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Genetic and evolutionary Computation Conference (GECCO)

作者： Dixit, Abhishek Mani, Ashish Chongqing Univ Educ 1 Sch Artificial Intelligence Chongjiao RdNanshan St Chongqing 400065 Peoples R China Amity Sch Engn & Technol Amity Innovat & Design Ctr Dept EEE Noida India

ISBN: (纸本)9798400701207

The differential evolution (DE) algorithm is an efficient algorithm with strong search capability. This algorithm is easy to implement and used for solving large-scale optimization problems. However, DE also has drawbacks of insufficient diversity in the later search stage, while solving optimization problems, slow convergence speed, and a high search stagnation possibility. Quantum Entanglement inspired differential evolution algorithm (QE-DE) has been proposed in this paper to solve the drawbacks of DE. For solving high-dependency optimization problems, QE-DE integrates entangled states in its Qubits (or quantum bit). The optimization process is further accelerated by utilizing the quantum local search. The proposed QE-DE algorithm is evaluated on the IEEE Congress of evolutionary Computing (CEC 2017) benchmark set for 10 and 30-dimensions. QE-DE is also compared with existing variants of DE and some other popular algorithms. QE-DE outperforms the performance comparison results.

关键词： Quantum Entanglement differential evolution algorithm Metaheuristic algorithms

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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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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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Adaptive weighted dynamic differential evolution algorithm for emergency material allocation and scheduling

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COMPUTATIONAL INTELLIGENCE 2022年第3期38卷 714-730页

作者： Wang, Tiejun Wu, Kaijun Du, Tiaotiao Cheng, Xiaochun Northwest Minzu Univ Sch Math Lanzhou 730030 Peoples R China Northwest Minzu Univ Comp Sci Inst Lanzhou 730030 Peoples R China Lanzhou Jiao Tong Univ Sch Elect & Informat Engn Lanzhou Peoples R China Middlesex Univ Dept Comp Sci London NW4 4BT England

Emergency material allocation and scheduling is a combination optimization problem, which is essentially a Non-deterministic Polynomial (NP) problem. Aiming at the problems such as slow convergence, easy prematurely falling into local optimum, and parameter constraints to solve high-dimensional and multi-modal combination optimization problems, this article proposes an adaptive weighted dynamic differential evolution (AWDDE) algorithm. The algorithm uses a chaotic mapping strategy to initialize the population. By weighting the standard differential evolution (DE) mutation strategy, a new weighted mutation operator is proposed. The scaling factor and cross probability can be adaptively adjusted. A disturbance operator is introduced to randomly generate the perturbation mutation and to accelerate the premature individuals to jump out of the local optimum. The algorithm is applied to the problem of emergency material allocation and scheduling, and a two-stage emergency material allocation and scheduling model is established. Compared with the standard DE algorithm and the chaos adaptive particle swarm algorithm, the results show that the AWDDE algorithm has the characteristics of stronger global optimization ability and faster convergence speed compared with other optimization algorithms, which provide assistance for smart cities research, including smart city services, applications, case studies, and policymaking considerations for emergency management.

关键词： adaptive dynamic weighting differential evolution algorithm emergency material allocation and scheduling smart cities research

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Fractional-order system identification based on an improved differential evolution algorithm

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ASIAN JOURNAL OF CONTROL 2022年第5期24卷 2617-2631页

作者： Yu, Wei Liang, HengHui Chen, Rui Wen, Chenglin Luo, Ying Foshan Univ Sch Mechatron Engn & Automat Foshan Peoples R China Guangdong Univ Petrochem Technol Sch Automat Maoming Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn 1037 Luoyu Rd Wuhan Hubei Peoples R China

With the continuous increasing of system modeling accuracy and control performance requirements, the fractional-order models are applied to describe the dynamic characteristics of the real systems more accurately over traditional integer-order models. Traditional identification algorithms for fractional-order system require stringent information on the derivatives of the objective function and initial values of the parameters, which is difficult in practical applications. The differential evolution (DE) algorithm has the advantage of simple implementation and superior performance for solving multi-dimensional complex optimization problems which is suitable for the identification of fractional-order systems. Aiming at improving the solution accuracy and convergence speed, an improved DE (IDE) algorithm for fractional-order system identification is proposed. The movement strategy of the marine predator algorithm (MPA) and the mutation strategy of randomly selecting one from the optimal individual population as the basis vector are both adopted in this proposed IDE algorithm. The parameter information of the successfully mutated individual is archived during the iteration process. The scaling factor and crossover probability factor are adaptively adjusted according to the archived information to enhance the best solution accuracy obtained in the search process. By testing 13 sets of unimodal and multimodal functions with high-performance optimization algorithms, the accuracy performance of the proposed IDE solution is verified. The IDE algorithm is applied to identify the parameters of the fractional-order system of permanent magnet synchronous motor with simulation and experiment. The identification results clearly show the effectiveness of the proposed methodology.

关键词： differential evolution algorithm fractional-order system identification adaptive adjustment permanent magnet synchronous motor

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Output-only structural damage identification using hybrid Jaya and differential evolution algorithm with reference-free correlation functions

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MEASUREMENT 2022年 199卷

作者： Zhang, Guangcai Wan, Chunfeng Xiong, Xiaobing Xie, Liyu Noori, Mohammad Xue, Songtao Southeast Univ Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing Peoples R China Tongji Univ Res Inst Struct Engn & Disaster Reduct Coll Civil Engn Shanghai Peoples R China Calif Polytech State Univ San Luis Obispo Dept Mech Engn San Luis Obispo CA USA Tohoku Inst Technol Dept Architecture Sendai Japan

To solve the optimization-based structural damage identification problem, a novel hybrid algorithm based on Jaya and differential evolution algorithm (HJDEA) is proposed to detect, locate and quantify structural damages by effectively incorporating the powerful local exploitation capacity of Jaya algorithm and global exploration capability of differential evolution. Meanwhile, Hammersley sequence initialization and Le acute accent vy flight search mechanism are introduced into HJDEA to further improve convergence rate and refining the quality of the best solution. Four different algorithms, genetic algorithm, particle swarm optimization, Jaya and the proposed HJDEA are employed for comparative study. In addition, the objective function is established by adjacent acceleration correlation function so as to avoid false identification caused by defining improper reference point. The performance of the proposed damage identification strategy based on HJDEA and adjacent acceleration correlation function is investigated with numerical examples involving an 8-DOF lumped mass model and a cantilever beam, as well as an experimental study of the ASCE benchmark structure under white noise excitation. Results show that the proposed hybrid identification method is accurate, efficient and robust in the identification of the damage existence, location and severity of stiffness and mass parameters even with partial output-only responses and 20% noise-polluted measurements.

关键词： Structural damage identification Jaya algorithm differential evolution algorithm Hybrid algorithm Adjacent acceleration correlation function Gradient search method

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Multi-objective optimum design of five-phase squirrel cage induction motor by differential evolution algorithm

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ENERGY REPORTS 2022年 8卷 51-62页

作者： Chen, Hansi Zhao, Jinghong Wang, Hanming Zhang, Qingfei Luo, Xiangyu Xu, Hao Xiong, Yiyong Naval Univ Engn Coll Elect Engn Wuhan 430033 Hubei Peoples R China

Compared with the traditional induction motors, five-phase squirrel cage induction motor (FSCIM) have the advantages of lower torque ripple and single-phase power, flexible control strategy, higher power density and fault-tolerant operation, which has been widely applied on electrical vehicles, rail transit, underwater vehicles and so on. This paper presents an analytical design optimization method based on the coupling of electromagnetic equivalent circuit (EEC) and differential evolution algorithm (DEA). Furthermore, the difference between three and five-phase winding on harmonic specific leakage permeance is investigated in detail. The nonlinear and oeolotropic influences of design geometrical parameters (slots openings, main dimension ratio, yoke height, airgap length, etc.) on the performances of the basic FSCIM model are evaluated with analytical model. For more accurate results, the harmonic characteristics of five-phase winding structure, iron core saturation, rotor slot skew width, stator end winding, slot leakage inductance and stator and rotor slot structure are considered. Meanwhile, the efficiency, power factor, maximum torque, slot fill factor and material consumption of FSCIM are calculated as the comprehensive objective function. The transient finite element analysis (TFEA) and experimental test verify the accuracy of the proposed optimum model. In addition, the feasibility of the multi objective optimization design scheme provided by the analytical model is verified by TFEA. (c) 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Five-phase induction motor differential evolution algorithm Multi objective optimization Fast performance computation

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Online algorithm configuration for differential evolution algorithm

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APPLIED INTELLIGENCE 2022年第8期52卷 9193-9211页

作者： Huang, Changwu Bai, Hao Yao, Xin Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China INSA Rouen Normandie Lab Mech Normandy LMN F-76000 Rouen France

The performance of evolutionary algorithms (EAs) is strongly affected by their configurations. Thus, algorithm configuration (AC) problem, that is, to properly set algorithm's configuration, including the operators and parameter values for maximizing the algorithm's performance on given problem(s) is an essential and challenging task in the design and application of EAs. In this paper, an online algorithm configuration (OAC) approach is proposed for differential evolution (DE) algorithm to adapt its configuration in a data-driven way. In our proposed OAC, the multi-armed bandit algorithm is adopted to select trial vector generation strategies for DE, and the kernel density estimation method is used to adapt the associated control parameters during the evolutionary search process. The performance of DE algorithm using the proposed OAC (OAC-DE) is evaluated on a benchmark set of 30 bound-constrained numerical optimization problems and compared with several adaptive DE variants. Besides, the influence of OAC's hyper-parameter on its performance is analyzed. The comparison results show OAC-DE achieves better average performance than the compared algorithms, which validates the effectiveness of the proposed OAC. The sensitivity analysis indicates that the hyper-parameter of OAC has little impact on OAC-DE's performance.

关键词： Automatic algorithm configuration differential evolution algorithm Adaptive parameter control Multi-armed bandit Kernel density estimation Machine learning

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Neighborhood-based differential evolution algorithm with direction induced strategy for the large-scale combined heat and power economic dispatch problem

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INFORMATION SCIENCES 2022年 613卷 469-493页

作者： Liu, Di Hu, Zhongbo Su, Qinghua Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China

The combined heat and power economic dispatch (CHPED) problem is a complicated non -convex optimization problem with several constraints that has been a challenging and important task in the power system. Great efforts have been made to solve the small-scale CHPED problem, and there is considerable scope for research in the large-scale CHPED problem with lots of local optima. This paper presents a neighborhood-based differ-ential evolution algorithm with direction induced strategy (NDIDE) to solve the large-scale CHPED problem. In NDIDE, a novel mutation strategy, named neighborhood nonelite direc-tion induced strategy based on DE/rand/1 (DE/ldrand/1), is proposed to explore some untrodden areas of a search space using the direction information of pointing to inferior solutions within a small neighborhood. Such a strategy helps to strengthen the exploitation of some selected neighborhoods. Consequently, a good balance between exploration and exploitation abilities could be achieved. NDIDE is implemented on the CHPED systems with 7, 84, 96 and 192 units. The dispatch schedules obtained from NDIDE are feasible in three large CHPED systems and show the advantages of NDIDE in terms of cost savings and the robustness over the results obtained by three state-of-the-art differential evolution algo-rithms as well as recent works in this field. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Combined heat and power (CHP) differential evolution algorithm Economic dispatch Large-scale system

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Novel Formulations and Improved differential evolution algorithm for Optimal Lane Reservation With Task Merging

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第11期23卷 21329-21344页

作者： Wu, Peng Xu, Ling D'Ariano, Andrea Zhao, Yongxiang Chu, Chengbin Fuzhou Univ Sch Econ & Management Fuzhou 350108 Peoples R China Roma Tre Univ Dept Engn Rome Italy Univ Gustave Eiffel ESIEE Paris COSYS GRETTIA F-77454 Marnela La Vallee France

This paper investigates a new lane reservation problem with task merging that consists of optimally determining which lanes in a transportation network have to be reserved and designing reserved lane-based routes in the network for time-crucial transport tasks. Part of the tasks whose destinations are geographically close is merged to reduce the number of vehicles and transport costs. Reserved lanes can reduce the travel time of task vehicles passing through them, while they will generate negative impact on normal traffic, such as traffic delay to the vehicles on adjacent non-reserved lanes. The objective is to minimize the total negative impact of all reserved lanes. For this problem, two new integer linear programming (ILP) models are first developed. The complexity of the problem is proved to be NP-hard. Since commercial solver (like CPLEX) is time-consuming for solving it when the problem size increases, a fast and effective improved differential evolution algorithm (IDEA) is developed based on explored problem properties. Extensive experimental results for a real-life case and benchmark instances of up to 500 nodes in the network and 30 transport tasks show the favorable performance of the IDEA, as compared to CPLEX, differential evolution algorithm and genetic algorithm. Management insights are also drawn to support practical decision-making.

关键词： Task analysis Roads Transportation Merging Costs Games Uncertainty Lane reservation task merging integer linear programming differential evolution algorithm

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