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Adaptive differential evolution algorithm for multiobjective optimization problems

APPLIED MATHEMATICS AND COMPUTATION 2008年第1-2期201卷 431-440页

作者： Qian, Weiyi Li, Ajun Bohai Univ Dept Math Jinzhou 121000 Liaoning Peoples R China

In this paper, a new adaptive differential evolution algorithm ( ADEA) is proposed for multiobjective optimization problems. In ADEA, the variable parameter F based on the number of the current Pareto-front and the diversity of the current solutions is given for adjusting search size in every generation to find Pareto solutions in mutation operator, and the select operator combines the advantages of DE with the mechanisms of Pareto-based ranking and crowding distance sorting. ADEA is implemented on five classical multiobjective problems, the results illustrate that ADEA efficiently achieves two goals of multiobjective optimization problems:find the solutions converge to the true Pareto-front and uniform spread along the front. (c) 2008 Elsevier Inc. All rights reserved.

关键词： multiobjective optimization problems differential evolution algorithm adaptive select operator

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Parameter identification for discrete memristive chaotic map using adaptive differential evolution algorithm

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NONLINEAR DYNAMICS 2022年第1期107卷 1263-1275页

作者： Peng, Yuexi He, Shaobo Sun, Kehui Xiangtan Univ Sch Comp Sci Xiangtan 411105 Peoples R China Xiangtan Univ Sch Cyberspace Sci Xiangtan 411105 Peoples R China Cent South Univ Sch Phys & Elect Changsha 410083 Peoples R China

Since the concept of discrete memristor was proposed, more and more scholars began to study this topic. At present, most of works on the discrete memristor are devoted to the mathematical modeling and digital circuit implementation, but the research on its synchronization control has not received much attention. This paper focuses on the parameter identification for the discrete memristive chaotic map, and a modified intelligent optimization algorithm named adaptive differential evolution algorithm is proposed. To deal with the complex behaviors of hyperchaos and coexisting attractors of the considered discrete memristive chaotic maps, the identification objective function adopts two special parts: time sequences and return maps. Numerical simulations demonstrate that the proposed algorithm has the best performance among six existing algorithms, and it can still accurately identify the parameters of the original system under noise interference.

关键词： Parameter identification Discrete memristor Chaotic map differential evolution algorithm Return maps Coexisting attractors

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Design of Three-Phase Induction Machine Using differential evolution algorithm

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IEEE LATIN AMERICA TRANSACTIONS 2015年第7期13卷 2202-2208页

作者： Malagoli, J. A. Camacho, J. R. Ferreira da Luz, M. V. Ferreira, J. H. I. Sobrinho, A. M. Univ Fed Uberlandia BR-38400 Uberlandia MG Brazil Univ Fed Santa Catarina Florianopolis SC Brazil Inst Fed Triangulo Min Ensino Basico Tecn & Tecnol Ituiutaba MG Brazil

In the present day, design of engineering systems appears as a line of research of great interest due the many applications that can be found in different areas of science and engineering. In this setting, design of induction motor, whose functions base are induce alternating currents in the rotor circuit, by the magnetic field rotating produced the stator coils, appears as an interesting theme research already which is directly related to manufacturing costs motors. In this context, this work aims the design of a three-phase induction motor using the differential evolution algorithm. For this purpose considered minimizing loss and cost on motor by determining the geometric variables vector characterizing the model mathematical presented. To solve these problems is used the MODE algorithm (Multiobjective Optimization differential evolution) and the outcome is compared to the NSGA II algorithm (Non-dominated Sorting Genetic algorithm II).

关键词： Design of Induction Motor differential evolution algorithm Optimization

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

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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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Three-stage multi-modal multi-objective differential evolution algorithm for vehicle routing problem with time windows

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INTELLIGENT DATA ANALYSIS 2024年第2期28卷 485-506页

作者： Zhang, Hai-Fei Ge, Hong-Wei Li, Ting Su, ShuZhi Tong, YuBing Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi Jiangsu Peoples R China Anhui Univ Sci Technol Sch Comp Sci & Engn Huainan Anhui Peoples R China Univ Penn Dept Radiol Philadelphia PA USA

In this paper, the mathematical model of Vehicle Routing Problem with Time Windows (VRPTW) is established based on the directed graph, and a 3-stage multi-modal multi-objective differential evolution algorithm (3S-MMDEA) is proposed. In the first stage, in order to expand the range of individuals to be selected, a generalized opposition-based learning (GOBL) strategy is used to generate a reverse population. In the second stage, a search strategy of reachable distribution area is proposed, which divides the population with the selected individual as the center point to improve the convergence of the solution set. In the third stage, an improved individual variation strategy is proposed to legalize the mutant individuals, so that the individual after variation still falls within the range of the population, further improving the diversity of individuals to ensure the diversity of the solution set. Based on the synergy of the above three stages of strategies, the diversity of individuals is ensured, so as to improve the diversity of solution sets, and multiple equivalent optimal paths are obtained to meet the planning needs of different decision-makers. Finally, the performance of the proposed method is evaluated on the standard benchmark datasets of the problem. The experimental results show that the proposed 3S-MMDEA can improve the efficiency of logistics distribution and obtain multiple equivalent optimal paths. The method achieves good performance, superior to the most advanced VRPTW solution methods, and has great potential in practical projects.

关键词： Logistics distribution vehicle routing problem multi-modal multi-objective optimization three-stage strategy differential evolution algorithm

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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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Optimal coordination of overcurrent relays in distribution systems with distributed generators based on differential evolution algorithm

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2013年第1期23卷 1-12页

作者： Yang, Hui Wen, Fushuan Ledwich, Gerard Zhuhai Polytech Zhuhai 519015 Peoples R China Zhejiang Univ Sch Elect Engn Hangzhou 310027 Peoples R China Queensland Univ Technol Sch Engn Syst Brisbane Qld 4001 Australia

Distributed generators (DGs) are defined as generators that are connected to a distribution network. The direction of the power flow and short-circuit current in a network could be changed compared with one without DGs. The conventional protective relay scheme does not meet the requirement in this emerging situation. As the number and capacity of DGs in the distribution network increase, the problem of coordinating protective relays becomes more challenging. Given this background, the protective relay coordination problem in distribution systems is investigated, with directional overcurrent relays taken as an example, and formulated as a mixed integer nonlinear programming problem. A mathematical model describing this problem is first developed, and the well-developed differential evolution algorithm is then used to solve it. Finally, a sample system is used to demonstrate the feasibility and efficiency of the developed method. Copyright (c) 2011 John Wiley & Sons, Ltd.

关键词： distributed generation distribution system coordination of protective relays directional relay differential evolution algorithm

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A green energy model for resource allocation in computational grid using differential evolution algorithm

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WIRELESS NETWORKS 2021年第7期27卷 4773-4794页

作者： Rafie, Mahnaz Islamic Azad Univ Dept Comp Engn Ramhormoz Branch Ramhormoz Iran

Energy loss due to the complete equipping and overstatement of infrastructure networks has recently prompted internet service providers to reduce energy consumption. Increasing energy costs and environmental awareness have recently raised concerns about grid power consumption and the underlying structure of services;therefore, one of the top priorities for service providers is to reduce energy consumption. In this paper, a method based on the differential evolution optimization algorithm is introduced to reduce the energy consumption costs, network load balancing, and execution time. To increase the convergence speed in the optimization algorithm, two Mamdani fuzzy inference systems have been used which are used in the optimization objective function to determine the fitness amount of each allocation. To better understand proposed algorithm behavior, the results of the differential evolution algorithm are compared with the AGA and MPGA methods. Simulation results show that the proposed algorithm achieves more than 30% performance improvement in comparison with the previous algorithms.

关键词： differential evolution algorithm Green energy Grid Resource allocation

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Determination of induction motor parameters with differential evolution algorithm

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NEURAL COMPUTING & APPLICATIONS 2012年第8期21卷 1995-2004页

作者： Arslan, Mustafa Cunkas, Mehmet Sag, Tahir Selcuk Univ Fac Tech Educ Elect & Comp Educ TR-42003 Konya Turkey Selcuk Univ Dept Elect Tech Sci Vocat Sch TR-42003 Konya Turkey

In this study, the determination of equivalent circuit parameters of induction motors is carried out with differential evolution algorithm (DEA) and genetic algorithm (GA). As an objective function in the algorithms, the sum torque error at zero speed, pull-out, and rated speed is used. The determination of equivalent circuit parameters is performed with three induction motors of 2.2, 5.5, and 37 kW. In particular, the search ability of DEA is compared with GA by using the same population size, number of iteration, and crossover rate. In addition, the effects of the obtained equivalent circuit parameters on induction motors characteristics are investigated and presented with graphics. The results show that the use of DEA instead of GA increases the convergence sensitivity and reduces the simulation time.

关键词： Inductions motor Parameter determination differential evolution algorithm Genetic algorithm

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A multi-objective differential evolution algorithm for parallel batch processing machine scheduling considering electricity consumption cost

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COMPUTERS & OPERATIONS RESEARCH 2018年 96卷 55-68页

作者： Zhou, Shengchao Li, Xiaolin Du, Ni Pang, Yan Chen, Huaping Cent South Univ Forestry & Technol Sch Logist Changsha 410004 Hunan Peoples R China China Univ Min & Technol Sch Mines Xuzhou 221116 Jiangsu Peoples R China Hunan Agr Univ Sch Engn Changsha 410128 Hunan Peoples R China Univ Sci & Technol China Sch Management Hefei 230026 Anhui Peoples R China

The manufacturing industry consumes massive amounts of energy and produces great numbers of greenhouse gases every year. Recently, an increasing attention has been paid to the energy efficiency of the manufacturing industry. This paper considers a parallel batch processing machine (BPM) scheduling problem in the presence of dynamic job arrivals and a time-of-use pricing scheme. The objective is to simultaneously minimize makespan, a measure of production efficiency and minimize total electricity cost (TEC), an indicator for environmental sustainability. A BPM is capable of processing multiple jobs at a time, which has wide applications in many manufacturing industries such as electronics manufacturing facilities and steel-making plants. We formulate this problem as a mixed integer programming model. Considering the problem is strongly NP-hard, a multi-objective differential evolution algorithm is proposed for effectively solving the problem at large scale. The performance of the proposed algorithm is evaluated by comparing it to the well-known NSGA-II algorithm and another multi-objective optimization algorithm AMGA. Experimental results show that the proposed algorithm performs better than NSGA-II and AMGA in terms of solution quality and distribution. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Sustainable scheduling Total electricity cost Batch processing machines differential evolution algorithm Multi-objective optimization

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