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- 摘要
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- 实验方案
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Complex Inverse Design of Meta-optics by Segmented Hierarchical Evolutionary Algorithm
摘要： With the recent burgeoning advances in nano-optics, ultracompact, miniaturized photonic devices with high-quality and spectacular functionalities are highly desired. Such devices’ design paradigms often call for the solution of a complex inverse nonanalytical/semianalytical problem. However, currently reported strategies dealing with amplitude-controlled meta-optics devices achieved limited functionalities mainly due to restricted search space and demanding computational schemes. Here, we established a segmented hierarchical evolutionary algorithm, aiming to solve large-pixelated, complex inverse meta-optics design and fully demonstrate the targeted performance. This paradigm allows significantly extended search space at a rapid converging speed. As typical complex proof-of-concept examples, large-pixelated meta-holograms are chosen to demonstrate the validity of our design paradigm. An improved fitness function is proposed to reinforce the performance balance among image pixels, so that the image quality is improved and computing speed is further accelerated. Broadband and full-color meta-holograms with high image fidelities using binary amplitude control are demonstrated experimentally. Our work may find important applications in the advanced design of future nanoscale high-quality optical devices.
关键词： full-color meta-holograms,fast-converging algorithm,meta-optics,segmented hierarchical evolutionary algorithm,complex large-pixelated inverse design
更新于2025-09-23 15:23:52
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Semiconducting B<sub>13</sub>C<sub>2</sub> system: Structure search and DFT-based analysis
摘要： DFT calculation on Boron Carbide in B13C2 stoichiometry using a 15-atom unit cell necessarily results in metallic ground state regardless of the crystal structure. This is because such a unit cell consists of odd number of electrons, and hence complete filling of the top most band(s) of nonzero occupancy is impossible. This is in contrast to the observed semiconducting nature. If the crystal structure of B13C2 is made of a 30-atom unit cell which cannot be reduced to a 15 atom cell, there is a possibility of obtaining either a metallic or a semiconducting state as such a cell consists of an even number of electrons. In this work the evolutionary algorithm based structure search using 30-atom unit cells has yielded a previously unreported semiconducting system of B13C2 with unique bonding pattern. The mechanical and dynamical stability of the system have been properly established through the computation of elastic constants and phonon spectra. Its bond lengths, elastic moduli, hardness and infrared spectrum are in good agreement with experimental data.
关键词： Boron Carbide,elastic constants,evolutionary algorithm,DFT,phonon spectra,semiconducting,structure search,B13C2
更新于2025-09-23 15:22:29
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Monte Carlo method for analyzing the propagation of radiation in the skin layers containing blood in photoplethysmography
摘要： In evolutionary multiobjective optimization, it is very important to be able to visualize approximations of the Pareto front (called approximation sets) that are found by multi-objective evolutionary algorithms. While scatter plots can be used for visualizing 2-D and 3-D approximation sets, more advanced approaches are needed to handle four or more objectives. This paper presents a comprehensive review of the existing visualization methods used in evolutionary multiobjective optimization, showing their outcomes on two novel 4-D benchmark approximation sets. In addition, a visualization method that uses prosection (projection of a section) to visualize 4-D approximation sets is proposed. The method reproduces the shape, range, and distribution of vectors in the observed approximation sets well and can handle multiple large approximation sets while being robust and computationally inexpensive. Even more importantly, for some vectors, the visualization with prosections preserves the Pareto dominance relation and relative closeness to reference points. The method is analyzed theoretically and demonstrated on several approximation sets.
关键词： projection,evolutionary multiobjective optimization,Pareto front,evolutionary algorithm,visualization,Approximation set
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Effects of Amorphous Silicon Thickness Variation on Infrared-Tuned Silicon Heterojunction Bottom Cells
摘要： The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.
关键词： problems,Multiobjective evolutionary algorithm (MOEA),niching,multiobjective optimization,evolutionary algorithm based on decomposition (MOEA/D)
更新于2025-09-19 17:13:59
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Coyote optimization algorithm for the parameter extraction of photovoltaic cells
摘要： In this paper, a new and powerful metaheuristic optimization technique known as the Coyote Optimization Algorithm (COA) is proposed for the parameter extraction of the PV cell/module. It is utilized to identify the parameters of the single diode and two-diode models. Inspired by the social norms adopted by the coyotes to ensure the survivability of their species, the COA possesses several outstanding merits such as low number of control parameters, ease of implementation and diverse mechanisms for balancing exploration and exploitation. For physically meaningful solutions, a set of parametric constraints is introduced to prevent the coyotes from straying outside of the predefined boundaries of the search space. Extensive tests indicate that the proposed optimizer exhibits superior accuracy compared to other state-of-the-art EA-based parameter extraction methods. It achieved root-mean-square error (RSME) as low as 7.7301E-04 A and 7.3265E-04 A, for the single-diode and two-diode models, respectively. Moreover, the algorithm maintains outstanding performance when tested on an assortment of modules of different technologies (i.e. mono-crystalline, poly-crystalline, and thin film) at varying irradiance and temperature. The standard deviations (STDs) of the fitness values over 35 runs are measured to be less than 1 × 10?5 for both models. This suggests that the results produced by the algorithm are highly consistent. With these outstanding merits, the COA is envisaged to be a competitive option for the parameter extraction problem of PV cell/module.
关键词： Equivalent circuit model,Solar photovoltaic,Evolutionary algorithm,Coyote optimization algorithm,Parameter extraction
更新于2025-09-16 10:30:52
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[IEEE 2019 International Conference on Advanced Electrical Engineering (ICAEE) - Algiers, Algeria (2019.11.19-2019.11.21)] 2019 International Conference on Advanced Electrical Engineering (ICAEE) - Photovoltaic module parameters extraction using best-so-far ABC algorithm
摘要： Existing multiobjective evolutionary algorithms (MOEAs) tackle a multiobjective problem either as a whole or as several decomposed single-objective sub-problems. Though the problem decomposition approach generally converges faster through optimizing all the sub-problems simultaneously, there are two issues not fully addressed, i.e., distribution of solutions often depends on a priori problem decomposition, and the lack of population diversity among sub-problems. In this paper, a MOEA with double-level archives is developed. The algorithm takes advantages of both the multiobjective-problem-level and the sub-problem-level approaches by introducing two types of archives, i.e., the global archive and the sub-archive. In each generation, self-reproduction with the global archive and cross-reproduction between the global archive and sub-archives both breed new individuals. The global archive and sub-archives communicate through cross-reproduction, and are updated using the reproduced individuals. Such a framework thus retains fast convergence, and at the same time handles solution distribution along Pareto front (PF) with scalability. To test the performance of the proposed algorithm, experiments are conducted on both the widely used benchmarks and a set of truly disconnected problems. The results verify that, compared with state-of-the-art MOEAs, the proposed algorithm offers competitive advantages in distance to the PF, solution coverage, and search speed.
关键词： multiobjective optimization,Evolutionary algorithm (EA),global optimization
更新于2025-09-16 10:30:52
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[IEEE 2019 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD) - Ottawa, ON, Canada (2019.7.8-2019.7.12)] 2019 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD) - Alternative approach to optimizing optical spacer layer thickness in solar cell using evolutionary algorithm
摘要： This work is inspired by Darwin's biological evolution theory: natural selection. We propose to use genetic evolutionary algorithm to optimize the search for the optimal thickness in solar cells with regards to maximizing short-circuit current density. Optical spacer layer thickness need to be optimized in order to achieve maximum absorption of the incoming light by the solar cell. In order to obtain the best optical spacer thickness, we perform multiple simulations with different number of population, number of generations, mutation probability, number of bits, and selection and crossover methods. Our preliminary experiments show that the introduction of evolutionary algorithm result in a satisfactorily accurate search method when compared to brute-force. The future works on utilizing the full ability of evolutionary algorithm will be presented at the conference.
关键词： finite difference time domain,evolutionary algorithm,organic solar cell,optical modelling
更新于2025-09-12 10:27:22
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Super-Oscillatory Metalens at Terahertz for Enhanced Focusing with Reduced Side Lobes
摘要： In this paper, we design and numerically demonstrate an ultra-thin super-oscillatory metalens with a resolution below the diffraction limit. The zones of the lens are implemented using metasurface concepts with hexagonal unit cells. This way, the transparency and, hence, ef?ciency is optimized, compared to the conventional transparent–opaque zoning approach that introduces, inevitably, a high re?ection in the opaque regions. Furthermore, a novel two-step optimization technique, based on evolutionary algorithms, is developed to reduce the side lobes and boost the intensity at the focus. After the design process, we demonstrate that the metalens is able to generate a focal spot of 0.46λ0 (1.4 times below the resolution limit) at the design focal length of 10λ0 with reduced side lobes (the side lobe level being approximately ?11 dB). The metalens is optimized at 0.327 THz, and has been validated with numerical simulations.
关键词： metamaterials,evolutionary algorithm,metasurfaces,terahertz,metalens,terahertz focusing,subwavelength focusing,super-oscillatory lens
更新于2025-09-09 09:28:46
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Simultaneous Prediction of Atomic Structure and Stability of Nanoclusters in a Wide Area of Compositions
摘要： We present a universal method for the large-scale prediction of the atomic structure of clusters. Our algorithm performs the joint evolutionary search for all clusters in a given area of the compositional space and takes advantage of structural similarities frequently observed in clusters of close compositions. The resulting speedup is up to 50 times compared to current methods. This enables the first-principles studies of multi-component clusters with full coverage of a wide range of compositions. As an example, we report an unprecedented first-principles global optimization of 315 SinOm clusters with n ≤ 15 and m ≤ 20. The obtained map of Si-O cluster stability shows the existence of both expected (SiO2)n and unexpected (e.g. Si4O18) stable (magic) clusters, which can be important for miscellaneous applications.
关键词： atomic structure,nanoclusters,evolutionary algorithm,first-principles,stability
更新于2025-09-04 15:30:14
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Dose reconstruction from PET images in carbon ion therapy: a deconvolution approach
摘要： Dose and range verification have become important tools to bring carbon ion therapy to a higher level of confidence in clinical applications. Positron emission tomography is among the most commonly used approaches for this purpose and relies on the creation of positron emitting nuclei in nuclear interactions of the primary ions with tissue. Predictions of these positron emitter distributions are usually obtained from time-consuming Monte Carlo simulations or measurements from previous treatment fractions, and their comparison to the current, measured image allows for treatment verification. Still, a direct comparison of planned and delivered dose would be highly desirable, since the dose is the quantity of interest in radiation therapy and its confirmation improves quality assurance in carbon ion therapy. In this work, we present a deconvolution approach to predict dose distributions from PET images in carbon ion therapy. Under the assumption that the one-dimensional PET distribution is described by a convolution of the depth dose distribution and a filter kernel, an evolutionary algorithm is introduced to perform the reverse step and predict the depth dose distribution from a measured PET distribution. Filter kernels are obtained from either a library or are created for any given situation on-the-fly, using predictions of the β+-decay and depth dose distributions, and the very same evolutionary algorithm. The applicability of this approach is demonstrated for monoenergetic and polyenergetic carbon ion irradiation of homogeneous and heterogeneous solid phantoms as well as a patient computed tomography image, using Monte Carlo simulated distributions and measured in-beam PET data. Carbon ion ranges are predicted within less than 0.5 mm and 1 mm deviation for simulated and measured distributions, respectively.
关键词： evolutionary algorithm,PET imaging,range verification,carbon ion therapy,dose reconstruction
更新于2025-09-04 15:30:14