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KNOT PLACEMENT FOR B-SPLINE CURVE APPROXIMATION VIA l_(∞,1)-NORM AND differential evolution algorithm

Journal of Computational Mathematics 2022年第4期40卷 589-606页

作者： Jiaqi Luo Hongmei Kang Zhouwang Yang School of Mathematical Sciences Soochow UniversitySuzhou 215008China School of Mathematical Sciences University of Science and Technology of ChinaHefei 230026China

In this paper,we consider the knot placement problem in B-spline curve approximation.A novel two-stage framework is proposed for addressing this *** the first step,the l_(∞,1)-norm model is introduced for the sparse selection of candidate knots from an initial knot *** this step,the knot number is *** the second step,knot positions are formulated into a nonlinear optimization problem and optimized by a global optimization algorithm—the differential evolution algorithm(DE).The candidate knots selected in the first step are served for initial values of the DE *** the candidate knots provide a good guess of knot positions,the DE algorithm can quickly *** advantage of the proposed algorithm is that the knot number and knot positions are determined *** with the current existing algorithms,the proposed algorithm finds approximations with smaller fitting error when the knot number is fixed in ***,the proposed algorithm is robust to noisy data and can handle with few data *** illustrate with some examples and applications.

关键词： B-spline curve approximation Knot placement l_(∞,1)-norm differential evolution algorithm

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Eigen-Structure Assignment-Based differential evolution algorithm for T-S Fuzzy Control Tuning Applied to Water-Turbine Governing System

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IEEE ACCESS 2021年 9卷 39322-39332页

作者： Zou, Yidong Qian, Jing Zeng, Yun Guo, Yaqing Wu, Mengyun Wang, Fangfang Mei, Hong Kunming Univ Sci & Technol Fac Met & Energy Engn Kunming 650000 Yunnan Peoples R China Kunming Univ Sci & Technol City Coll Kunming 650093 Yunnan Peoples R China

In this paper, a novel Takagi-Sugeno (T-S) fuzzy controller based on eigenstructure assignment (EA) and optimized by differential evolution algorithm (DE) is proposed, and the application of this control strategy in the hydro-turbine governing system (HTGS) is studied. Based on the non-linear model of HTGS, the corresponding state-space equations (SSE) are obtained by linearization through multiple equilibrium points. Combining with the principle of T-S fuzzy control, a T-S fuzzy model of HTGS integrating multiple SSE with generator power angle as a prerequisite is established. This paper adopts the EA method to design the fitness function in the optimization process and uses DE to complete the optimization operation to improve the performance of T-S fuzzy control in HTGS. Which makes each Linear-Quadratic-Regulator (LQR) controller gain in the fuzzy control reach the optimal under the constraint conditions. The simulation results show that compared with the standard fitness functions IAE (integral absolute error), ITAE (integral time absolute error) and ISE (integral square error), the fitness function designed using the EA method can expand the angle between the left and right eigenvectors of the T-S closed-loop system, and push the closed-loop pole from the imaginary axis to the left. That will make the adjustment time of the system shorter and the robustness against external interferences enhanced. The results also show that the proposed control strategy has better dynamic performance during the non-linear motion of HTGS and is superior to the existing control strategy.

关键词： Licenses Takagi-Sugeno model Mathematical model Fuzzy control Tuning Optimization Indexes Hydro-turbine governing system fuzzy control linear-quadratic-regulator differential evolution algorithm controller optimization

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Environmental/economic power dispatch problem using multi-objective differential evolution algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2010年第9期80卷 1171-1181页

作者： Wu, L. H. Wang, Y. N. Yuan, X. F. Zhou, S. W. Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Hunan Univ Sci & Technol Coll Informat & Elect Engn Xiangtan 411201 Hunan Peoples R China

This paper presents a multi-objective differential evolution (MODE) algorithm for environmental/economic power dispatch (EED) problem. The EED problem is formulated as a nonlinear constrained multi-objective problem with competing and non-commensurable objectives of fuel cost, emission and system loss. The proposed MODE approach adopts an external elitist archive to retain non-dominated solutions found during the evolutionary process. In order to preserve the diversity of Pareto optimality, a crowding entropy diversity measure tactic is proposed. The crowding entropy strategy is able to measure the crowding degree of the solutions more accurately. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed approach have been carried out on the IEEE 30- and 118-bus test system. The results demonstrate the capability of the proposed MODE approach to generate well-distributed Pareto optimal non-dominated solutions of multi-objective EED problem. The comparison with reported results of other MOEAs reveals the superiority of the proposed MODE approach and confirms its potential for solving other power systems multi-objective optimization problems. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Environmental/economic power dispatch evolutionary algorithms differential evolution algorithm Multi-objective optimization

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An optimized service broker routing policy based on differential evolution algorithm in fog/cloud environment

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第1期22卷 1639-1653页

作者： Manasrah, Ahmad M. Aldomi, Ala'a Gupta, B. B. Yarmouk Univ Network & Informat Secur Dept Irbid 21163 Jordan Yarmouk Univ Comp Sci Dept Irbid 21163 Jordan Natl Inst Technol Kurukshetra Kurukshetra Haryana India

A cloud service provider (CP) offers computing resources with their own interface type and pricing policies besides other services such as storage on a pay-per-use model. Client's requests should be processed in an appropriate CP datacenters in a trade-off relation between price and performance. The appropriate choice of a CP datacenters is the responsibility of the cloud-based service broker routing policy which acts as an intermediate between the users and the CP's datacenters. However, due to the distribution nature of the CP's datacenters, these datacenters can be overloaded with the increasing number of users and their requests being served at the same time if the datacenters are unwisely chosen. Therefore, choosing the appropriate datacenter is significant to the overall performance of the cloud computing systems. This paper aims to propose an optimized service broker routing policy based on different parameters that aims to achieve minimum processing time, minimum response time and minimum cost through employing a searching algorithm to search for the optimal solution from a possible solution space. A simulation-based deployment of the proposed algorithm along with a comparison study with other known algorithms form the field, confirms the ability of the proposed algorithm to minimize the load on service provider datacenters with minimum processing time, response time and overall cost.

关键词： Cloud computing Fog computing Datacenters selection Service broker policy differential evolution algorithm Optimization problems Simulation and modeling

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Optimization of heliostat field layout in solar central receiver systems on annual basis using differential evolution algorithm

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ENERGY CONVERSION AND MANAGEMENT 2015年 95卷 1-9页

作者： Atif, Maimoon Al-Sulaiman, Fahad A. King Fahd Univ Petr & Minerals Dept Mech Engn Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Ctr Res Excellence Renewable Energy CoRE RE Dhahran 31261 Saudi Arabia

Optimization of a heliostat field is an essential task to make a solar central receiver system effective because major optical losses are associated with the heliostat fields. In this study, a mathematical model was developed to effectively optimize the heliostat field on annual basis using differential evolution, which is an evolutionary algorithm. The heliostat field layout optimization is based on the calculation of five optical performance parameters: the mirror or the heliostat reflectivity, the cosine factor, the atmospheric attenuation factor, the shadowing and blocking factor, and the intercept factor. This model calculates all the aforementioned performance parameters at every stage of the optimization, until the best heliostat field layout based on annual performance is obtained. Two different approaches were undertaken to optimize the heliostat field layout: one with optimizing insolation weighted annual efficiency and the other with optimizing the un-weighted annual efficiency. Moreover, an alternate approach was also proposed to efficiently optimize the heliostat field in which the number of computational time steps was considerably reduced. It was observed that the daily averaged annual optical efficiency was calculated to be 0.5023 as compared to the monthly averaged annual optical efficiency, 0.5025. Moreover, the insolation weighted daily averaged annual efficiency of the heliostat field was 0.5634 for Dhahran, Saudi Arabia. The code developed can be used for any other selected location. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Annual optimization differential evolution algorithm Genetic algorithm Heliostat field layout system Solar power tower Solar central receiver system

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A self-adaptive binary differential evolution algorithm for large scale binary optimization problems

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INFORMATION SCIENCES 2016年 367卷 487-511页

作者： Banitalebi, Akbar Abd Aziz, Mohd Ismail Aziz, Zainal Abdul Univ Teknol Malaysia Ibnu Sina Inst Sci & Ind Res UTM Ctr Ind & Appl Math Utm Skudai 81310 Johor Malaysia

This study proposes a new self-adaptive binary variant of a differential evolution algorithm, based on measure of dissimilarity and named SabDE. It uses an adaptive mechanism for selecting how new trial solutions are generated, and a chaotic process for adapting parameter values. SabDE is compared against a number of existing state of the art algorithms, on a set of benchmark problems including high dimensional knapsack problems with up to 10,000 dimensions as well as on the 15 learning based problems of the Congress on evolutionary Computation (CEC 2015). Experimental results reveal that the proposed algorithm performs competitively and in some cases is superior to the existing algorithms. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Binary optimization Large-Scale global optimization Self-adaptation differential evolution algorithm Knapsack problem

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Parameter estimation for chaotic systems by hybrid differential evolution algorithm and artificial bee colony algorithm

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NONLINEAR DYNAMICS 2014年第1-2期77卷 61-71页

作者： Li, Xiangtao Yin, Minghao NE Normal Univ Sch Comp Sci & Informat Technol Changchun 130117 Peoples R China

This paper is concerned with the parameter estimation of nonlinear chaotic system, which could be essentially formulated as a multi-dimensional optimization problem. In this paper, a hybrid algorithm by combining differential evolution with artificial bee colony is implemented to solve parameter estimation for chaotic systems. Hybrid algorithm combines the exploration of differential evolution with the exploitation of the artificial bee colony effectively. Experiments have been conducted on Lorenz system and Chen system. The proposed algorithm is applied to estimate the parameters of two chaotic systems. Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to differential evolution, artificial bee colony, particle swarm optimization, and genetic algorithm from literature when considering the quality of the solutions obtained.

关键词： differential evolution algorithm Artificial bee colony Exploration Exploitation Chaotic system Parameter estimation

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differential evolution algorithm for Ship Path Planning in Open Waters

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JOURNAL OF MARINE SCIENCE AND TECHNOLOGY-TAIWAN 2021年第4期29卷 476-486页

作者： Kang, Yu-Tao Chen, Wei-Jiong Wang, Jin-Hui Shanghai Maritime Univ Coll Ocean Sci & Engn 1550 Hai Gang RdPudong New Area Shanghai Peoples R China

A basic problem in the design of a decision support system for ship collision avoidance is path optimization in a complex and dynamic navigational environment. This paper introduces a new path planning method based on the differential evolution (DE) algorithm to calculate a safe, optimal path for a ship. The algorithm was tested on a set of traffic scenarios typically encountered in open waters. The simulation test results prove the method's ability to solve a path planning problem for ships. We also discuss the optimality, consistency, and performance of the algorithm and provide a comparison of this algorithm with the particle swarm optimization (PSO) algorithm. The comparison results clearly show a significant advantage of the DE algorithm over the PSO algorithm in the areas of output optimality, algorithm consistency, and execution efficiency.

关键词： Collision avoidance Path planning differential evolution algorithm

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A hybrid differential evolution algorithm with estimation of distribution algorithm for reentrant hybrid flow shop scheduling problem

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NEURAL COMPUTING & APPLICATIONS 2018年第1期30卷 193-209页

作者： Zhou, Bing-hai Hu, Li-man Zhong, Zhen-yi Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China

This paper proposes a reentrant hybrid flow shop scheduling problem where inspection and repair operations are carried out as soon as a layer has completed fabrication. Firstly, a scheduling problem domain of reentrant hybrid flow shop is described, and then, a mathematical programming model is constructed with an objective of minimizing total weighted completion time. Then, a hybrid differential evolution (DE) algorithm with estimation of distribution algorithm using an ensemble model (eEDA), named DE-eEDA, is proposed to solve the problem. DE-eEDA incorporates the global statistical information collected from an ensemble probability model into DE. Finally, simulation experiments of different problem scales are carried out to analyze the proposed algorithm. Results indicate that the proposed algorithm can obtain satisfactory solutions within a short time.

关键词： Reentrant hybrid flow shop Scheduling differential evolution algorithm Estimation of distribution algorithm

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Optimization of a spectrum splitter using differential evolution algorithm for solar cell applications

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JOURNAL OF PHOTONICS FOR ENERGY 2015年第1期5卷

作者： Fan, Li Faryad, Muhammad Barber, Greg D. Mallouk, Thomas E. Monk, Peter B. Lakhtakia, Akhlesh Univ Delaware Dept Math Sci Newark DE 19716 USA Penn State Univ Dept Engn Sci & Mech University Pk PA 16802 USA Lahore Univ Management Sci Dept Phys DHA Lahore 54792 Pakistan Penn State Univ Penn State Inst Energy & Environm University Pk PA 16802 USA Penn State Univ Dept Chem University Pk PA 16802 USA

A spectrum splitter can be used to spatially multiplex different solar cells that have high efficiency in mutually exclusive parts of the solar spectrum. We investigated the use of a grating, comprising an array of dielectric cylinders embedded in a dielectric slab, for specularly transmitting one part of the solar spectrum while the other part is transmitted nonspecularly and the total reflectance is very low. A combination of (1) the rigorous coupled-wave approach for computing the reflection and transmission coefficients of the grating and (2) the differential evolution algorithm for optimizing the grating geometry and the refractive indices of dielectric materials was devised as a design tool. We used this tool to optimize two candidate gratings and obtained definite improvements to the initial guesses for the structural and constitutive parameters. Significant spectrum splitting can be achieved if the angle of incidence does not exceed 15 deg. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： solar spectrum splitting differential evolution algorithm optimization

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