared, and the data are classified. Through the classification results, the packet loss rate monitoring model of the Internet of things is established. differential evolution algorithm is used to solve the model to obtain the best monitoring scheme to ensure the security of network data transmission. The experimental results show that the proposed model can effectively reduce the data acquisition overhead and energy consumption, and improve the execution efficiency of the model. The maximum monitoring efficiency is 99.74%.

关键词： differential evolution algorithm IoT packet loss rate monitoring model

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An Adaptive differential evolution algorithm Based on Data Preprocessing Method and a New Mutation Strategy to Solve Dynamic Economic Dispatch Considering Generator Constraints

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COMPUTATIONAL ECONOMICS 2024年 1-34页

作者： Zhao, Ruxin Wang, Wei Zhang, Tingting Liu, Chang Fu, Lixiang Kang, Jiajie Zhang, Hongtan Shi, Yang Jiang, Chao Yangzhou Univ Sch Informat Engn Yangzhou 225127 Jiangsu Peoples R China Yangzhou Polytech Inst Sch Intelligent Mfg Yangzhou 225127 Jiangsu Peoples R China

differential evolution (DE) algorithm is a classical natural-inspired optimiza-tion algorithm which has a good. However, with the deepening of research, some researchers found that the quality of the candidate solution of the population in the differential evolution algorithm is poor and its global search ability is not enough when solving the global optimization problem. Therefore, in order to solve the above problems, we proposed an adaptive differential evolution algorithm based on the data processing method and a new mutation strategy (ADEDPMS). In this paper, the data preprocessing method is implemented by k-means clustering algorithm, which is used to divide the initial population into multiple clusters according to the average value of fitness, and select candidate solutions in each cluster according to different proportions. This method improves the quality of candidate solutions of the population to a certain extent. In addition, in order to solve the problem of insuf-ficient global search ability in differential evolution algorithm, we also proposed a new mutation strategy, which is called "DE/current-to-p1 best&p2 best". This strat-egy guides the search direction of the differential evolution algorithm by selecting individuals with good fitness, so that its search range is in the most promising can-didate solution region, and indirectly increases the population diversity of the algo-rithm. We also proposed an adaptive parameter control method, which can effec-tively balance the relationship between the exploration process and the exploitation process to achieve the best performance. In order to verify the effectiveness of the proposed algorithm, the ADEDPMS is compared with five optimization algorithms of the same type in the past three years, which are AAGSA, DFPSO, HGASSO, HHO and VAGWO. In the simulation experiment, 6 benchmark test functions and 4 engineering example problems are used, and the convergence accuracy, convergence speed and stability are

关键词： differential evolution algorithm Data preprocessing method Adaptive parameter control method Dynamic economic dispatch problem differential evolution algorithm Data preprocessing method DE/current-to-p1 best&p2 best Adaptive parameter control method Dynamic economic dispatch problem

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Improved differential evolution algorithm for resource-constrained project scheduling problem

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Journal of Systems Engineering and Electronics 2010年第5期21卷 798-805页

作者： Lianghong Wu Yaonan Wang Shaowu Zhou College of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan 411201 E R. China College of Electrical and Information Engineering Hunan University Changsha 410082 R R. China

An improved differential evolution（IDE）algorithm that adopts a novel mutation strategy to speed up the convergence rate is introduced to solve the resource-constrained project scheduling problem（RCPSP）with the objective of minimizing project duration Activities priorities for scheduling are represented by individual vectors and a senal scheme is utilized to transform the individual-represented priorities to a feasible schedule according to the precedence and resource constraints so as to be *** investigate the performance of the IDE-based approach for the RCPSP,it is compared against the meta-heuristic methods of hybrid genetic algorithm（HGA）,particle swarm optimization（PSO） and several well selected *** results show that the proposed scheduling method is better than general heuristic rules and is able to obtain the same optimal result as the HGA and PSO approaches but more efficient than the two algorithms.

关键词： differential evolution algorithm project soheduling resource constraint priority-based scheduling.

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A differential evolution algorithm for tooth profile optimization with respect to balancing specific sliding coefficients of involute cylindrical spur and helical gears

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ADVANCES IN MECHANICAL ENGINEERING 2015年第9期7卷 1-11页

作者： Abderazek, Hammoudi Ferhat, Djeddou Atanasovska, Ivana Boualem, Keskes Setif I Univ Inst Opt & Precis Mech Appl Precis Mech Lab Setif 19000 Algeria Univ Belgrade Fac Mech Engn Innovat Ctr Belgrade Serbia

Profile shift has an immense effect on the sliding, load capacity, and stability of involute cylindrical gears. Available standards such as ISO/DIS 6336 and BS 436 DIN/3990 currently give the recommendation for the selection of profile shift coefficients. It is, however, very approximate and usually given in the form of implicit graphs or charts. In this article, the optimal selection values of profile shift coefficients for cylindrical involute spur and helical gears are described, using a differential evolution algorithm. The optimization procedure is developed specifically for exact balancing specific sliding coefficients at extremes of contact path and account for gear design constraints. The obtained results are compared with those of standards and research of other authors. They demonstrate the effectiveness and robustness of the applied method. A substantial improvement in balancing specific sliding coefficients is found in this work.

关键词： Gears profile shift coefficients specific sliding optimization differential evolution algorithm

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Research on personalised recommendation method for English online course resources based on hybrid differential evolution algorithm

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2024年第1-2期74卷 73-79页

作者： Zhu, Wei Nanjing Normal Univ Taizhou Coll Sch Foreign Languages & Cultures Taizhou Nanjing Peoples R China

In order to improve personalised satisfaction and recommendation accuracy of English resources, and effectively reduce recommendation time, this article proposes a personalised recommendation method for English online course resources based on hybrid differential evolution algorithm. Firstly, collect English resource data;secondly, matrix decomposition technology is introduced to determine the user's interest information. Then, select the naive Bayesian algorithm to classify the resources. Finally, the fitness value of the individual is calculated, and the personalised recommendation function is designed using the hybrid differential evolution algorithm. The recommendation function is solved through differential mutation, and the final recommendation result is obtained. The results show that the recommendation satisfaction of this method can reach up to 99.5%, the accuracy can reach 99.5% and the recommendation time always does not exceed 4 seconds, effectively improving the personalised precision recommendation effect.

关键词： differential evolution algorithm matrix decomposition Naive Bayes English online courses personalised recommendations

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Performance Analyses of differential evolution algorithm Based on Dynamic Fitness Landscape

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2019年第1期13卷 36-61页

作者： Li, Kangshun Liang, Zhuozhi Yang, Shuling Chen, Zhangxing Wang, Hui Lin, Zhiyi South China Agr Univ Guangzhou Guangdong Peoples R China Univ Calgary iCtr Simulat & Visualizat Calgary AB Canada

Dynamic fitness landscape analyses contain different metrics to attempt to analyze optimization problems. In this article, some of dynamic fitness landscape metrics are selected to discuss differential evolution (DE) algorithm properties and performance. Based on traditional differential evolution algorithm, benchmark functions and dynamic fitness landscape measures such as fitness distance correlation for calculating the distance to the nearest global optimum, ruggedness based on entropy, dynamic severity for estimating dynamic properties, a fitness cloud for getting a visual rendering of evolvability and a gradient for analyzing micro changes of benchmark functions in differential evolution algorithm, the authors obtain useful results and try to apply effective data, figures and graphs to analyze the performance differential evolution algorithm and make conclusions. Those metrics have great value and more details as DE performance.

关键词： Benchmark Functions differential evolution algorithm Dynamic Fitness Landscape Analysis Global Optimum

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A limited-memory BFGS-based differential evolution algorithm for optimal control of nonlinear systems with mixed control variables and probability constraints

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NUMERICAL algorithmS 2023年第2期93卷 493-542页

作者： Wu, Xiang Zhang, Kanjian Guizhou Normal Univ Sch Math Sci Guiyang 550001 Peoples R China Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Peoples R China Southeast Univ Key Lab Measurement & Control CSE Minist Educ Nanjing 210096 Peoples R China

In this paper, we consider an optimal control problem of nonlinear systems with mixed control variables and probability constraints. To obtain a numerical solution of this optimal control problem, our target is to formulate this problem as a constrained nonlinear parameter optimization problem (CNPOP), which can be solved by using any gradient-based numerical computation method. Firstly, some binary functions are introduced for each value of the discrete-valued control variable (DCV). Following that, we relax these binary functions and impose a penalty term on the relaxation such that the solution of the resulting relaxed problem (RP) can converge to the solution of the original problem as the penalty parameter increases. Secondly, we introduce a simple initial transformation for the probability constraints. Following that, an adaptive sample approximation method (ASAM) and a novel smooth approximation technique (NSAT) are adopted to formulate the probability constraints as some deterministic constraints. Thirdly, a control parameterization approach (CPA) is used to transform the deterministic problem (i.e., an infinite dimensional problem) into a finite dimensional CNPOP. Fourthly, in order to combine the advantages of limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithms and differential evolution (DE) algorithms, a L-BFGS-based DE (L-BFGS-DE) algorithm is proposed for solving the resulting approximation problem based on an improvied L-BFGS (IL-BFGS) method and an improved DE (IDE) algorithm. Following that, we establish the convergence result of the L-BFGS-DE algorithm. The L-BFGS-DE algorithm consists of two stages. The objectives of the first and second stages are to obtain a probable position of the global solution and to accelerate the convergence rate, respectively. In the IL-BFGS method, we propose a novel updating rule (NUR), which uses not only the gradient information of the objective function but also the value of the objective function. T

关键词： Optimal control Nonlinear systems Mixed control variables Probability constraints Limited memory BFGS method differential evolution algorithm

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