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Optimization of weighted myriad filters with differential evolution algorithm

AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2017年 77卷 1-9页

作者： Zorlu, Hasan Erciyes Univ Dept Elect Elect Engn TR-38039 Kayseri Turkey

Derivative-based algorithms, called classical algorithms, for optimization of weighted myriad (WMy) filters which are robust nonlinear filters in impulsive noisy environments are presented in literature. In this study, (WMy) filters that have various window lengths are optimized with differential evolution algorithm (DEA) that is known as a derivative-free based and evolutionary algorithm. The main contribution of this paper is to propose a novel strategy that is based on using the DEA in optimization of the parameters of nonlinear WMy filters. The results of proposed optimization studies are compared with a classical algorithm presented in literature. According to the simulation results;DEA has better performance than classical algorithm in optimization studies and also WMy filters optimized with DEA have ability of effectively suppressing the impulsive noisy environments. (C) 2017 Elsevier GmbH. All rights reserved.

关键词： Optimization Nonlinear filtering Weighted myriad filter differential evolution algorithm alpha-Stable noise

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Prediction of blast-induced flyrock using differential evolution algorithm

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ENGINEERING WITH COMPUTERS 2017年第1期33卷 149-158页

作者： Dehghani, Hesam Shafaghi, Mehdi Hamedan Univ Technol Hamadan Hamedan Iran

Flyrock is an undesirable phenomenon in blasting operations. Due to high potential to cause damage to machinery and nearby structures and to cause injuries, even fatal, to personnel, flyrock is the most dangerous adverse effect of blasting. For controlling and decreasing the effect of this phenomenon, it is necessary to predict it. Because of multiplicity of effective parameters and complexity of interactions among these parameters, empirical methods may not be fully appropriate for flyrock estimation. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of differential evaluation algorithm (DE) and dimensional analysis algorithm (DA). For this purpose, the parameters of 300 blasting operations were accurately recorded and flyrock distances were measured for each operation. In the next stage, two new empirical predictors were developed to predict flyrock distance. The results clearly showed the superiority of the proposed DE-DA model in comparison with the empirical approaches.

关键词： differential evolution algorithm Dimensional analysis Blasting Flyrock Soungun Copper Mine

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The classic differential evolution algorithm and its convergence properties

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APPLICATIONS OF MATHEMATICS 2017年第2期62卷 197-208页

作者： Knobloch, Roman Mlynek, Jaroslav Srb, Radek Tech Univ Liberec Studentska 2 Liberec 46117 Czech Republic

differential evolution algorithms represent an up to date and efficient way of solving complicated optimization tasks. In this article we concentrate on the ability of the differential evolution algorithms to attain the global minimum of the cost function. We demonstrate that although often declared as a global optimizer the classic differential evolution algorithm does not in general guarantee the convergence to the global minimum. To improve this weakness we design a simple modification of the classic differential evolution algorithm. This modification limits the possible premature convergence to local minima and ensures the asymptotic global convergence. We also introduce concepts that are necessary for the subsequent proof of the asymptotic global convergence of the modified algorithm. We test the classic and modified algorithm by numerical experiments and compare the efficiency of finding the global minimum for both algorithms. The tests confirm that the modified algorithm is significantly more efficient with respect to the global convergence than the classic algorithm.

关键词： optimization cost function global minimum global convergence local convergence differential evolution algorithm optimal solution set convergence in probability numerical testing

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The energy demand estimation for Turkey using differential evolution algorithm

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2017年第10期42卷 1705-1715页

作者： Beskirli, Mehmet Hakli, Huseyin Kodaz, Halife Selcuk Univ Dept Comp Engn TR-42075 Konya Turkey

The energy demand estimation commands great importance for both developing and developed countries in terms of the economy and country resources. In this study, the differential evolution algorithm ( DE) was used to forecast the long-term energy demand in Turkey. In addition to being employed for solving regular optimization problems, DE is also a global, meta-heuristic algorithm that enables fast, reliable and operative stochastic searches based on population. Considering the correlation between the increase in certain economic indicators in Turkey and the increase of energy consumption, two equations were used-one applying the linear form and the other the quadratic form. Turkey's long-term energy demand from 2012 to 2031 was estimated through the DE method in three different scenarios and in terms of the gross domestic product, import, export and population. To prove the success of the DE method in addressing the energy demand problem, the DE method was compared to other methods found in the literature. Results showed that the proposed DE method was more successful than the other methods. Furthermore, the future projections of energy demand obtained using the proposed method were compared to the indicators of energy demand estimated and observed by the Ministry of Energy and Natural Resources.

关键词： differential evolution algorithm optimization energy demand estimation Turkey

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Modified differential evolution algorithm for contrast and brightness enhancement of satellite images

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APPLIED SOFT COMPUTING 2017年 61卷 622-641页

作者： Suresh, Shilpa Lal, Shyam Natl Inst Technol Karnataka Dept Elect & Commun Engn Surathkal 575025 Mangaluru India

Satellite images normally possess relatively narrow brightness value ranges necessitating the requirement for contrast stretching, preserving the relevant details before further image analysis. Image enhancement algorithms focus on improving the human image perception. More specifically, contrast and brightness enhancement is considered as a key processing step prior to any further image analysis like segmentation, feature extraction, etc. Metaheuristic optimization algorithms are used effectively for the past few decades, for solving such complex image processing problems. In this paper, a modified differential Modified differential evolution (MDE) algorithm for contrast and brightness enhancement of satellite images is proposed. The proposed algorithm is developed with exploration phase by differential evolution algorithm and exploitation phase by cuckoo search algorithm. The proposed algorithm is used to maximize a defined fitness function so as to enhance the entropy, standard deviation and edge details of an image by adjusting a set of parameters to remodel a global transformation function subjective to each of the image being processed. The performance of the proposed algorithm is compared with ten recent state-of-the-art enhancement algorithms. Experimental results demonstrate the efficiency and robustness of the proposed algorithm in enhancing satellite images and natural scenes effectively. Objective evaluation of the compared methods was done using several full-reference and no reference performance metrics. Qualitative and quantitative evaluation results proves that the proposed MDE algorithm outperforms others to a greater extend. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Image enhancement Metaheuristics differential evolution algorithm Cuckoo search algorithm Satellite images

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Improving sampling-based image matting with cooperative coevolution differential evolution algorithm

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SOFT COMPUTING 2017年第15期21卷 4417-4430页

作者： Cai, Zhao-Quan Lv, Liang Huang, Han Hu, Hui Liang, Yi-Hui Coll Huizhou Huizhou 516007 Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Guangdong Peoples R China

Image matting is a fundamental operator in image editing and has significant influence on video production. This paper explores sampling-based image matting technology, with the aim to improve the accuracy of matting result. The result of sampling-based image matting technology is determined by the selected samples. Every undetermined pixel needs both a foreground and background pixel to estimate whether the undetermined one is in the foreground region of the image. These foreground pixels and background pixels are sampled from known regions, which form sample pairs. High-quality sample pairs can improve the accuracy of matting results. Therefore, how to search for the best sample pairs for all undetermined pixels is a key optimization problem of sampling-based image matting technology, termed "sample optimization problem." In this paper, in order to improve the efficiency of searching for high-quality sample pairs, we propose a cooperative coevolution differential evolution (DE) algorithm in solution to this optimization problem. Strong-correlate pixels are divided into a group to cooperatively search for the best sample pairs. In order to avoid premature convergence of DE algorithm, a scattered strategy is used to keep the diversit) of population. Besides. a simple but effective evaluation function is proposed to distinguish the quality of various candidate solutions. The existing optimization method, original DE algorithm and a popular evolution algorithm are used for comparison. The experimental results demonstrate that the proposed cooperative coevolution DE algorithm can search for higher-quality sample pairs and improve the accuracy of sampling-based image matting.

关键词： Image matting Sample selection Cooperative coevolution differential evolution algorithm Diversity of population

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A small-population based parallel differential evolution algorithm for short-term hydrothermal scheduling problem considering power flow constraints

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ENERGY 2017年 123卷 538-554页

作者： Zhang, Jingrui Lin, Shuang Liu, Houde Chen, Yalin Zhu, Mingcheng Xu, Yinliang Xiamen Univ Sch Aerosp Engn Dept Instrumental & Elect Engn Xiamen 361005 Peoples R China State Grid Corp China Fujian Elect Power Co LTD Elect Power Res Inst Fuzhou 350007 Peoples R China Tsinghua Univ Grad Sch Shenzhen Shenzhen Engn Lab Geometry Measurement Technol Shenzhen 518055 Peoples R China Sun Yat Sen Univ SYSU CMU Joint Inst Engn Sch Elect & Informat Technol Guangzhou 510275 Guangdong Peoples R China

Short-term optimal hydrothermal scheduling plays one of the most important roles in the modern power system plan and operation. The problem aims at minimizing the total fuel cost of the thermal units while satisfying various constraints such as power balance, water balance and other constraints on thermal units as well as hydro units. Except for the above various constraints, transmission network topology and valve point effects are also introduced into the mathematical optimizing model of the short-term hydrothermal scheduling (STHS) problem. Then a small-population based parallel differential evolution approach is proposed to solve the STHS problem considering power flow constraints. In the proposed approach, a large population is divided into several subpopulations each with a small population size and several parallel running processes of one or more CPUs are performed synchronously each evolving a certain subpopulation and searching for the optimal solution independently. Two different methods are employed in the proposed parallel DE approach in order to avoid low diversity of the small population in each process. One is implemented through the small population itself and the other through the communication mechanism among different running processes. Four constraint handling rules as well as a lead operation are proposed to enhance the feasibility of solutions. Numerical results for two well-known sample test systems are presented to demonstrate the capabilities of the proposed parallel DE algorithm to generate optimal solutions of STHS problem. Two other test systems with transmission networks of standard IEEE 9-bus and IEEE 39-bus are also employed to test the effectiveness of the proposed parallel DE algorithm. The results demonstrate the superiority of the proposed algorithm. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Hydrothermal scheduling differential evolution algorithm Parallel Small population Power flow

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Enhanced differential evolution algorithm for coordination of directional overcurrent relays

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ELECTRIC POWER SYSTEMS RESEARCH 2017年 143卷 365-375页

作者： Yen Shih, Meng Conde Enriquez, Arturo Hsiao, Tsun-Yu Torres Trevino, Luis Martin Univ Quintana Roo Div Sci & Engn Quintana Roo Mexico Autonomous Univ Nuevo Leon Fac Mech & Elect Engn Nuevo Leon Mexico Taiwan Power Co Dept Syst Operat Taipei Taiwan

This paper presents an enhanced differential evolution algorithm (DE) for solving the directional overcurrent relay (DOCR) coordination problem. Comparisons among different versions of DE for coordination of DOCRs in larger and interconnected power systems are presented. The evaluation criteria consist of fitness value, number of violation and standard deviation. The outstanding DE algorithm has been enhanced in four aspects: dial reduction scheme, elitism and mediocrity scheme, mutation index renew scheme, and population reduction scheme. The enhanced-DE (eDE) shown best result in terms of execution time, result quality, robustness and convergence ability compared to the others analyzed in this research. The IEEE 14 and 57 bus systems are used to validate the proposed algorithm. A significant comparison result between eDE and DE on the IEEE 57 bus network are: execution time in seconds 364 vs. 2040, fitness value 1.933 vs. 7.91, and standard deviation of fitness value 0.018 vs. 5.52. Results are averaged values in 50 runs. (C) 2016 Elsevier B.V. All rights reserved.

关键词： differential evolution algorithm Digital relays Directional overcurrent relay coordination Power system protection

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Precise Estimation of Cutting Force Coefficients and Cutter Runout in Milling using differential evolution algorithm

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Procedia CIRP 2018年 77卷 283-286页

作者： Ding Chen Xiaojian Zhang Yakun Xie Han Ding State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

An optimization method for calibrating cutting force coefficients and cutter runout parameters is developed in this study. The optimization technique works by minimizing the difference between the measured force and predicted force for all force components. Formulation of the instantaneous uncut chip thickness demonstrates that the total measured cutting forces can be separated into a nominal component independent of cutter runout and a perturbation component induced by cutter runout. Based on the differential evolution algorithm, the cutting force coefficients are identified via the nominal components of cutting forces, then the cutter runout parameters are determined from the perturbation components of cutting forces. The experimental results indicate that, compared with the linear-edge force model, the cutting forces predicted by the proposed method agree better with the measured forces both in magnitude and in waveform distribution.

关键词： differential evolution algorithm Cutting force coefficients Cutter runout End milling

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Multi-objective Task Scheduling Optimization in Cloud Computing based on Genetic algorithm and differential evolution algorithm

Multi-objective Task Scheduling Optimization in Cloud Comput...

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第37届中国控制会议

作者： Yuqing Li Shichuan Wang Xin Hong Yongzhi Li School of Automation China University of Geosciences

Reasonable task scheduling is a long-standing challenge in cloud computing. Scheduling process of cloud computing has the characteristics of dynamic nature, meanwhile the constraint of the target function from a single aspect cannot meet the needs of users. According to the above problem, a multi-objective task scheduling GA-DE algorithm based on Genetic algorithm（GA） and differential evolution（DE） is proposed in this paper, in which total time, cost and virtual machine load balancing three aspects are taken into account simultaneously. In the phase of population initialization and crossover, diversity of the initial population is increased by introducing individual different factors, which can prevent cross-operation of similar individuals and satisfy laws of inbreeding of natural relatives. The introduction of the DE in the GA mutation stage can not only give full play to the advantages of the global search ability of GA but also accelerate the algorithm produce optimal solution by utilizing advantage of local search ability and fast convergence speed of DE. The algorithm proposed in this paper is compared with GA and DE by cloud computing simulation experiments on Cloud Sim platform. Experimental result shows that this algorithm can optimize both GA and DE in terms of quality of service and virtual machine load balancing under the same conditions, which is proved to be an efficient task scheduling algorithm in cloud computing environment.

关键词： cloud computing task scheduling multi-objective Genetic algorithm differential evolution algorithm

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