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Adaptive variable space differential evolution algorithm based on population distribution

MEMETIC COMPUTING 2013年第1期5卷 49-64页

作者： Zhu, Jun Yan, Xuefeng E China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

A novel memetic computing optimization algorithms, i.e. an adaptive variable space differential evolution algorithm (AVSDE), is proposed to improve the global optimization performance. AVSDE guides most individuals search in adaptive variable space (AVS) and employs adaptive mutation and adaptive control parameter. In AVSDE, AVS is determined by population global distribution information, and DE's operators depend on the local information of the distance and direction. The performance of AVSDE is improved by integrating the global information with the local information. In addition, different mutation strategies are selected according to the evolution stage and random probability to balance AVSDE's exploration and exploitation abilities, and adaptive control parameter is used to further enhance the performance of AVSDE. 19 scalable benchmark functions are employed to demonstrate the performance of AVSDE. Comparing with two well-tuned conventional DE and several state-of-the-art parameter adaptive DE variants, the whole performance of AVSDE is the best. Finally, two experiments are conducted to analyze the effect of the key parameters on AVSDE's performance, and the optimal parameters are obtained.

关键词： Memetic computing algorithm differential evolution algorithm Adaptive variable space Adaptive control parameter Adaptive mutation

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Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm

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ENERGY 2018年 153卷 801-815页

作者： Wang, Lin Hu, Huanling Ai, Xue-Yi Liu, Hua Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China Wuhan Univ Sci & Technol Sch Management Wuhan 430081 Hubei Peoples R China

Electricity energy consumption (EEC) has great effect on the government to make reasonable energy policy and has attracted great attentions of the power generation groups with the liberalization of competition in the electricity industry. In fact, the EEC is easily affected by many factors, including the climate factor and the gross domestic product. So, the precise forecasting of electricity energy consumption is very challenging. This study aims to propose an effective and stable model named ESN-DE using an improved echo state network for forecasting electricity energy consumption. differential evolution algorithm is used to search optimal values of the three crucial parameters of echo state network. Two comparative examples and an extended example are used to validate the applicability and accuracy of the proposed ESN-DE. Errors of the comparative examples where mean absolute percentage errors of ESN-DE are 1.516% and 0.570% respectively indicate that the ESN-DE outperforms the traditional echo state network and the existing best model. Mean absolute percentage error of ESN-DE is 2.156% for Zhengzhou City's electricity energy consumption forecasting. The proposed ESN-DE is a potential candidate for effective forecasting of electricity energy consumption because of its easy implementation and stability. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Electricity energy consumption forecasting Echo state network differential evolution algorithm

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Fractional-order system identification based on an improved differential evolution algorithm

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ASIAN JOURNAL OF CONTROL 2022年第5期24卷 2617-2631页

作者： Yu, Wei Liang, HengHui Chen, Rui Wen, Chenglin Luo, Ying Foshan Univ Sch Mechatron Engn & Automat Foshan Peoples R China Guangdong Univ Petrochem Technol Sch Automat Maoming Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn 1037 Luoyu Rd Wuhan Hubei Peoples R China

With the continuous increasing of system modeling accuracy and control performance requirements, the fractional-order models are applied to describe the dynamic characteristics of the real systems more accurately over traditional integer-order models. Traditional identification algorithms for fractional-order system require stringent information on the derivatives of the objective function and initial values of the parameters, which is difficult in practical applications. The differential evolution (DE) algorithm has the advantage of simple implementation and superior performance for solving multi-dimensional complex optimization problems which is suitable for the identification of fractional-order systems. Aiming at improving the solution accuracy and convergence speed, an improved DE (IDE) algorithm for fractional-order system identification is proposed. The movement strategy of the marine predator algorithm (MPA) and the mutation strategy of randomly selecting one from the optimal individual population as the basis vector are both adopted in this proposed IDE algorithm. The parameter information of the successfully mutated individual is archived during the iteration process. The scaling factor and crossover probability factor are adaptively adjusted according to the archived information to enhance the best solution accuracy obtained in the search process. By testing 13 sets of unimodal and multimodal functions with high-performance optimization algorithms, the accuracy performance of the proposed IDE solution is verified. The IDE algorithm is applied to identify the parameters of the fractional-order system of permanent magnet synchronous motor with simulation and experiment. The identification results clearly show the effectiveness of the proposed methodology.

关键词： differential evolution algorithm fractional-order system identification adaptive adjustment permanent magnet synchronous motor

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An adaptive hybrid differential evolution algorithm for dynamic economic dispatch with valve-point effects

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第7期37卷 4842-4849页

作者： Lu, Youlin Zhou, Jianzhong Qin, Hui Li, Yinghai Zhang, Yongchuan Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China

The dynamic economic dispatch (DED) is a complicated nonlinear dynamic constrained problem which occupies an important role in the economic operation of power system. An adaptive hybrid differential evolution algorithm (AHDE) is proposed in this paper to solve DED problem. In the proposed algorithm, an adaptive dynamic parameter control mechanism is adopted to obtain parameter settings in differential evolution algorithm (DE). Moreover, a local random search (LRS) operator is integrated with DE to avoid premature convergence. In view of the difficulties of handling the complicated constraints of DED problem, a new heuristic constraints handling method which does not require penalty factor settings is presented. The feasibility and effectiveness of the proposed method is demonstrated for an application example. Compared with other algorithms, AHDE can find the global optimum dispatch result with a shorter computational time along with higher effectiveness and robustness. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Dynamic economic dispatch differential evolution algorithm Adaptive Local random search Constraint handle

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A two-stage assessment method using damage locating vector method and differential evolution algorithm for damage identification of cross-ply laminated composite beams

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ADVANCES IN STRUCTURAL ENGINEERING 2017年第12期20卷 1807-1827页

作者： Du Dinh-Cong Trung Vo-Duy Nhan Nguyen-Minh Vinh Ho-Huu Trung Nguyen-Thoi Ton Duc Thang Univ Inst Computat Sci Div Construct Computat Ho Chi Minh City Vietnam Ton Duc Thang Univ Inst Computat Sci Div Computat Math & Engn Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam

This article proposes a two-stage damage identification method to identify the location and extent of multiple damages at individual layers in laminated composite beams. In the first stage, the damage locating vector method using normalized cumulative energy is employed to detect the locations of suspected damaged elements. In the second stage, the degree of the suspected damaged elements is determined by solving an optimization problem via differential evolution algorithm. To identify damage at a specific layer of the laminated composite beams, the layerwise theory is applied to analysis behavior of the beams. The performance of the method is assessed through two numerical examples with different damage scenarios. In addition, the effect of noise on the damage identification results is investigated.

关键词： damage identification methods damage locating vector method differential evolution algorithm laminated composite beam layerwise theory

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Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm

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COMPUTERS & OPERATIONS RESEARCH 2013年第12期40卷 3045-3055页

作者： Tsai, Jinn-Tsong Fang, Jia-Cen Chou, Jyh-Horng Natl Pingtung Univ Educ Dept Comp Sci Pingtung 900 Taiwan Natl Kaohsiung First Univ Sci & Technol Inst Syst Informat & Control Kaohsiung 824 Taiwan Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung 807 Taiwan Kaohsiung Med Univ Dept Healthcare Adm & Med Informat Kaohsiung 807 Taiwan

Purpose: The objective of this study is to optimize task scheduling and resource allocation using an improved differential evolution algorithm (IDEA) based on the proposed cost and time models on cloud computing environment. Methods: The proposed IDEA combines the Taguchi method and a differential evolution algorithm (DEA). The DEA has a powerful global exploration capability on macro-space and uses fewer control parameters. The systematic reasoning ability of the Taguchi method is used to exploit the better individuals on microspace to be potential offspring. Therefore, the proposed IDEA is well enhanced and balanced on exploration and exploitation. The proposed cost model includes the processing and receiving cost. In addition, the time model incorporates receiving, processing, and waiting time. The multi-objective optimization approach, which is the non-dominated sorting technique, not with normalized singleobjective method, is applied to find the Pareto front of total cost and makespan. Results: In the five-task five-resource problem, the mean coverage ratios C(IDEA, DEA) of 0.368 and C (IDEA, NSGA-II) of 03 are superior to the ratios C(DEA, IDEA) of 0.249 and C(NSGA-II, IDEA) of 0.288, respectively. In the ten-task ten-resource problem, the mean coverage ratios C(IDEA, DEA) of 0.506 and C (IDEA, NSGA-II) of 0.701 are superior to the ratios C(DEA, IDEA) of 0.286 and C(NSGA-II, IDEA) of 0.052, respectively. Wilcoxon matched-pairs signed-rank test confirms there is a significant difference between IDEA and the other methods. In summary, the above experimental results confirm that the IDEA outperforms both the DEA and NSGA-II in finding the better Pareto-optimal solutions. Conclusions: In the study, the IDEA shows its effectiveness to optimize task scheduling and resource allocation compared with both the DEA and the NSGA-II. Moreover, for decision makers, the Gantt charts of task scheduling in terms of having smaller makespan, cost, and both can be selected to mak

关键词： Cloud computing differential evolution algorithm Task scheduling Cost and time models Multi-objective approach

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Optimal power flow using differential evolution algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2010年第7期80卷 878-885页

作者： Abou El Ela, A. A. Abido, M. A. Spea, S. R. Menoufiya Univ Fac Engn Dept Elect Engn Menoufia Egypt King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran Saudi Arabia

This paper presents an evolutionary-based approach to solve the optimal power flow (OPF) problem. The proposed approach employs differential evolution (DE) algorithm for optimal settings of OPF control variables. The proposed approach is examined and tested on the standard IEEE 30-bus test system with different objective functions that reflect fuel cost minimization, voltage profile improvement, and voltage stability enhancement. In addition, non-smooth piecewise quadratic cost function has been considered. The simulation results of the proposed approach are compared to those reported in the literature. The results demonstrate the potential of the proposed approach and show its effectiveness and robustness to solve the OPF problem for the systems considered. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Optimal power flow differential evolution algorithm Fuel cost minimization Voltage profile improvement Voltage stability enhancement

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Output-only structural damage identification using hybrid Jaya and differential evolution algorithm with reference-free correlation functions

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MEASUREMENT 2022年 199卷

作者： Zhang, Guangcai Wan, Chunfeng Xiong, Xiaobing Xie, Liyu Noori, Mohammad Xue, Songtao Southeast Univ Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing Peoples R China Tongji Univ Res Inst Struct Engn & Disaster Reduct Coll Civil Engn Shanghai Peoples R China Calif Polytech State Univ San Luis Obispo Dept Mech Engn San Luis Obispo CA USA Tohoku Inst Technol Dept Architecture Sendai Japan

To solve the optimization-based structural damage identification problem, a novel hybrid algorithm based on Jaya and differential evolution algorithm (HJDEA) is proposed to detect, locate and quantify structural damages by effectively incorporating the powerful local exploitation capacity of Jaya algorithm and global exploration capability of differential evolution. Meanwhile, Hammersley sequence initialization and Le acute accent vy flight search mechanism are introduced into HJDEA to further improve convergence rate and refining the quality of the best solution. Four different algorithms, genetic algorithm, particle swarm optimization, Jaya and the proposed HJDEA are employed for comparative study. In addition, the objective function is established by adjacent acceleration correlation function so as to avoid false identification caused by defining improper reference point. The performance of the proposed damage identification strategy based on HJDEA and adjacent acceleration correlation function is investigated with numerical examples involving an 8-DOF lumped mass model and a cantilever beam, as well as an experimental study of the ASCE benchmark structure under white noise excitation. Results show that the proposed hybrid identification method is accurate, efficient and robust in the identification of the damage existence, location and severity of stiffness and mass parameters even with partial output-only responses and 20% noise-polluted measurements.

关键词： Structural damage identification Jaya algorithm differential evolution algorithm Hybrid algorithm Adjacent acceleration correlation function Gradient search method

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Modified differential evolution algorithm for simple assembly line balancing with a limit on the number of machine types

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ENGINEERING OPTIMIZATION 2016年第2期48卷 253-271页

作者： Pitakaso, Rapeepan Sethanan, Kanchana Ubon Ratchathani Univ Dept Ind Engn Res Unit Syst Modelling Ind Ubon Ratchathani Thailand Khon Kaen Univ Fac Engn Res Unit Syst Modelling Ind Dept Ind Engn Khon Kaen Thailand

This article proposes the differential evolution algorithm (DE) and the modified differential evolution algorithm (DE-C) to solve a simple assembly line balancing problem type 1 (SALBP-1) and SALBP-1 when the maximum number of machine types in a workstation is considered (SALBP-1M). The proposed algorithms are tested and compared with existing effective heuristics using various sets of test instances found in the literature. The computational results show that the proposed heuristics is one of the best methods, compared with the other approaches.

关键词： simple assembly line balancing differential evolution algorithm binomial recombination exponential recombination

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A novel decomposition-denoising ANFIS model based on singular spectrum analysis and differential evolution algorithm for seasonal AQI forecasting

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第2期44卷 2325-2349页

作者： He, Mingjun Che, Jinxing Jiang, Zheyong Zhao, Weihua Wan, Bingrong Nanchang Inst Technol Sch Sci Nanchang 330099 Jiangxi Peoples R China Nanchang Inst Technol Jiangxi Prov Key Lab Water Informat Cooperat Sens Nanchang Jiangxi Peoples R China Nantong Univ Sch Sci Nantong Jiangsu Peoples R China

Understanding and forecasting air quality index (AQI) plays a vital role in guiding the reduction of air pollution and helping social sustainable development. By combining fuzzy logic with decomposition techniques, ANFIS has become an important means to analyze the data resources, uncertainty and fuzziness. However, fewstudies have paid attention to the noise of decomposed subseries. Therefore, this paper presents a novel decomposition-denoising ANFIS model named SSADD-DE-ANFIS (Singular Spectrum Analysis Decomposition and Denoising-differential evolution-Adaptive Neuro-Fuzzy Inference System). This method uses twice SSA to decompose and denoise the AQI series, respectively, then fed the subseries obtained after the decomposition and denoising into the constructed ANFIS for training and predicting, and the parameters of ANFIS are optimized using DE. To investigate the prediction performance of the proposed model, twelve models are included in the comparisons. The experimental results of four seasons show that: the RMSE of the proposed SSADD-DE-ANFIS model is 1.400628, 0.63844, 0.901987 and 0.634114, respectively, which is 19.38%, 21.27%, 20.43%, 21.27% and 87.36%, 88.12%, 88.97%, 88.71% lower than that of the single SSA decomposition and SSA denoising. Diebold-Mariano test is performed on all the prediction results, and the test results show that the proposed model has the best prediction performance.

关键词： Air quality index forecasting decomposition-denoising Adaptive Neuro-Fuzzy Inference System singular spectrum analysis differential evolution algorithm

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