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3rd International Conference on Manufacturing Science and Engineering (ICMSE 2012)

作者： Wang Zhan-kui Zheng Yan-ling Henan Inst Sci & Technol Henan Peoples R China

ISBN: (纸本)9783037853733

An optimal design math model of 2KH planetary gear was built based the planet gear design features, which was resolved by the differential evolution algorithm. A program was programmed used the software Visual Basic 6.0, which was used for engineering example to checking it. The program running results indicated that the differential evolution algorithm can be used to resolve the optimal design problem of 2KH planetary gear and the calculating results of the program is proper and has some use value.

关键词： Planetary gear Optimal design differential evolution algorithm

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Self-adapting differential evolution algorithm with Extension Variable Dimension

Self-adapting Differential Evolution Algorithm with Extensio...

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24th Chinese Control and Decision Conference (CCDC)

作者： Feng Da Gao Yuan Gao LiQun AVIC Aerodynam Res Inst Shenyang 110034 Peoples R China Univ Western Ontario Dept Commun Sci London ON N6A 5B7 Canada NE Univ Sch Informat Sci & Engn 110004 Shenyang Peoples R China

ISBN: (纸本)9781457720727

Recently, the differential evolution (DE) algorithm has attracted much attention as an effective approach for numerical optimization. Since the performance of DE is sensitive to the choice of associated control parameters, a large number of strategies on parameter determination have been presented in the past several years. However, most of them have limitations. Thus, to get optimal performance, time-consuming parameter tuning is necessary. This paper introduces an extension variable dimension of DE (EVSDE). In EVSDE, the control parameters are considered as a variable of the component. The variable dimension is expended and a new mutation strategy is employed for the extension dimension of variables on mutation operation. On the basis of experience value, the control parameters follow the individual variable and implement the dynamic self-adaptive process in the evolutionary process. It thus helps to improve the robustness of the algorithm and avoid premature convergence. Simulation results show the EVSDE is better than or at least comparable to other classic and adaptive DE algorithms from the literature in terms of convergence performance for a set of 10 benchmark problems.

关键词： differential evolution algorithm Adaptive parameter control Extension variable dimension

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Modified lightning search algorithm for optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 105卷 104419-104419页

作者： Panigrahy, Damodar Samal, Padarbinda SRM Inst Sci & Technol Coll Engn & Technol Dept Elect & Commun Engn Chennai 603202 Tamil Nadu India KIIT Deemed Univ Sch Elect Engn Bhubaneswar India

This manuscript proposes a new metaheuristic optimization algorithm for solving the problems related to constraint optimization. The proposed algorithm is based on a nature-inspired algorithm like a lightning search algorithm and differential evolution algorithm. The proposed algorithm consists of two stages. In the first/exploitation/local stage, the fitness values are calculated by using a lightning search algorithm, the inclusion of weight factor in the lightning search algorithm, and differential evolution algorithm using the same population. In the second/global/exploration stage, the minimum value of fitness for each row population by the implementation of the above three algorithms is updated with the population. The first and second stage continues until certain criteria are achieved. The final minimum value is the optimized result. Validation of the proposed methodology is performed by using 60 benchmark functions. The proposed methodology is compared with seven other well-known algorithms namely the lightning search algorithm, differential evolution algorithm cuckoo search algorithm, particle swarm optimization algorithm, seagull optimization algorithm, chimp optimization algorithm, and mayfly algorithm by computing mean, standard deviation, best and worst value for 60 benchmark functions. The proposed methodology is also validated by implementing it in seven real-time constraint optimized problems. Furthermore, the efficiency of the proposed technique has been validated statistically for unimodal and multimodal test functions. The results show a better performance of the proposed optimized algorithm compared to other algorithms.

关键词： Benchmark test function differential evolution algorithm Engineering design problems Lightning search algorithm Optimization

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Dynamic Scaling Factor Based differential evolution algorithm 1

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International Conference on Soft Computing for Problem Solving (SocProS 2011)

作者： Sharma, Harish Bansal, Jagdish Chand Arya, K. V. ABV Indian Inst Informat Technol & Management Gwalior India

ISBN: (数字)9788132204879

ISBN: (纸本)9788132204862

differential evolution (DE) is a well known and simple population based probabilistic approach used to solve nonlinear and complex problems. It has reportedly outperformed a few evolutionary algorithms when tested over both benchmark and real world problems. DE, like other probabilistic optimization algorithms, has inherent drawback of premature convergence and stagnation. Therefore, in order to find a trade-off between exploration and exploitation capability of DE algorithm, scaling factor in mutation process is modified. In mutation process, trial vector is calculated by perturbing the target vector. In this paper, a dynamic scale factor is proposed which controls the perturbation rate in mutation process. The proposed strategy is named as Dynamic Scaling Factor based differential evolution algorithm (DSFDE). To prove efficiency of DSFDE, it is tested over 10 benchmark problems.

关键词： Optimization differential evolution algorithm Scale Factor Mutation Process Meta-heuristics

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Improved differential evolution algorithm for Structural Optimization Design of Flumes with Hybrid Discrete Variables

Improved Differential Evolution Algorithm for Structural Opt...

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International Conference on Manufacturing Science and Technology (ICMST 2011)

作者： Zhou Juan Zhang Duoxin Liu Xianliang North China Univ Water Conversancy & Hydroelect Zhengzhou 450011 Peoples R China Yellow River Conservancy Tech Inst Kaifeng 475003 Peoples R China

ISBN: (纸本)9783037852958

The traditional method applying to solve continuous variable optimization problems is not suit for flume structural optimization design with hybrid discrete variable. According to the mathematical model of structural optimum design of the prestressed U-shell flumes, differential evolution (DE) algorithm was introduced to flume structural optimization design. In order to improve the population's diversity and the ability of escaping from the local optimum, a self-adapting crossover probability factor was presented. Furthermore, a chaotic sequence based on logistic map was employed to self-adaptively adjust mutation factor based on linear crossover, which can improve the convergence of DE algorithm. Dynamic penalty function, to transform the constrained problem to unconstrained one, was employed. The result shows that, compared with the original design scheme, the optimization design scheme can greatly reduce the amount of prestressed reinforcement. The construction cost of both the flume and the whole project can be reduced accordingly.

关键词： flume structural optimization design differential evolution algorithm linear crossover probability self-daptive mutation factor dynamic penalty function hybrid discrete variable

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Influence of atmospheric stability on wind farm layout optimization based on an improved Gaussian wake model

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JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS 2021年 211卷 104548-104548页

作者： Guo, Naizhi Zhang, Mingming Li, Bo Cheng, Yu Chinese Acad Sci Key Lab Wind Energy Utilizat Beijing 100190 Peoples R China Chinese Acad Sci Inst Engn Thermophys Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Dalian Natl Lab Clean Energy Dalian 116023 Peoples R China Huaneng Clean Energy Res Inst Beijing 102209 Peoples R China

Atmospheric stability, which has received widespread attention in recent years, has a significant impact on the performance of wind farms. However, majority of traditional wind farm layout optimization (WFLO) studies assume that atmospheric stability is always neutral. Hence, to consider the influence of atmospheric stability, we developed a novel WFLO framework based on an improved Gaussian wake model. In this framework, local atmospheric stability was used as an important input parameter, and the calculation method of wind power generation was modified accordingly. The proposed method was applied to three ideal wind farms and one real wind farm (Horns Rev 1 wind farm) for layout optimization. The results illustrated that atmospheric stability had a critical influence on WFLO results. As the atmosphere became more stable, the number of wind turbines with low power generation levels in the optimized layout increased gradually, resulting in a decline in the overall power generation. Moreover, the layout optimized under traditional neutral assumption was not suitable for the actual atmospheric environment. However, correct consideration of local atmospheric stability distribution in WFLO will result in a layout with a higher power output. In the real-world scenario, the developed WFLO framework can comprehensively consider local wind resource conditions to obtain a more efficient and reliable optimized layout scheme.

关键词： Atmospheric stability Wind farm layout optimization Analytical wake model Wind power generation differential evolution algorithm

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Parameter identification of photovoltaic cell model based on improved grasshopper optimization algorithm

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OPTIK 2021年 247卷 167979-167979页

作者： Wu, Zhongqiang Shen, Dandan Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao Hebei Peoples R China

An accurate mathematical model has practical applications in the design and research of solar cells. Many intelligent optimization algorithms are currently used to extract solar cell parameters. However, optimization algorithms including grasshopper optimization algorithm (GOA) are easy to fall into local optimums. An improved grasshopper optimization algorithm (IGOA) was proposed in this paper. Chaotic initialization was carried out to improve the quality of initial population. Then differential evolution strategy was introduced to maintain the diversity of the population through mutation, crossover and selection process, leading to getting rid of local optimums and searching for better solutions in GOA. To accelerate the convergence speed of the algorithm, the positions of particles were updated with current particle optimal values as targets as in particle swarm optimization. The optimization experiments on standard test functions shown the superiority of IGOA compared with other intelligent optimization algorithms. IGOA was then used to identify the parameters of polycrystalline silicon solar cells. The identification accuracy and stability of IGOA are much higher than harris hawks optimization, grey wolf optimization and ant lion optimization. The effectiveness of IGOA in identifying parameters of solar cell under different illuminations was also verified by experiments.

关键词： Photovoltaic cell Parameter identification Improved grasshopper optimization algorithm differential evolution algorithm Chaotic initialization

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An Improved Multi-objective differential evolution algorithm

An Improved Multi-objective Differential Evolution Algorithm

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24th Chinese Control and Decision Conference (CCDC)

作者： Niu, Dapeng Wang, Fuli Chang, Yuqing He, Dakuo Gu, Dehao Northeastern Univ Sch Informat Sci & Engn Shenyang Liaoning Provin Peoples R China Northeastern Univ State Key Lab Synthet Automat Process Ind Shenyang Liaoning Provin Peoples R China Northeastern Univ State Key Lab Rolling & Automat Shenyang Liaoning Provin Peoples R China

ISBN: (纸本)9781457720727

Based on multi-objective differential evolution algorithm, adaptive chaotic multi-objective differential evolution algorithm(AC-DEMO) is proposed, combining with adaptive and chaotic principles. In AC-DEMO, chaotic initialization and adaptive mutation operator are introduced to improve the efficiency of the algorithm. Numerical experiment results of commonly used test functions show that the algorithm has a good approximation and uniformity index and is suitable to solve complex multi-objective optimization problems.

关键词： Multi-objective differential evolution algorithm chaotic initialization adaptive mutation approximation and uniformity

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Application of Ideal Bode Transfer Function Tuning Fractional Order PID in Pressure Difference of Vertical Mill 32

Application of Ideal Bode Transfer Function Tuning Fractiona...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Zhang, Lei Zhang, Qiang Wang, Wei Univ Jinan Sch Elect Engn Jinan 250022 Shandong Peoples R China Linyi Zhonglian Cement Co LTD Tancheng Branch Dept Elect Linyi 276100 Shandong Peoples R China

ISBN: (纸本)9781728158556

Summing the grinding process of cement vertical mill, this paper analyzes the technology of this process, points out the key controlled variable in the process - the vertical mill pressure difference, and establishes the model. In this paper, the vertical mill pressure difference is controlled by fractional order PID controller, which is based on the parameter optimization method with ideal bode transfer function as the reference model, because of its insensitivity to gain variation. As the integral order lambda and differential mu are also adjustable, parameter optimization is more complex,a modified differential evolution algorithm is adopted to speed up the optimization. The simulation results show that the control performance of fractional order PID controller is better than that of integer order controller, and the optimization speed of modified differential evolution algorithm is also improved.

关键词： vertical roller mill Bodes ideal transfer function fractional order PID differential evolution algorithm

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Integrated Optimization of differential evolution with Grasshopper Optimization algorithm

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JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE 2018年第3期5卷 165-168页

作者： Jitkongchuen, Duangjai Ampant, Udomlux Dhurakij Pundit Univ Coll Innovat Technol & Engn Bangkok Thailand

This paper proposes a scheme to improve the differential evolution (DE) algorithm performance with integrated the grasshopper optimization algorithm (GOA). The grasshopper optimization algorithm mimics the behavior of grasshopper. The characteristic of grasshoppers is slow movement in the larval stage but sudden movement in the adulthood which seem as exploration and exploitation. The grasshopper optimization algorithm concept is added to DE to guide the search process for potential solutions. The efficiency of the DE/GOA is validated by testing on unimodal and multimodal benchmarks optimization problems. The results prove that the DE/GOA algorithm is competitive compared to the other meta-heuristic algorithms. (C) 2018 The Authors. Published by Atlantis Press SARL. This is an open access article under the CC BY-NC license.

关键词： Meta-heuristic differential evolution algorithm grasshopper optimization algorithm optimization

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