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- 摘要
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- 实验方案
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Optical Fiber Transducer for Monitoring Single-Phase and Two-Phase Flows in Pipes
摘要： This paper presents a cooperative differential evolution (DE) with multiple populations for multiobjective optimization. The proposed algorithm has M single-objective optimization subpopulations and an archive population for an M-objective optimization problem. An adaptive DE is applied to each subpopulation to optimize the corresponding objective of the multiobjective optimization problem (MOP). The archive population is also optimized by an adaptive DE. The archive population is used not only to maintain all nondominated solutions found so far but also to guide each subpopulation to search along the whole Pareto front. These (M + 1) populations cooperate to optimize all objectives of the MOP by using adaptive DEs. Simulation results on benchmark problems with two, three, and many objectives show that the proposed algorithm is better than some state-of-the-art multiobjective DE algorithms and other popular multiobjective evolutionary algorithms. The online search behavior and parameter sensitivity of the proposed algorithm are also investigated.
关键词： cooperative populations,differential evolution,archive search,multiobjective optimization,many-objective optimization
更新于2025-09-23 15:19:57
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[IEEE 2018 20th International Conference on Transparent Optical Networks (ICTON) - Bucharest (2018.7.1-2018.7.5)] 2018 20th International Conference on Transparent Optical Networks (ICTON) - All Dielectric Mode Order Transformation Photonic Structure Design by Evolutionary Optimization Approach
摘要： In this manuscript, an evolutionary algorithm is applied to design a mode order converter photonic crystal structure for achieving transformation of the propagating fundamental mode to the higher order mode. In order to transform incident light mode from an even mode to an odd mode, predefined mask object functions are employed. The cross-sectional field profiles at the output face of the optimized structure are utilized to evaluate the optimization cost function to achieve mode order transformation task. In the study, the optimized structure composed of dielectric alumina (Al2O3) rods and time domain analyses of the optimization process are done by using finite-difference time domain method. Furthermore, numerical results are confirmed by experiments for designed structure at the microwave frequencies. It is shown that the numerical calculations and experimental measurements agree well with each other.
关键词： mode order converter,differential evolution algorithm,phase shift,photonic crystals,integrated optics
更新于2025-09-23 15:19:57
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Differential evolution based small signal modeling of GAN HEMT
摘要： In this article differential evolution based method of small signal modeling of GAN HEMT has been investigated. The method uses a unique search space exploration strategy to obtain optimized values of intrinsic and extrinsic elements pertaining to compact small signal model from extracted equivalent circuit elements and measured S-parameter data. Effectiveness of the method has been illustrated by comparing the measured S-parameter data of a 4 × 0.1 × 75 μm2 GaN/SiC HEMT in the frequency range of 1 to 30 GHz wherein modeled and measured data are in good agreement.
关键词： GaN HEMT,small signal model,differential evolution
更新于2025-09-19 17:15:36
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[IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - Buried-ridge-waveguide Type GaInAsP/InP Membrane Distributed-Reflector Lasers for Reduction of Differential Resistance
摘要： Utilizing cumulative correlation information already existing in an evolutionary process, this paper proposes a predictive approach to the reproduction mechanism of new individuals for differential evolution (DE) algorithms. DE uses a distributed model (DM) to generate new individuals, which is relatively explorative, whilst evolution strategy (ES) uses a centralized model (CM) to generate offspring, which through adaptation retains a convergence momentum. This paper adopts a key feature in the CM of a covariance matrix adaptation ES, the cumulatively learned evolution path (EP), to formulate a new evolutionary algorithm (EA) framework, termed DEEP, standing for DE with an EP. Without mechanistically combining two CM and DM based algorithms together, the DEEP framework offers advantages of both a DM and a CM and hence substantially enhances performance. Under this architecture, a self-adaptation mechanism can be built inherently in a DEEP algorithm, easing the task of predetermining algorithm control parameters. Two DEEP variants are developed and illustrated in the paper. Experiments on the CEC’13 test suites and two practical problems demonstrate that the DEEP algorithms offer promising results, compared with the original DEs and other relevant state-of-the-art EAs.
关键词： evolutionary computation,differential evolution (DE),evolution path (EP),Cumulative learning
更新于2025-09-19 17:13:59
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Comparative study on parameter extraction of photovoltaic models via differential evolution
摘要： Parameter extraction of photovoltaic (PV) models, which remains a multi-variable, nonlinear, and multi-modal problem, has recently gained considerable attention in the simulation and calculation of solar PV systems. Among various parameter extraction techniques, differential evolution (DE) and its variants are envisaged to be pretty effective for parameter extraction of PV models. In this paper, 11 state-of-the-art DE algorithms are comprehensively compared to extract the parameters of different PV models. The performance of each algorithm is evaluated based on the accuracy of solution, convergence speed, and the robustness. Based on the experimental results and analysis of different DE algorithms, the useful insights are concluded, which can guide the improvement of designing more efficient alternative DE methods for solving the PV parameter extraction problems.
关键词： Comparative study,Parameter extraction,Differential evolution,Photovoltaic models
更新于2025-09-19 17:13:59
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Efficient Carrier Transport for AlGaN-Based Deep-UV LEDs With Graded Superlattice p-AlGaN
摘要： This paper studies the optimization problem of topological active net (TAN), which is often seen in image segmentation and shape modeling. A TAN is a topological structure containing many nodes, whose positions must be optimized while a prede?ned topology needs to be maintained. TAN optimization is often time-consuming and even constructing a single solution is hard to do. Such a problem is usually approached by a “best improvement local search” (BILS) algorithm based on deterministic search (DS), which is inef?cient because it spends too much efforts in nonpromising probing. In this paper, we propose the use of micro-differential evolution (DE) to replace DS in BILS for improved directional guidance. The resultant algorithm is termed deBILS. Its micro-population ef?ciently utilizes historical information for potentially promising search directions and hence improves ef?ciency in probing. Results show that deBILS can probe promising neighborhoods for each node of a TAN. Experimental tests verify that deBILS offers substantially higher search speed and solution quality not only than ordinary BILS, but also the genetic algorithm and scatter search algorithm.
关键词： grid deformation,topological active net (TAN),structure optimization,Differential evolution (DE),topological optimization
更新于2025-09-19 17:13:59
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An experimental study on photovoltaic module with optimum power point tracking method
摘要： With the latest development in the area of power electronics, the photovoltaic (PV) cell can be made to operate at the optimum peak point with increased system efficiency. To maximize the power on photovoltaic cells under various conditions, optimum power point tracking (OPPT) methods such as conventional and soft computing methods are used. But it is not providing accurate and efficient output due to its randomness, fixed step size, and poor convergence. In this paper, the adaptive differential evolution (ADE) algorithm is introduced in the solar module to obtain the maximum power, and it has the ability to reach the optimum peak with the shorter time period. An Apriori method is used in the proposed ADE algorithm, wherein mutation factor and crossover are used as control parameters to increase the speed. The ruggedness of the ADE algorithm is tested under different shading condition such as no shading, 30% shading, and 50% shading condition. Extensive simulation has been carried out using PV solar module, and the analysis has been tabulated and compared with the existing results. Various statistical metrics such as root mean square error, the relative error, tracking efficiency, standard deviation, and efficiency are used to evaluate the effectiveness and validate the feasibility of the proposed method. Further, hardware has been implemented and tested with this algorithm.
关键词： photovoltaic cell,differential evolution algorithm,shading condition,optimum power point tracking
更新于2025-09-19 17:13:59
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Multiple Round-Trip Delay-Based Architecture for Si-Integrated Photonic Reservoir Computing
摘要： This paper presents a cooperative differential evolution (DE) with multiple populations for multiobjective optimization. The proposed algorithm has M single-objective optimization subpopulations and an archive population for an M-objective optimization problem. An adaptive DE is applied to each subpopulation to optimize the corresponding objective of the multiobjective optimization problem (MOP). The archive population is also optimized by an adaptive DE. The archive population is used not only to maintain all nondominated solutions found so far but also to guide each subpopulation to search along the whole Pareto front. These (M + 1) populations cooperate to optimize all objectives of the MOP by using adaptive DEs. Simulation results on benchmark problems with two, three, and many objectives show that the proposed algorithm is better than some state-of-the-art multiobjective DE algorithms and other popular multiobjective evolutionary algorithms. The online search behavior and parameter sensitivity of the proposed algorithm are also investigated.
关键词： Archive,differential evolution (DE),cooperative populations,search,multiobjective optimization,many-objective optimization
更新于2025-09-19 17:13:59
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Parameters Identification of Photovoltaic Models Using a Multi-Strategy Success-History-Based Adaptive Differential Evolution
摘要： Parameters identi?cation of photovoltaic (PV) models based on measured current-voltage characteristics curves is signi?cant for the simulation, evaluation and control of PV systems. To accurately and reliably identify the parameters of different PV models, a novel optimization algorithm, multi-strategy success-history based adaptive differential evolution with linear population size reduction (MLSHADE), is proposed. MLSHADE mainly divides evolutionary process into two phases during every generation. According to the de?nition of class probability variable, the population individuals of ?rst phase are assigned to different two populations for exploration and exploitation, respectively. The novelty of MLSHADE algorithm lies primarily in three improvements: (i) a new weighted mutation strategy is used to enrich the population diversity of later iterations for differential evolution population in the ?rst phase; (ii) inferior solutions search (ISS) technique is presented to avoid falling into local optimum for covariance matrix adaptation evolution strategy population in the ?rst phase; and (iii) Eigen Gaussian random walk strategy is proposed to help maintain effectively the balance between the global exploration and local exploitation abilities in the second phase. The experiments on CEC 2018 test suite illustrate that the proposed MLSHADE exerts the better performances against the stat-of-the-art algorithms in terms of accuracy, reliability and time consumption. The proposed MLSHADE is used to solve the parameters identi?cation problems of different PV models including single diode, double diode, and PV module. Comprehensive experiment results and analyses indicate that MLSHADE can obtain a highly competitive performance compared with other state-of-the-art algorithms, especially in terms of accuracy and reliability.
关键词： Differential evolution operator,numerical optimization,parameters identi?cation of photovoltaic,multi-strategy LSHADE
更新于2025-09-16 10:30:52
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Evolutionary multi-task optimization for parameters extraction of photovoltaic models
摘要： As the demand for solar energy increases dramatically, the optimization and control of photovoltaic systems become increasingly important, accurate and reliable parameter identification of photovoltaic models is always required, which proposes an urgent need for accurate and robust algorithms. To this end, many heuristic algorithms have been proposed to extract the parameters of different photovoltaic models. However, they only extract the parameters of one model in a single run, which is inconsistent with the human ability to solve multiple tasks simultaneously and ignores the useful information derived from different models. Therefore, in this paper an evolutionary multi-task optimization algorithm is proposed to extract the parameters of multiple different photovoltaic models simultaneously. To be specific, the helpful information found by the population is transferred through the cross-task crossover to improve the performance in terms of solution quality and convergence rate of the population. The proposed algorithm is evaluated by extracting the parameters of three different models simultaneously, i.e., single diode, double diode, and photovoltaic module model. Comprehensive results demonstrate that the proposed algorithm has better performance with respect to the accuracy and robustness in comparison with other state-of-the-art algorithms.
关键词： Differential evolution,Parameter extraction,Evolutionary multi-task optimization,Photovoltaic models
更新于2025-09-16 10:30:52