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10th International Conference on Power Electronics Systems and Applications (PESA)

作者： Mo, Xin Han, Liguo Wu, Lilin Tang, Wentao Li, Jingming Liu, Junfeng Fan, Junhao Guangdong Power Grid Co Ltd Zhanjiang Power Supply Bur Zhanjiang Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou Peoples R China South China Univ Technol Sch Elect Power Engn Guangzhou Peoples R China

ISBN: (纸本)9798350365818;9798350365801

After distributed power generations are connected to the power grid, the original fault location algorithm is not applicable, and the intelligent algorithm may fall into the local optimal solution. To solve these problems, this paper adopt a differential evolution grey wolf optimizer (DE-GWO) to locate faults in active distribution networks. First, the algorithm introduces the tent chaotic mapping for the high quality of initial population generation. Next, the differential evolution (DE) process is used to generate a mutated population and select individuals with high fitness for subsequent calculation to improve the algorithm's global search. Finally, the proposed method is simulated on the IEEE33-bus active distribution network model. It is verified that the proposed DE-GWO algorithm is faster and more accurate than the original GWO algorithm under both single fault and multiple faults, which can achieve accurate positioning.

关键词： Active distribution network fault location grey wolf optimization algorithm differential evolution algorithm

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DV-Hop Localization algorithm Based on RSSI and differential evolution

DV-Hop Localization Algorithm Based on RSSI and Differential...

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9th International Conference on Signal and Image Processing (ICSIP)

作者： Ma, Qian-li Qian, Hui-meng Zhang, Qi Qi, Xin Shanxi Univ Sch Comp & Informat Technol Taiyuan Peoples R China

ISBN: (纸本)9798350350920

In order to address the issues of large cumulative errors and low positioning accuracy in the traditional DV-Hop localization algorithm, this paper proposes an DV-Hop Localization algorithm based on RSSI and differential evolution(RDE-Hop). The algorithm establishes a reliable anchor node set, ensuring the effectiveness of the anchor nodes involved in localization through geometric verification of their relationships. It introduces RSSI distance measurement technology to optimize the minimum hop count and increase candidate anchor nodes, reducing hop count errors and dynamically increasing the proportion of anchor nodes. Furthermore, it utilizes RSSI inverse distance weighting to correct hop distances, further reducing distance errors. In the phase of unknown node localization, an optimized differential evolution algorithm is employed to minimize localization errors. Simulation experiments demonstrate that, under the same network environment, compared with traditional DV-Hop, DEDV-Hop, and HDCDV-Hop algorithms, the RDE-Hop algorithm effectively reduces localization errors.

关键词： Wireless Sensor Network DV-Hop RSSI differential evolution algorithm Reliable Anchor Nodes

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Enhanced Dingo Optimization algorithm Based on differential evolution and Chaotic Mapping for Engineering Optimization 15th

Enhanced Dingo Optimization Algorithm Based on Differential ...

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15th International Conference on Advances in Swarm Intelligence (ICSI)

作者： Yu, Zijing Shao, Peng Zhang, Shaoping Jiangxi Agr Univ Sch Comp & Informat Engn Nanchang 330045 Jiangxi Peoples R China

ISBN: (纸本)9789819771806;9789819771813

Aiming at the problems of lower initial population diversity and insufficient global search ability of primitive Dingo Optimization algorithm (DOA), a dingo optimization algorithm based on differential evolution and chaotic mapping (DCDOA) is proposed. In DCDOA, differential evolution is introduced to randomly generate a new population to increase the diversity of the dingo population;Tent chaotic map can effectively faster the convergence rate and strengthen the global search ability. Taking CEC2019 as the test function set, they are performed by DCDOA and three other algorithms. Experiments show that DCDOA has superior with convergence performance and stronger robustness. Furthermore, to verify its performance in solving engineering optimization problems (pressure vessel and car side collisions design). The experimental results demonstrate that DCDOA conserves 49.85% and 4.62% in economic costs for pressure vessels and vehicle side collisions compared to DOA, respectively, verifying the practicality and superiority of DCDOA for engineering optimization problems.

关键词： Dingo optimization algorithm differential evolution algorithm Tent chaos mapping CEC2019 Engineering optimization

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Foreign exchange forecasting and portfolio optimization strategy based on hybrid-molecular differential evolution algorithms

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SOFT COMPUTING 2023年第7期27卷 3921-3939页

作者： Zhang, Xuecong Zhong, Chen Abualigah, Laith South China Univ Technol Sch Business Adm Guangzhou Guangdong Peoples R China Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ Fac Informat Technol Amman 11831 Jordan

At present, the COVID-19 epidemic is still spreading at home and abroad, and the foreign exchange market is highly volatile. From financial institutions to individual investors, foreign exchange asset allocation has become important contents worthy of attention. However, most intelligent optimization algorithms (hereinafter IOAS) adopt the existing data and ignore the forecasted one in the foreign exchange portfolio allocation, which will result in a huge difference between portfolio allocation and actual demand;at the same time, many IOAS are less adaptable and have lower optimization ability in portfolio problems. To solve the aforementioned problems, this paper first proposed a DETS based on hybrid tabu search and differential evolution algorithms (DEAs), which has excellent optimization ability. Subsequently, the DETS algorithm was applied to support vector machine (SVM) model. Experiments show that, compared with other algorithms, the MAE and RMSE obtained by using DETS optimization parameters are reduced by at least 3.79 and 1.47%, while the CTR is improved by at least 2.19%. Then combined with the DETS algorithm and Pareto sorting theory, an algorithm suitable for multi-objective optimization was further proposed, named NSDE-TS. Finally, by applying NSDE-TS algorithm, the optimal foreign exchange portfolio is acquired. The empirical analysis shows that the Pareto front obtained by this algorithm is better than that of NSGA-II. Since the lower the uniformity index and convergence index, the stronger the optimization performance of the corresponding algorithm, compared with NSGA-II, its uniformity and convergence index decreased by 15.7 and 39.6%.

关键词： differential evolution algorithm Tabu search algorithm Exchange rate forecast Foreign exchange portfolio Pareto principle

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A self-adaptive chaotic differential evolution algorithm using gamma distribution for unconstrained global optimization

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APPLIED MATHEMATICS AND COMPUTATION 2014年 234卷 452-459页

作者： Coelho, Leandro dos Santos Hultmann Ayala, Helon Vicente Mariani, Viviana Cocco Pontif Catholic Univ Parana PUCPR Ind & Syst Engn Grad Program PPGEPS BR-80215901 Curitiba Parana Brazil Univ Fed Parana UFPR Dept Elect Engn BR-81531980 Curitiba Parana Brazil Pontif Catholic Univ Parana PPGEM Mech Engn Grad Program BR-80215901 Curitiba Parana Brazil

evolutionary algorithms (EAs) have yielded promising results for solving nonlinear, non-differentiable and multi-modal optimization problems. Due to its population-based nature, EAs can avoid being trapped in a local optimum, and consequently have the ability to find global optimal solutions. As a novel evolutionary technique, differential evolution (DE) has received increasing attention and wide applications in a variety of fields. DE algorithm uses an efficient way of self-adapting mutation using small populations for function optimization over continuous spaces. The potentialities of DE are its simple structure, easy use, convergence property, quality of solution, and robustness. In this paper, an effective self-adaptive DE algorithm based on Gaussian probability distribution, gamma distribution and chaotic sequence (DEGC) for solving continuous global optimization problems is proposed. The proposed DEGC algorithm is tested on several benchmark functions from the usual literature. Numerical results comparisons with a classical DE approach and a self-adaptive DE approach demonstrate the effectiveness and efficiency of the proposed DEGC algorithm. (C) 2014 Elsevier Inc. All rights reserved.

关键词： evolutionary algorithms differential evolution algorithm Chaotic sequence Global optimization

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Identification of Mass Characteristic Parameters for Spacecraft Based on differential evolution algorithm

Identification of Mass Characteristic Parameters for Spacecr...

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2015 Fifth International Conference on Instrumentation and Measurement,Computer,Communication and Control (IMCCC2015)(第五届仪器测量、计算机通信与控制国际会议)

作者： Shunli Li Mingliang Suo Xinlong Chen School of Astronautics Harbin Institute of Technology Harbin Heilongjiang Province China Qian Xuesen Laboratory of Space Technology China Academy of Space Technology Beijing China

ISBN: (纸本)9781467377249

The accuracy of the spacecraft mass parameters is the important precondition for spacecraft precision attitude and orbit control, differential evolution (DE) algorithm is proposed for this to identify spacecraft mass characteristics. The object function of identification is obtained on the account of the spacecraft dynamics model, respectively using the flywheel and thruster as the acting force. Then, on-orbit spacecraft inertia parameters identification and one-time offline mass characteristic parameters identification are implemented by the DE method. The proposed method does not require the status of the spacecraft rigorously, needs less amount of sampling data, and has faster convergence speed and less time. Results of simulation demonstrate effectiveness of the proposed method.

关键词： spacecraft mass characteristics identification differential evolution algorithm

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Parameter Estimation by Using an Improved Bee Memory differential evolution algorithm (IBMDE) to Simulate Biochemical Pathways

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CURRENT BIOINFORMATICS 2014年第1期9卷 65-75页

作者： Chong, Chuii Khim Mohamad, Mohd Saberi Deris, Safaai Shamsir, Mohd Shahir Chai, Lian En Choon, Yee Wen Univ Teknol Malaysia Fac Comp Artificial Intelligence & Bioinformat Res Grp Skudai 81310 Johor Malaysia Univ Teknol Malaysia Fac Biosci & Med Engn Dept Biol Sci Skudai 81310 Johor Malaysia

Assessing and estimating essential parameters for a metabolic pathway by using a mathematical model is a significant step in Systems Biology. However, estimating process often faces numerous obstacles, for example when the number of unknown parameters escalates or data has noise, gets trapped in local minima and or having repeated exploration of poor solution during search process. Thus, this study proposes an improved Bee Memory differential evolution algorithm (IBMDE), which is a combination of the differential evolution algorithm (DE), the Kalman Filter (KF), the Artificial Bee Colony algorithm (ABC), and a memory feature to solve the aforementioned problems. The implemented metabolic pathways for this improved estimation algorithm were glycerol and pyruvate synthesis pathways. IBMDE was successful in generating the estimated optimal kinetic parameter values with noticeable reduction in errors (81.36% and 99.46% respectively) and faster convergence times (6.19% and 15.72% respectively) compared to DE, the Genetic algorithm (GA), the Nelder Mead (NM), and the Simulated Annealing (SA). The results indicated that, most importantly, the kinetic parameters produced by IBMDE had enhanced the production of desired metabolites than the other estimation algorithms. Besides that, the results also demonstrated the reliability of IBMDE as an estimation algorithm in terms of lower error.

关键词： Artificial bee colony algorithm differential evolution algorithm kalman filter memory feature parameter estimation

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Optimal tunning of type-2 fuzzy logic power system stabilizer based on differential evolution algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 62卷 19-28页

作者： Sun, Zhe Wang, Ning Srinivasan, Dipti Bi, Yunrui Zhejiang Univ Natl Lab Ind Control Technol Inst Cyber Syst & Control Hangzhou 310027 Zhejiang Peoples R China Southeast Univ Sch Automat Nanjing 210096 Jiangsu Peoples R China Natl Univ Singapore Dept Elect Comp Engn Singapore 117576 Singapore

In this paper, a type-2 fuzzy logic power system stabilizer with differential evolution algorithm is proposed. As an extension of type-1 fuzzy logic theory, type-2 fuzzy logic theory can effectively improve the control performance by uncertainty of membership function especially when we have to confront with less expert knowledge or unpredicted external disturbances. The corresponding parameters and rule base of type-2 fuzzy logic power system stabilizer are optimally tuned by using differential evolution algorithm for multi-machine power system. Through simulation under different operational conditions, the results demonstrate the effectiveness of the proposed approach for damping the power system electromechanical oscillations. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Power system stabilizer Type-2 fuzzy logic control differential evolution algorithm Parameter optimization

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Finite Markov chain analysis of classical differential evolution algorithm

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2014年 268卷 121-134页

作者： Hu, ZhongBo Xiong, ShengWu Su, QingHua Fang, ZhiXiang Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430070 Peoples R China Hubei Engn Univ Sch Math & Stat Xiaogan 432000 Hunan Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430072 Peoples R China

Theoretical analyses of algorithms are important to understand their search behaviors and develop more efficient algorithms. Compared with the plethora of works concerning the empirical study of the differential evolution (DE), little theoretical research has been done to investigate the convergence properties of DE so far. This paper focuses on theoretical researches on the convergence of DE and presents a convergent DE algorithm. First of all, it is proved that the classical DE cannot converge to the global optimal set with probability 1 by using the property that it cannot escape from a local optimal set. Inspired by the characteristics of the elitist genetic algorithm, this paper proposed a modified DE to overcome the disadvantage. The proposed algorithm employs two operators that assist it in escaping from a local optimal set and enhance the diversity of the population. And it is then verified that the proposed algorithm is capable of converging to global optima with probability 1. The theoretical research of this paper is undertaken in a finite discrete set, and the analysis tool used is the Markov chain. The numerical experiments are conducted on a deceptive function and a set of benchmark functions. The experimental results support the theoretical analyses on the convergence performances of the classical and modified DE algorithm. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Convergence in probability Continuous optimization differential evolution algorithm Markov chain

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Pattern Synthesis of Time-Modulated Conformal Arrays by an Improved differential evolution algorithm

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2014年第6期24卷 697-705页

作者： Yang, Jing Li, Wentao Shi, Xiaowei Xidian Univ Dept Elect Engn Sci & Technol Antenna & Microwave Lab Xian 710071 Peoples R China

In this article, an improved differential evolution algorithm (IDE) based on two different colonies is proposed and applied to time-modulated conformal arrays syntheses. The whole population of IDE is divided into two parts. The one part searches the solution globally while the other searches the neighborhood of the solution provided by the previous one. Benchmark functions are provided to testify IDE. Furthermore, IDE is applied to synthetize sum-difference patterns with a 1 x 16 elements time-modulated circular array and low sidelobe level (SLL) patterns with an 8 x 12 elements time-modulated cone array. After optimization, the sideband level (SBL) of the circular array at the first sideband frequency is -1.00 dB. The SLL and SBL at the first sideband frequency of the cone array are lower than -30.00 and -20.00 dB, respectively. Experiment results verify the superior performance of IDE. Moreover, to accelerate the computation speed, graphics processing unit parallel computing technique is introduced into pattern synthesis and the acceleration ratios of more than 23 times can be achieved. (c) 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:697-705, 2014.

关键词： array synthesis conformal arrays differential evolution algorithm GPU parallel computing time-modulated arrays

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