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Optimized Design of the Lithium Niobate for Spectrally-Pure-State Generation at MIR Wavelengths Using metaheuristic algorithm

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ADVANCED QUANTUM TECHNOLOGIES 2022年第7期5卷

作者： Cai, Wu-Hao Tian, Ying Wang, Shun You, Chenglong Zhou, Qiang Jin, Rui-Bo Wuhan Inst Technol Hubei Key Lab Opt Informat & Pattern Recognit Wuhan 430205 Peoples R China Louisiana State Univ Dept Phys & Astron Quantum Photon Lab Baton Rouge LA 70803 USA Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu 610054 Peoples R China Univ Elect Sci & Technol China Sch Optoelect Sci & Engn Chengdu 610054 Peoples R China Southern Univ Sci & Technol Guangdong Prov Key Lab Quantum Sci & Engn Shenzhen 518055 Peoples R China

Quantum light sources in the mid-infrared (MIR) band play an important role in many applications, such as quantum sensing, quantum imaging, and quantum communication. However, there is still a lack of high-quality quantum light sources in the MIR band, such as the spectrally pure single-photon source. In this work, the generation of a spectrally-pure state in an optimized poled lithium niobate crystal using a metaheuristic algorithm is presented. In particular, the particle swarm optimization algorithm is adopted to optimize the duty cycle of the poling period of the lithium niobate crystal. With this approach, the spectral purity can be improved from 0.820 to 0.998 under the third group-velocity-matched condition, and the wavelength-tunable range from 3.0 to 4.0 mu m for the degenerate case and 3.0 to 3.7 mu m for the nondegenerate case. This work paves the way for developing quantum photonic technologies at the MIR wavelength band.

关键词： lithium niobate crystals metaheuristic algorithm mid-infrared quantum light sources

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Drops on surface optimization (DSO): A new reinforcement learning based metaheuristic algorithm for virtual network functions placement in distributed cloud architectures

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COMPUTER NETWORKS 2022年 219卷

作者： Mohammadi, Marzieh Motamedi, S. Ahmad Sharifian, Saeed Amirkabir Univ Technol Fac Elect Engn Tehran Iran

With the goal of making the networks more agile, flexible and cost efficient, network function virtualization (NFV) is one of the latest and most promising technologies introduced. This technology is the key block of future internet and plays a critical role in modern networks. Using this paradigm, different network functions can be independently deployed on general purpose hardware instead of residing on dedicated hardware (network appliances in traditional networks). NFV enables faster development and lower costs. As any other new technology, NFV has some emerging challenges along with its benefits. The main research challenge of NFV is the necessity of optimally placing the virtual network functions (VNFs) in an NFV-enabled network. This problem is mainly known as the VNF placement (VNFP) problem in the literature. Due to the nature of network applications, this placement should be scalable for large network sizes. In this paper, the VNFP problem is addressed and a new metaheuristic algorithm is proposed based on the behavior of droplets on a surface, named the Drops on Surface Optimization (DSO) algorithm. In order to enhance the proposed algorithm by dynamic selection strategy, a reinforcement learning approach (Q learning) is adopted. A comprehensive evaluation is carried out using standard networks, compared with several methods. Simulation results reveal that our proposed algorithm outperforms other state-of-the-art approaches in terms of total end-to-end propagation delay of the placed service chain, cost of placement, number of active servers involved in the placement and scalability.

关键词： Cloud architecture metaheuristic algorithm Network function virtualization (NFV) Optimization Reinforcement learning Resource allocation

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Wolf-Bird Optimizer (WBO): A novel metaheuristic algorithm for Building Information Modeling-based resource tradeoff

Journal of Engineering Research (Kuwait)

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Journal of Engineering Research (Kuwait) 2024年

作者： Azizi, Mahdi Baghalzadeh Shishehgarkhaneh, Milad Basiri, Mahla Moehler, Robert C. Fang, Yihai Chan, Melissa Department of Civil Engineering University of Tabriz Iran Department of Civil Engineering Monash University Clayton 3800 VIC Australia Department of Infrastructure Engineering The University of Melbourne Melbourne Australia Institute for Sustainable Industries & Liveable Cities (ISILC) Victoria University Melbourne 3011 Australia College of Sport Health and Engineering Victoria University Melbourne 3011 Australia

In the animal kingdom, a mutually-beneficial ecosystemic coexistence and partnership in predation between wolves and ravens, known as the wolf-bird relationship, is observed in various cultures. The Wolf-Bird Optimizer (WBO), a novel metaheuristic algorithm inspired by this natural zoological relationship, is proposed. This method is developed based on the foraging behaviors of ravens and wolves, wherein the intelligence of ravens in finding prey and sending signals to wolves for assistance in hunting is considered. Furthermore, a framework for resource tradeoffs in project scheduling using metaheuristic algorithms and the Building Information Modeling (BIM) approach is established in this research. For statistical analysis, the algorithms are independently run 30 times with a preset stopping condition, enabling the calculation of descriptive statistical metrics such as mean, standard deviation (SD), and the required number of objective function evaluations. To ensure the statistical significance of the results, several inferential statistical methods, including the Kolmogorov-Smirnov, Wilcoxon, Mann-Whitney, and Kruskal-Wallis tests, are employed. Additionally, the capability of the proposed algorithm in solving resource tradeoff problems in four construction projects is assessed. The performance of the WBO algorithm is also evaluated in two benchmark construction projects, with the results indicating the algorithm's ability to produce competitive and exceptional outcomes regarding tradeoffs. © 2023 The Authors

关键词： Bioinspiration Evolutionary computation, Building information modeling metaheuristic algorithm Resource management Wolf-bird optimizer

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Ensemble of metaheuristic and Exact algorithm Based on the Divide-and-Conquer Framework for Multisatellite Observation Scheduling

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第5期58卷 4396-4408页

作者： Wu, Guohua Luo, Qizhang Du, Xiao Chen, Yingguo Suganthan, Ponnuthurai Nagaratnam Wang, Xinwei Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 119260 Singapore Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Qatar Univ Coll Engn KINDI Ctr Comp Res Doha Qatar Delft Univ Technol Dept Transport & Planning NL-2628 CN Delft Netherlands

Satellite observation scheduling plays a significant role in improving the efficiency of Earth observation systems. To solve the large-scale multisatellite observation scheduling problem, this article proposes an ensemble of metaheuristic and exact algorithms based on a divide-and-conquer framework (EHE-DCF), including a task allocation phase and a task scheduling phase. In the task allocation phase, each task is allocated to a proper orbit based on a metaheuristic incorporated with a probabilistic selection and a tabu mechanism derived from ant colony optimization and tabu search, respectively. In the task scheduling phase, we construct a task scheduling model for every single orbit and solve the model by using an exact method (i.e., branch and bound, B&B). The task allocation and task scheduling phases are performed iteratively to obtain a promising solution. To validate the performance of the EHE-DCF, we compare it with B&B, three divide-and-conquer-based metaheuristics, and a state-of-the-art metaheuristic. Experimental results show that the EHE-DCF can obtain higher scheduling profits and complete more tasks compared with existing algorithms. The EHE-DCF is especially efficient for large-scale satellite observation scheduling problems.

关键词： Task analysis Earth Observing System Satellites Orbits Processor scheduling Resource management Optimal scheduling Divide-and-conquer framework (DCF) ensemble algorithm exact algorithm metaheuristic algorithm observation satellite scheduling

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Ebola Optimization Search algorithm: A New Nature-Inspired metaheuristic Optimization algorithm

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IEEE ACCESS 2022年 10卷 16150-16177页

作者： Oyelade, Olaide Nathaniel Ezugwu, Absalom El-Shamir Mohamed, Tehnan I. A. Abualigah, Laith Univ KwaZulu Natal Sch Math Stat & Comp Sci Pietermaritzburg Campus ZA-3201 Kwa Zulu South Africa Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Pulau Pinang Malaysia

Nature computing has evolved with exciting performance to solve complex real-world combinatorial optimization problems. These problems span across engineering, medical sciences, and sciences generally. The Ebola virus has a propagation strategy that allows individuals in a population to move among susceptible, infected, quarantined, hospitalized, recovered, and dead sub-population groups. Motivated by the effectiveness of this strategy of propagation of the disease, a new bio-inspired and population-based optimization algorithm is proposed. This study presents a novel metaheuristic algorithm named Ebola Optimization Search algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease. First, we designed an improved SIR model of the disease, namely SEIR-HVQD: Susceptible (S), Exposed (E), Infected (I), Recovered (R), Hospitalized (H), Vaccinated (V), Quarantine (Q), and Death or Dead (D). Secondly, we represented the new model using a mathematical model based on a system of first-order differential equations. A combination of the propagation and mathematical models was adapted for developing the new metaheuristic algorithm. To evaluate the performance and capability of the proposed method in comparison with other optimization methods, two sets of benchmark functions consisting of forty-seven (47) classical and thirty (30) constrained IEEE-CEC benchmark functions were investigated. The results indicate that the performance of the proposed algorithm is competitive with other state-of-the-art optimization methods based on scalability, convergence, and sensitivity analyses. Extensive simulation results show that the EOSA outperforms popular metaheuristic algorithms such as the Particle Swarm Optimization algorithm (PSO), Genetic algorithm (GA), and Artificial Bee Colony algorithm (ABC). Also, the algorithm was applied to address the complex problem of selecting the best combination of convolutional neural network (CNN) hyperparameters in the image classi

关键词： Optimization Viruses (medical) Diseases metaheuristics Mathematical models Heuristic algorithms Statistics Ebola virus metaheuristic algorithm optimization problems constrained benchmark functions image classification convolutional neural network

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A novel approach for b-value optimization in Intravoxel Incoherent Motion Imaging using metaheuristic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 168卷 114270-114270页

作者： Raju, Jini Amma, Ushadevi C. John, Ansamma APJ Abdul Kalam Technol Univ Thangal Kunju Musaliar Coll Engn Dept Comp Sci & Engn Thiruvananthapuram Kerala India APJ Abdul Kalam Technol Univ Thangal Kunju Musaliar Coll Engn Dept Elect & Elect Engn Thiruvananthapuram Kerala India

Intravoxel Incoherent Motion (IVIM) based Magnetic Resonance Imaging (MRI) technique allows the quantitative evaluation of perfusion and diffusion without the use of contrast agents. The correctness of the diagnosis depends upon the accuracy and precision of IVIM parameter estimation. To achieve this, several diffusion weighted images must be acquired. The criteria of selection of diffusion weights varies among researchers. The diffusion weights are incorporated by a factor called 'b value', which reflects the intensity and duration of the diffusion gradient pulses used for imaging. IVIM imaging takes more time for image acquisition with multiple b values and the selection of the absolute b values as well as the number of b values, is a real challenge. As the b value count increases the scan time increases, which leads to increased patient discomfort and motion artifacts, resulting in poor image quality. Moreover, in most cases, these b values are found using trial and error methods during image acquisition. These issues can be addressed to a large extent by finding, optimum number and range of b values. In this paper, we propose a population based metaheuristic algorithm for arriving at the optimal b value count and the range of absolute b values for liver, which is an organ with high perfusion. Three separate models are developed to appropriately choose all possible b values ranging between 0 s/mm(2) and 850 s/mm(2), to observe the effects of diffusion and perfusion as well as to increase the global search space. The effect of low (0 s/mm(2) to 50 s/mm(2)), medium (55 s/mm(2) to 220 s/mm(2)) and high (230 s/mm(2) to 800 s/mm(2)) b values has been studied to find the optimal number of b values that can be used for IVIM imaging. In order to define a b value count that minimizes the error in IVIM parameters, simulation experiments are performed for different b value counts specifically 16, 14, 12, 10, 8, 6 and 4. For each of these experiments, repeated observations a

关键词： Diffusion weighted imaging Intravoxel Incoherent Motion Optimization metaheuristic algorithm

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Exploring the diffusion mechanism of Li ions in different modulated arrangements of La(1-X)/3LixNbO3 with fitted force fields obtained via a metaheuristic algorithm

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SOLID STATE IONICS 2021年 366卷 115662-115662页

作者： Yang, Zijian Ward, Robyn E. Tanibata, Naoto Takeda, Hayami Nakayama, Masanobu Kobayashi, Ryo Nagoya Inst Technol Dept Adv Ceram Showa Ku Nagoya Aichi 4668555 Japan Kyoto Univ Unit Elements Strategy Initiat Catalysts & Batter Saikyo Ku Kyoto 6158520 Japan Natl Inst Mat Sci NIMS MaDIS CMI2 1-2-1 Sengen Tsukuba Ibaraki 3050047 Japan Nagoya Inst Technol Frontier Res Inst Mat Sci FRIMS Showa Ku Nagoya Aichi 4668555 Japan Nagoya Inst Technol Dept Phys Sci & Engn Showa Ku Nagoya Aichi 4668555 Japan

As a potential solid-state electrolyte material, Li-containing A-site deficient perovskite oxides have attracted the attention of researchers because of their high Li-ion conductivity and the relationship between Li-ion conduction and structural characteristics, which has been intensively investigated. We have recently confirmed a quasiperiodic ordered arrangement of La and vacancies (Vac) at the perovskite A-sites of La(1-X)/3LixNbO3 (LLNO) using a combination of density functional theory (DFT), Monte Carlo simulations, and electron diffraction. Interestingly, two types of modulated arrangements, namely closed and striped structures, coexist in the La-rich layer, which affect Li-ion migration. In this study, DFT-derived force-field molecular dynamics (FFMD) simulations were performed to investigate the effect of a modulated structure on the migration behavior of Li ions in LLNO compounds. The results indicate that the type of modulated arrangements of La/Vac has a significant influence on the migration of Li ions. Moreover, the estimated diffusion coefficients of the modulated structures are higher by a factor of 10 than those of La/Vac disordered models at 800 K. The migration energy in the ab plane appeared to be much lower than along the c-axis, controlling the modulated arrangement of LLNO is beneficial to eliminate La-ion blockage during long-distance migration. Accordingly, the present study reveals that the controlling cation/Vac arrangement at perovskite A-sites is crucial for achieving high Li-ion conductivity. At the same time, the research scheme of this work is also applicable to other solid electrolyte materials, which provides research guidance for high-throughput material retrieval.

关键词： Perovskite Modulated structure Li-ion conduction metaheuristic algorithm Molecular dynamics

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Golden eagle optimizer: A nature-inspired metaheuristic algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 152卷 107050-107050页

作者： Mohammadi-Balani, Abdolkarim Nayeri, Mahmoud Dehghan Azar, Adel Taghizadeh-Yazdi, Mohammadreza Tarbiat Modares Univ Fac Management & Econ Dept Ind Management Jalal Al Ahmad HighwayPOB 14115-111 Tehran *** Iran Univ Tehran Fac Management Dept Ind Management Tehran Iran

This paper proposes a nature-inspired swarm-based metaheuristic for solving global optimization problems called Golden Eagle Optimizer (GEO). The core inspiration of GEO is the intelligence of golden eagles in tuning speed at different stages of their spiral trajectory for hunting. They show more propensity to cruise around and search for prey in the initial stages of hunting and more propensity to attack in the final stages. A golden eagle adjusts these two components to catch the best possible prey in feasible region the shortest possible time. This behavior is mathematically modeled to highlight exploration and exploitation for a global optimization method. The performance of the proposed algorithm is tested and confirmed using 33 benchmark test functions and a scalability test. Results were compared to that of six other well-known algorithms, which revealed GEO's superiority, which indicates that it can find the global optimum and avoid local optima effectively. The Multi-Objective Golden Eagle Optimizer (MOGEO) is also proposed to solve multi-objective problems. The performance of MOGEO is also tested and verified on ten multi-objective benchmark functions. Results were compared to that of two other multi-objective algorithms, which showed that it can approximate true Pareto optimal solutions better than the other two algorithms. The software (toolbox) and source code for GEO and MOGEO are also provided, which are publicly available.

关键词： Golden eagle optimizer Multi-objective golden eagle optimizer Nature-inspired computing Swarm intelligence metaheuristic algorithm

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Groundwater vulnerability mapping using the modified DRASTIC model: the metaheuristic algorithm approach

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2021年第1期193卷 25-25页

作者： Balaji, L. Saravanan, R. Saravanan, K. Sreemanthrarupini, N. A. Anna Univ Ctr Water Resources Chennai 600025 India

Vulnerability assessment and mapping is a significant tool for sustainable management of the precious natural groundwater resources. DRASTIC is an extensively used index model to map groundwater vulnerable zones. However, the original DRASTIC model rates and weights used in most of the research depict the poor correlation between nitrate concentration and groundwater vulnerability index. Wilcoxon test and five population-based metaheuristic (MH) algorithms, namely, firefly algorithm (FA), invasive weed optimization (IWO), teaching learning-based optimization (TLBO), shuffled frog leaping algorithm (SFLA), and particle swarm optimization (PSO), were used to optimize the rates and weights of the DRASTIC model to improve its accuracy. The performance of all the employed metaheuristic algorithms converges to a global optimal solution at different iterations, and to choose the best algorithm for DRASTIC weights optimization, a ranking methodology was proposed. The algorithms were ranked by calculating the relative closeness of alternatives with computational speed and the number of iterations as attributes in the TOPSIS method. This study identifies FA as the outperforming algorithm among the employed for this specified weight optimization problem based on ranking. The result of the optimization model proposed depicts significant improvement in the correlation coefficient between the groundwater vulnerability index and nitrate concentration from 0.0545 for the original DRASTIC model to 0.7247 for the Wilcoxon-MH- DRASTIC. Hence, this ranking approach can be adopted when global optimal solution is found by all employed algorithms in DRASTIC weight optimization.

关键词： Groundwater vulnerability DRASTIC model Wilcoxon test metaheuristic algorithm TOPSIS

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The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 80卷 20-34页

作者： Shadravan, S. Naji, H. R. Bardsiri, V. K. Islamic Azad Univ Kerman Branch Dept Comp Engn Kerman Iran Grad Univ Adv Technol Dept Comp Engn Kerman Iran

Nature-inspired optimization algorithms, especially swarm based algorithms (SAs), solve many scientific and engineering problems due to their flexibility and simplicity. These algorithms are applicable for optimization problems without structural modifications. This work presents a novel nature-inspired metaheuristic optimization algorithm, called SailFish Optimizer (SFO), which is inspired by a group of hunting sailfish. This method consists of two tips of populations, sailfish population for intensification of the search around the best so far and sardines population for diversification of the search space. The SFO algorithm is evaluated on 20 well-known unimodal and multimodal mathematical functions to test different characteristics of the algorithm. In addition, SFO is compared with the six state-of-art metaheuristic algorithms in low and high dimensions. It also indicates competitive results for improvement of exploration and exploitation phases, avoidance of local optima, and high speed convergence especially on large-scale global optimization. The SFO algorithm outperforms the best algorithms in the literature on the majority of the test functions and it shows the statistically significant difference among other algorithms. Moreover, the SFO algorithm shows significantly great results for non-convex, non-separable and scalable test functions. Eventually, the promising results on five real world optimization problems indicate that the SFO is applicable for problem solving with constrained and unknown search spaces.

关键词： Swarm optimization metaheuristic algorithm Sailfish optimization algorithm Constrained engineering optimization

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