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differential evolution algorithm-based range image registration for free-form surface parts quality inspection

SWARM AND evolutionARY COMPUTATION 2017年 36卷 106-123页

作者： Li, Taifeng Pan, Quanke Gao, Liang Li, Peigen Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan Hubei Peoples R China

Increasing demands on precision manufacturing of complex free-form surface parts have been observed in the past several years. Although some advanced techniques have been employed to solve the design and machining problems for such parts, quality inspection remains a difficult problem. Registration is a crucial issue in surface inspection;it is used to transform the design model and measurement model into a common coordinate system. The comparison results are then outputted in a report and displayed visually by color gradients. This paper presents a design model-based inspection method with range image registration, in which the measurement model is represented by a series of 3D discrete points. In the model preprocessing, the directed Hausdorff distance (DHD) method is employed for point cloud simplification, and a novel point descriptor is designed to evaluate the property of each point. Subsequently, a differential evolution (DE) algorithm-based optimizer is proposed for error evaluation. Combined with the properties of 3D points, the optimizer can measure the similarity between the design model and the measurement model with a recursive process. The proposed algorithms have been implemented and tested with several sets of simulated and real data. The experiment results illustrate that they are effective and efficient for free-form surface part quality inspection.

关键词： Range image registration Quality inspection Point cloud preprocessing differential evolution algorithm

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differential evolution algorithm with self-adaptive strategy and control parameters for P-xylene oxidation process optimization

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SOFT COMPUTING 2015年第5期19卷 1363-1391页

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

Considering that the model of the p-Xylene (PX) oxidation reaction process is a hybrid and highly nonlinear model, a differential evolution algorithm with self-adaptive mutation strategy and control parameters (SSCPDE) was proposed to optimize the operating conditions. In SSCPDE, each individual has its own control parameters and mutation strategies that can be self-adaptively adjusted to different evolution phases and various optimization problems. SSCPDE was compared with 6 state-of-the-art DE variants by 38 different types of benchmark functions. Simulation results show that the average performance of SSCPDE is better than the six famous self-adaptive DE algorithms. Finally, the SSCPDE algorithm was used to optimize the five main operating conditions of the PX oxidation reaction process. Optimization results indicate that the production cost, loss of acetic acid and PX combustion of the PX oxidation reaction process are greatly reduced and that SSCPDE performs better than JADE, EPSDE, SaDE, and the optimizer of Aspen Plus and similar to jDE and CoDE.

关键词： Process optimization p-Xylene oxidation process differential evolution algorithm Parameter adaptation Mutation strategy adaptation

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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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differential evolution algorithm with co-evolution of control parameters and penalty factors for constrained optimization problems

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ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING 2012年第2期7卷 227-235页

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

Considering that it is difficult to set suitable penalty factors for the penalty function method, which is one of the most important ways to solve constrained optimization problems, and that the quality of obtained optimal solution mainly depends on the optimization algorithm's performance and handling constraints capacity, a novel differential evolution algorithm with co-evolution of control parameters and penalty factors, named as CoE-DE, is proposed. In CoE-DE, differential evolution operator is applied for evolving the original individuals, which consist of optimal variables. To improve the performance of CoE-DE and the handling constraints capacity, Alopex algorithm is used to co-evolve the symbiotic individuals, which consist of two DE control parameters and the penalty factors. To illustrate the whole performance of CoE-DE, several algorithms are applied to solve 13 benchmark functions and five constrained engineering problems. The results show that the performance of CoE-DE is better than SR algorithm and similar to a SIMPILE in 13 benchmark functions, and the satisfactory result is obtained in five constrained engineering problems. Copyright (C) 2010 Curtin University of Technology and John Wiley & Sons, Ltd.

关键词： self-adaptive co-evolution constrained optimization differential evolution algorithm Alopex algorithm

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differential evolution algorithm Based Self-adaptive Control Strategy for Fed-batch Cultivation of Yeast

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Computer Systems Science & Engineering 2021年第7期38卷 65-77页

作者： Aiyun Hu Sunli Cong Jian Ding Yao Cheng Enock Mpofu Jiangsu Key Lab of IoT Application Technology Wuxi Taihu UniversityWuxi214064China The key Laboratory of Industrial Biotechnology Ministry of EducationSchool of BiotechnologyJiangnan UniversityWuxi214122China Department of Food Processing Technology Harare Institute of TechnologyHarareZimbabwe

In the fed-batch cultivation of Saccharomyces cerevisiae,excessive glucose addition leads to increased ethanol accumulation,which will reduce the efficiency of glucose utilization and inhibit product *** glucose addition limits cell *** properly regulate glucose feed,a different evolution algorithm based on self-adaptive control strategy was proposed,consisting of three modules(PID,system identification and parameter optimization).Performance of the proposed and conventional PID controllers was validated and compared in simulated and experimental *** the simulation,cultivation with the self-adaptive control strategy had a more stable glucose feed rate and concentration,more stable ethanol concentration around the set-point(1.0 g·L^(-1)),and final biomass concentration of 34.5 g-DCW·L^(-1),29.2%higher than that with a conventional PID control *** the experiment,the cultivation with the self-adaptive control strategy also had more stable glucose and ethanol concentrations,as well as a final biomass concentration that was 37.4%higher than that using the conventional strategy.

关键词： Saccharomyces cerevisiae Ethanol accumulation differential evolution algorithm self-adaptive control

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differential evolution algorithm for Security-Constrained Energy and Reserve Optimization Considering Credible Contingencies

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IEEE TRANSACTIONS ON POWER SYSTEMS 2011年第3期26卷 1145-1155页

作者： Hejazi, H. A. Mohabati, H. R. Hosseinian, S. H. Abedi, M. Amirkabir Univ Technol Dept Elect Engn Tehran Iran Amirkabir Univ Technol Tehran Polytech Tehran Iran

Reserve services are an essential part of a standard market design and are under development in major electricity markets. This paper presents the development of differential evolution algorithm for optimal allocation of energy and spinning reserve. The optimization is performed in an integrated market with biddable spinning reserve and sectional price offer curves while considering all security and power systems constraints in steady state and system credible contingencies. The effectiveness of the proposed method is examined by application of the proposed algorithm on the IEEE 30-bus test system. Some comparisons are made with analytical and evolutionary methods in various cases.

关键词： differential evolution algorithm energy market evolutionary algorithms integrated electricity market reserve market sectional offering price uniform price

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differential evolution algorithm Optimized Dual Mode Load Frequency Controller for Isolated Wind-Diesel Power System with SMES & Fuel Cell

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2019年第1期12卷 50-60页

作者： Lal, Deepak Kumar Barisal, Ajit K. Tripathy, Manish Veer Surendra Sai Univ Technol Odisha Dept Elect Engn Burla India

Background: This paper presents dynamic performance analysis of isolated wind-diesel power system. A dual mode controller is proposed for pitch control of wind turbine generator. Methods: The parameters of the controller are optimized by differential evolution (DE) algorithm. The hybrid model was simulated with the proposed load frequency controller (LFC) by considering step load perturbation. The minimization of time multiplied integral of absolute error is considered as the objective function. The performance of the proposed controller is compared with the published result of the optimal controller. Further, the performance of the system is investigated by incorporating Super Conducting Magnetic Energy Storage (SMES) and Fuel Cell (FC). Also, the dynamic performance is investigated for changing step load perturbations. Furthermore, the response of the system is analyzed towards random loading. Results: Finally, sensitivity analysis is done by varying the system parameters and operating conditions from their nominal values. Conclusion: The simulation results show that the proposed dual mode DE optimized controller gives better transient and steady state response.

关键词： Load frequency control differential evolution algorithm wind-diesel system pitch control SMES fuel cell

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differential evolution algorithm for static and multistage transmission expansion planning

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IET GENERATION TRANSMISSION & DISTRIBUTION 2009年第4期3卷 365-384页

作者： Sum-Im, T. Taylor, G. A. Irving, M. R. Song, Y. H. Brunel Univ Brunel Inst Power Syst Sch Engn & Design Uxbridge UB8 3PH Middx England Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England

A novel differential evolution algorithm (DEA) is applied directly to the DC power flow-based model in order to efficiently solve the problems of static and multistage transmission expansion planning (TEP). The purpose of TEP is to minimise the transmission investment cost associated with the technical operation and economical constraints. Mathematically, long-term TEP using the DC model is a mixed integer nonlinear programming problem that is difficult to solve for large-scale real-world transmission networks. In addition, the static TEP problem is considered both with and without the resizing of power generation in this research. The efficiency of the proposed method is initially demonstrated via the analysis of low, medium and high complexity transmission network test cases. The analysis is performed within the mathematical programming environment of MATLAB using both DEA and conventional genetic algorithm and a detailed comparative study is presented.

关键词： DC power flow-based model power transmission planning Power transmission, distribution and supply Power system planning and layout Power system management, operation and economics Optimisation techniques nonlinear programming integer programming large-scale transmission network DEA investment cost power transmission economics integer nonlinear programming evolutionary computation MATLAB environment multistage transmission expansion planning load flow static TEP economical constraint costing differential evolution algorithm

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differential evolution algorithm applied to sidelobe level reduction on a planar array

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2007年第5期61卷 286-290页

作者： Rocha-Alicano, Carlos Covarrubias-Rosales, David Brizuela-Rodriguez, Carlos Panduro-Mendoza, Marco CICESE Res Ctr Ensenada 22860 Baja California Mexico Autonomous Univ Tamaulipas Reynosa Rhode Acad Ctr Reynosa Mexico

In this paper, differential evolution (DE), one of the youngest paradigms in evolutionary computation, is applied to the process of synthesizing a planar array factor focused on sidelobe-level reduction. Sidelobe level is a critical array factor parameter in the task of reducing background noise and interference in the most recent Wireless Communications Systems. Minimization of sidelobe level involves nonlinear and non-convex dependence between array factor and its elements parameters becoming a highly complex problem. However, DE has proven to be a fast and efficient algorithm for complex real-valued problems. Subsequently, a binary-coded genetic algorithm is proposed for the synthesis of planar arrays. Numerical results show a promising performance ofDE reducing noticeably the sidelobe level generated by a uniform planar array. (C) 2006 Elsevier GmbH. All rights reserved.

关键词： planar array differential evolution algorithm array factor synthesis

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differential evolution algorithm for multi-project resource leveling problem

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JOURNAL OF INTERDISCIPLINARY MATHEMATICS 2017年第6-7期20卷 1383-1386页

作者： Wang, Hai-Xin Wang, Zu-He Zhu, Mei Shandong Univ Sci & Technol Sch Econ & Management Qingdao 266510 Peoples R China Shandong TV Univ Sch Humanities & Law Jinan 250000 Shandong Peoples R China

In this paper, the model of RLP which in the multi-project environment is expanded to the RLPMP using the minimum of the variance of total resource consumption within unit time for all projects as the optimization objective. In order to ensure the optimal individual will not be destroyed by operations such as crossover or variation and still maintain convergence of differential evolution algorithm, we try to introduce the elitist reservation to the differential evolution algorithm. Case analysis showed that the improved differential evolution algorithm can solve the model of RLPMP and then improve the balance of multi-project resources effectively.

关键词： differential evolution algorithm Elitist reservation Multi-project resource leveling problem

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