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COVID-19: Estimation of the transmission dynamics in Spain using a stochastic simulator and black-box optimization techniques

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021年 211卷 106399-106399页

作者： Matabuena, Marcos Rodriguez-Mier, Pablo Garcia-Meixide, Carlos Leboran, Victor Univ Santiago de Compostela CiTIUS Ctr Singular Invest Tecnoloxias Intelixent Santiago De Compostela Spain Univ Toulouse Toxalim Res Ctr Food Toxicol INRAE ENVTINP PurpanUPS F-31300 Toulouse France Univ Santiago de Compostela Santiago De Compostela Spain

Background and objectives: Epidemiological models of epidemic spread are an essential tool for optimizing decision-making. The current literature is very extensive and covers a wide variety of deterministic and stochastic models. However, with the increase in computing resources, new, more general, and flexible procedures based on simulation models can assess the effectiveness of measures and quantify the cur-rent state of the epidemic. This paper illustrates the potential of this approach to build a new dynamic probabilistic model to estimate the prevalence of SARS-CoV-2 infections in different compartments. Methods: We propose a new probabilistic model in which, for the first time in the epidemic literature, parameter learning is carried out using gradient-free stochastic black-box optimization techniques simu-lating multiple trajectories of the infection dynamics in a general way, solving an inverse problem that is defined employing the daily information from mortality records. Results: After the application of the new proposal in Spain in the first and successive waves, the result of the model confirms the accuracy to estimate the seroprevalence and allows us to know the real dynamics of the pandemic a posteriori to assess the impact of epidemiological measures by the Spanish government and to plan more efficiently the subsequent decisions with the prior knowledge obtained. Conclusions: The model results allow us to estimate the daily patterns of COVID-19 infections in Spain retrospectively and examine the population's exposure to the virus dynamically in contrast to seropreva-lence surveys. Furthermore, given the flexibility of our simulation framework, we can model situations -even using non-parametric distributions between the different compartments in the model- that other models in the existing literature cannot. Our general optimization strategy remains valid in these cases, and we can easily create other non-standard simulation epidemic models that incorp

关键词： Epidemic models COVID-19 Computing science Stochastic processes evolutionary computations

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Adaptive simulation budget allocation in Simulation assisted Differential Evolution algorithm

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APPLIED SOFT COMPUTING 2019年 83卷 105678-000页

作者： Akl, Amany M. Sarker, Ruhul A. Essam, Daryl L. Univ New South Wales Canberra Sch Engn & Informat Technol Canberra ACT 2600 Australia

Recently, the literature on simulation assisted optimization for solving stochastic optimization problems has been considerably growing. In the optimization context, the population based meta-heuristics algorithms, such as, Differential evolutionary (DE), has shown tremendous success in solving continuous optimization problems. While in the simulation context, Monte-Carlo Simulation for sample average approximation is one of the successful approaches in handling the stochastic parameters of such problems. However, the intertwined computational burden, when combining these two approaches is amplified, and that encourages new research in this topic. In this problem, the challenge is to maintain high quality stochastic solutions by minimizing the computational cost to a reasonable level. To deal with this challenge, we propose a novel Adaptive Segment Based Scheme (ASBS) algorithm, for allocating the MCS budget in a Simulation assisted Differential Evolution (Sim-DE) Algorithm. This allows the algorithm to adaptively control the simulation budget based on a performance measure. The performance of the proposed ASBS algorithm is compared with other simulation budget allocation techniques while using the same base algorithm. The experimental study has been conducted by solving a modified set of IEEE-CEC'2006 test problems and a wind thermal power systems application. The experimental results reveal that the ASBS algorithm is able to substantially reduce the simulation budget, with an insignificant effect in solution quality. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Simulation optimization evolutionary computations Adaptive simulation budget Stochastic programming

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Differential evolution reveals the effect of polar and nonpolar solvents on carotenoids: A case study of astaxanthin optical response modeling

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SWARM AND evolutionary COMPUTATION 2022年 75卷

作者： Chesalin, Denis D. Kulikov, Eugene A. Yaroshevich, Igor A. Maksimov, Eugene G. Selishcheva, Alla A. Pishchalnikov, Roman Y. Russian Acad Sci Prokhorov Gen Phys Inst Vavilov Str 38 Moscow 119991 Russia Kurchatov Inst Natl Res Ctr Akadem Kurchatov Sq 1 Moscow 123182 Russia Lomonosov Moscow State Univ Fac Biol Dept Biophys Moscow 119991 Russia

The task to simulate realistic absorption spectra of electronic transitions in pigment molecules takes the form of the art of optimization, particularly if one must choose between the quality of the results obtained and the calculation time necessary to get such quality. The semiclassical theory of optical response provides us with a good opportunity to escape a so-called sum-over-state procedure of considering the innumerable number of vibronic states of a pigment by applying a correlation function approach. This approach, known as the multi -mode Brownian oscillator model (MBOM), involves replacing an infinite set of vibronic states, which interacts with an electronic transition, with a finite set of effective vibronic modes. Thus, the parameters of effective vibronic modes and an electronic transition can be found only by fitting experimental data. This situation gives rise to the following problem: as the number of free parameters increases, it becomes more difficult to find a single solution. To overcome this obstacle, differential evolution (DE), a method of global optimization, which is based on the idea of natural selection, seems to be the right choice. To test the efficiency of DE in solving quantum mechanical problems, we simulated absorption spectra of astaxanthin (AST) in 18 different organic solvents by using the MBOM. To do so, we developed software that implements both the DE algorithm and linear absorption modeling procedures. We have shown that simulating practically identical AST spectra, DE allows determining the effect of polar and nonpolar solvents on several parameters of MBOM of AST with high accuracy.

关键词： Differential evolution evolutionary computations Astaxanthin Linear optical response Absorption Carotenoids Multimode Brownian oscillator model

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evolutionary Optimizing Process Parameters in the Induction Hardening of Rack Bar by Response Surface Methodology and Desirability Function Approach under Industrial Conditions

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MATERIALS 2023年第17期16卷 5791-5791页

作者： Dziatkiewicz, Grzegorz Kuska, Krzysztof Popiel, Rafal Silesian Tech Univ Dept Computat Mech & Engn ul Konarskiego 18a PL-44100 Gliwice Poland HL Mando Corp Poland Ltd ul Uczniowska 36 PL-58306 Walbrzych Poland PST Consulting Ltd ul Biblioteczna 29-14 PL-43100 Tychy Poland

Conditions of industrial production introduce additional complexities while attempting to solve optimization problems of material technology processes. The complexity of the physics of such processes and the uncertainties arising from the natural variability of material parameters and the occurrence of disturbances make modeling based on first principles and modern computational methods difficult and even impossible. In particular, this applies to designing material processes considering their quality criteria. This paper shows the optimization of the rack bar induction hardening operation using the response surface methodology approach and the desirability function. The industrial conditions impose additional constraints on time, cost and implementation of experimental plans, so constructing empirical models is more complicated than in laboratory conditions. The empirical models of nine system responses were identified and used to construct a desirability function using expert knowledge to describe the quality requirements of the hardening operation. An analysis of the hypersurface of the desirability function is presented, and the impossibility of using classical gradient algorithms during optimization is empirically established. An evolutionary strategy in the form of a floating-point encoded genetic algorithm was used, which exhibits a non-zero probability of obtaining a global extremum and is a gradient-free method. Confirmation experiments show the improvement of the process quality using introduced measures.

关键词： induction hardening thermal residual deformation multiobjective optimization response surface methodology desirability function evolutionary computations genetic algorithm

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A cuckoo search algorithm with scale-free population topology

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 188卷 116049-116049页

作者： Tang, Cheng Song, Shuangbao Ji, Junkai Tang, Yajiao Tang, Zheng Todo, Yuki Univ Toyama Fac Engn Toyama 9308555 Japan Changzhou Univ Aliyun Sch Big Data Changzhou 213164 Jiangsu Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Cent South Univ Forestry & Technol Coll Econ Changsha 410004 Peoples R China Kanazawa Univ Fac Elect & Comp Engn Kanazawa Ishikawa 9201192 Japan

The scale-free network is an important type of complex network. The node degrees in a scale-free network follow the power-law distribution. In the skeleton of a scale-free network, there exists a few nodes which own huge neighborhood size and play an important role in information transmission of the entire network, while most of the network nodes have few connections whose influences of information exchange are limited to a relatively low level. In this paper, we introduce a scale-free population topology into the cuckoo search (CS) algorithm to propose a novel variant, which is termed the scale-free cuckoo search (SFCS) algorithm. Unlike other CS algorithms where the individuals exchange information randomly, two properties of the scale-free network can improve the SFCS algorithm in two aspects: the possibility that the information of competent individuals quickly floods the whole population is reduced significantly, which guarantees population diversity;and the corrupt individuals can learn from competent individuals with greater probability, which is beneficial for convergence. Thus, SFCS can obtain a better trade-off between exploitation and exploration during the search process. To evaluate the effectiveness of the proposed SFCS, 58 benchmark functions with different dimensions (10-D, 30-D, and 50-D), and 21 real-world optimization problems are employed in our experiment. We compare SFCS with the basic CS algorithm, two CS variants, and five state-of-the-art optimization algorithms, and the experimental results and statistical analysis verify the superiority of SFCS in terms of solution quality and convergence speed. Furthermore, we compare SFCS with a scale-free fully informed particle swarm optimization algorithm (SFIPSO) and the results prove our scale-free idea is effective despite its simplicity. We also introduce the scale-free population topology into the differential evolution (DE) and the firefly algorithm (FA) and the experimental results show that th

关键词： evolutionary computations Optimization Cuckoo search Scale-free network Population topology

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Tuning three-dimensional tumor progression simulations on a cluster of GPGPUs

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2022年 412卷

作者： Siwik, Leszek Los, Marcin Klusek, Adrian Paszynska, Anna Pingali, Keshav Dzwinel, Witold Paszynski, Maciej AGH Univ Sci & Technol Al Mickiewicza 30 PL-30059 Krakow Poland Jagiellonian Univ Krakow Poland Univ Texas Austin Oden Inst Computat & Engn Sci Austin TX 78712 USA

In the paper, we present the three-dimensional parallel simulator of tumor progression implemented for GPGPUs, together with an automatic model parameters tuning performed by evolutionary computations. We model the tumor growth by a set of Partial Differential Equations, describing the tumor density, tumor angiogenic factor, and damaged extra-cellular matrix, oxygen concentration, and a couple of auxiliary parameters, tumor pressure, tumor flux, and tumor cell sinks and sources. We also model the changes in the vasculature by the stochastic graph grammar model, expressing the angiogenesis process. We use the finite element method in the isogeometric analysis (IGA) context employing higher-order and continuity B-spline basis functions for approximation of the scalar fields modeling the tumor progression process. We show that replacing the traditional solver algorithm using the loop through elements into the alternative approach employing the loop through global basis functions enables for efficient parallelization on GPGPU. We also employ classical code optimization techniques, includes multithreading organization, memory access, and nesting and unrolling the loops. The experiments performed on the Prometheus supercomputer reported the speed-up of more than 171 times in comparison to analogous CPU simulator for the 256(3) problem size. We also employ the GPGPU code for the solution of an inverse problem of identification of model parameters for patient-specific data. To achieve this goal, we synchronize three different GPGPUs simulators. We use evolutionary algorithms to find a proper model and synchronization parameters matching the prescribed medical data. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Code design and optimization Isogeometric analysis Tumor progression simulations GPGPU computing Graph transformations evolutionary computations

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An objective reduction algorithm based on population decomposition and hyperplane approximation

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SWARM AND evolutionary COMPUTATION 2024年 86卷

作者： Yang, Ning Liu, Hai-Lin Xiao, Junrong Guangdong Univ Technol Sch Automat Guangzhou Peoples R China Guangdong Univ Technol Sch Math & Stat Guangzhou Peoples R China

Objective reduction is an efficient method to simplify many -objective optimization problems (MaOPs) with redundant objectives. However, most objective reduction algorithms operate on an entire sample set, which would easily omit local features and lead to an over -reduction of objectives. To alleviate the above problems, this paper proposes an objective reduction algorithm based on population decomposition and hyperplane approximation, denoted as PDHA, where the population is decomposed into several subpopulations, and a method based on hyperplane approximation is applied to extract the essential objectives from subpopulations. PDHA has two advantages. First, extracting essential objectives from the subpopulations could reduce errors produced by the reduction technique. Second, more attention is paid to local features via extracting the essential objectives from different subpopulations, which could prevent an over -reduction of objectives. The performance of PDHA is theoretically verified and experimentally compared with some state-of-theart objective reduction algorithms and some algorithms for MaOPs on some benchmark problems. The experimental results show that PDHA is effective for the objective reduction of objective -redundant MaOPs.

关键词： evolutionary computations Many-objective optimization Objective reduction Population decomposition Hyperplane approximation

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evolutionary computation technique enhancing the performance of cognitive radio networks with energy harvesting

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AD HOC NETWORKS 2020年 107卷 102254-102254页

作者： Abd El-Malek, Ahmed H. Aboulhassan, Mohamed A. Abdou, Mohamed A. Egypt Japan Univ Sci & Technol Sch Elect Commun & Comp Engn Alexandria Egypt Pharos Univ Alexandria PUA Dept Elect Engn Alexandria Egypt City Sci Res & Technol Applicat Informat Res Inst Alexandria Egypt

The intervention of evolutionary computation methods in studying energy harvesting (EH) techniques in multiple-input-single-output cognitive radio networks (MISO-CRNs) is carefully investigated. In this work, a secondary user (SU) with multiple antennas harvests energy from a hybrid base station (HBS) in addition to harvesting from the primary user (PU) transmission during the downlink time-slot. Hence, the SU transmits its data on the uplink over Nakagami-m fading channels. For the underlay paradigm, a specific tolerable PU interference limits the SU total transmission power. The HBS does not have previous information about the SU battery level. A closed-form expression for the SU exact outage probability is derived and simplified to its asymptotic formula for the high signal-to-noise-ratio (SNR) region. Closed-form expressions for the system average symbol error probability (ASEP) and ergodic capacity are obtained. The particle swarm optimization (PSO) algorithm is used to find the optimal SU transmission power to minimize the sophisticated formula of the exact outage probability. Simulations and numerical results reveal the effect of the critical system parameters on the overall system performance and validate the derived mathematical analysis. Moreover, the optimum solution of the power optimization problem shows noteworthy enhancement of the system performance compared to the traditional equally distributed model. (C) 2020 Elsevier B.V. All rights reserved.

关键词： evolutionary computations Cognitive radio Multiple antennas Nakagami-m Energy harvesting Power optimization Outage probability Average symbol error probability Ergodic capacity

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A hybrid genetic-particle swarm algorithm based on multilevel neighbourhood structure for flexible job shop scheduling problem

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COMPUTERS & OPERATIONS RESEARCH 2021年 135卷 105431-105431页

作者： Liu, Zhifeng Wang, Junlong Zhang, Caixia Chu, Hongyan Ding, Guozhi Zhang, Lu Jilin Univ Key Lab CNC Equipment Reliabil Minist Educ Changchun 130025 Peoples R China Beijing Univ Technol Inst Adv Mfg & Intelligent Technol Beijing 100124 Peoples R China Beijing Xinghang Electromech Equipment Co Ltd Beijing 10074 Peoples R China Beihang Univ Beijing 100191 Peoples R China China Acad Informat & Commun Technol Informatizat & Industrializat Integrat Res Inst Beijing 100191 Peoples R China

Reasonable job shop scheduling can improve the production efficiency and product delivery and reduce the costs and energy consumption. The quality of a scheduling scheme mainly depends on the performance of the used algorithm. Therefore, several researchers have attempted to improve the performance of algorithms used for solving the flexible job shop scheduling problem (FJSSP). Currently, the genetic algorithm (GA) is one of the most widely used algorithms for solving the FJSSP. However, it has a low convergence speed and accuracy. To overcome these limitations of the GA, a novel variable neighbourhood descent hybrid genetic algorithm (VNDhGA) is proposed here. In this algorithm, a barebones particle swarm optimisation (BBPSO)-based mutation operator, a hybrid heuristic initialisation strategy, and VND based on an improved multilevel neighbourhood structure are integrated into the standard GA framework to improve its convergence performance and solution accuracy. Furthermore, a real-number-based chromosome representation, coding, decoding, and crossover method is proposed for maximising the advantages of BBPSO. The proposed algorithm was tested on benchmark cases, and the results were compared with those of existing algorithms. The proposed algorithm exhibited superior solution accuracy and convergence performance than those of existing ones.

关键词： evolutionary computations Hybrid genetic algorithm Multilevel neighbourhood structure Variable neighbourhood descent Flexible job shop scheduling problem

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Wind farm layout optimization using adaptive evolutionary algorithm with Monte Carlo Tree Search reinforcement learning

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ENERGY CONVERSION AND MANAGEMENT 2022年 252卷 115047-115047页

作者： Bai, Fangyun Ju, Xinglong Wang, Shouyi Zhou, Wenyong Liu, Feng Tongji Univ Dept Management Sci & Engn Shanghai Peoples R China Univ Oklahoma Price Coll Business Norman OK 73019 USA Univ Texas Arlington Dept Ind Mfg & Syst Engn Arlington TX 76019 USA Stevens Inst Technol Sch Syst & Enterprises Hoboken NJ 07030 USA

Recent years have witnessed an enormous growth of wind farm capacity worldwide. Due to the wake effect, the velocity of incoming wind is reduced for the wind turbines in the downwind directions, thus causing discounted power generation in a wind farm. Previously, a self-informed adaptivity mechanism in evolutionary algorithms was introduced by the authors, which is inspired by the individuals' self-adaptive capability to fit the environment in the natural world, where relocating the worst wind turbine with a surrogate model informed mechanism was found to be effective in improving the power conversion efficiency. In this paper, the exploitation capability in the adaptive genetic algorithm is further improved by casting the relocation of multiple wind turbines into a single-player reinforcement learning problem, which is further addressed by Monte-Carlo Tree Search embedded within the evolutionary algorithm. In contrast to the moderate improvements of the authors' previous algorithms, significant improvement is achieved due to the enhanced algorithmic exploitation. The new algorithm is also applied to solve the optimal layout problem for a recently approved wind farm in New Jersey, and showed better performance against the benchmark algorithms.

关键词： evolutionary computations Adaptive genetic algorithm Reinforcement learning Monte-Carlo Tree Search Wind farm layout optimization

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