Search

Contraposing the problem of cutter wear and other issues in TBM boring in erosive and hard strata, this paper analyzes the boring parameters and cutter wear amount in the trial 2 km of TBM section in Qinling Tunnel of Han River to Wei River Water Diversion Project. Then several pieces of advice are proposed and analysis results suggest that the inadequate amount of cutters around No. 43 cutter causes their relatively high wear. Experiments of disc cutter breaking hard rock are conducted, showing that the specific energy of TBM used for breaking rock is relatively low while the efficiency high when the S/p (the ratio of cutter space to penetration) is between 25 and 30. Relevant scaled experiments indicate a critical penetration value beyond which the mass wear rate of disc cutter surges. The experiment also proves that round-blade disc cutter could effectively reduce the wear. The research could provide reference for the cutter-head design and parameter control of TBM in similar geologies.

Brain storm optimization (BSO) is a swarm intelligence optimization algorithm which is proven to have practical values in various fields. During these years, many modifications have been facilitated to effectively improve BSO's search performance. So far, these modifications focus on improving the solution quality by applying different clustering methods and learning strategies, in which the population diversity is often neglected. However, in recent studies, population diversity plays a more significant role in designing optimization algorithm. A population that maintains its diversity in a high level can easily obtain better solutions than the one with low level of diversity. Therefore, this paper proposes a control method that evaluates the population diversity of BSO to improve its performance. Two diversity measures, which are known as distance-based diversity and fitness-based diversity, are implemented to realize the adaptation of algorithm parameters. The new algorithm is called multiple diversity-driven BSO (MDBSO). Its performance is verified by CEC2017 benchmark function suit and a neuron model training task. The results demonstrate the effectiveness and efficiency of MDBSO.

Представлен гибридный метод глобальной оптимизации НАИЗ-PSO на основе нейросетевой аппроксимации инверсных зависимостей (координат от значений целевой функции) и метода роя частиц, служащий для нахождения глобального минимума непрерывной целевой функции многих переменных в области, имеющей вид многомерного параллелепипеда. Целевая функция рассматривается как абстрактная вычислительная процедура («чёрный ящик»). Метод использует группы пробных точек, движущихся как в методе роя частиц. Одна из возможных целей движения определяется через отображение пониженных значений целевой функции в координаты посредством модифицированных дуальных обобщённо-регрессионных нейронных сетей, конструируемых по пробным точкам. Параметрами процесса управляет эволюционный алгоритм. В алгоритме управления популяция состоит из эволюционирующих правил, заключающих в себе наборы параметров. Для оценки приспособленности особи используются две числовые характеристики: краткосрочная (очарование) и долгосрочная (достоинство). По очарованию правила отбираются для размножения и применения. Достоинством определяется выживание особи при формировании новой популяции. Двойная оценка правил решает проблему вымирания потенциально полезных особей при краткосрочном изменении ситуации. Преимущество эволюционного управления над случайным изменением параметров НАИЗ-PSO в процессе поиска, а также тенденция к уменьшению погрешности при повторном использовании базы правил показаны на тестовых задачах с целевыми функциями 100 переменных.

In this paper, we formulate a special epidemic dynamic model to describe the transmission of COVID-19 in Algeria. We derive the threshold parameter control reproduction number, and present the effective control reproduction number (Rc(t)) as a real-time index for evaluating the epidemic under different control strategies. Due to the limitation of the reported data, we redefine the number of accumulative confirmed cases with diagnostic shadow and then use the processed data to do the optimal numerical simulations. According to the control measures, we divide the whole research period into six stages. And then the corresponding medical resource estimations and the average effective control reproduction numbers for each stage are given. Meanwhile, we use the parameter values which are obtained from the optimal numerical simulations to forecast the whole epidemic tendency under different control strategies.

Evolutionary algorithms (EAs) are general-purpose optimisers that come with several parameters like the sizes of parent and offspring populations or the mutation rate. It is well known that the performance of EAs may depend drastically on these parameters. Recent theoretical studies have shown that self-adjusting parameter control mechanisms that tune parameters during the algorithm run can provably outperform the best static parameters in EAs on discrete problems. However, the majority of these studies concerned elitist EAs and we do not have a clear answer on whether the same mechanisms can be applied for non-elitist EAs. We study one of the best-known parameter control mechanisms, the one-fifth success rule, to control the offspring population size $\lambda$ in the non-elitist $(1,\lambda)$ EA. It is known that the $(1,\lambda)$ EA has a sharp threshold with respect to the choice of $\lambda$ where the expected runtime on the benchmark function OneMax changes from polynomial to exponential time. Hence, it is not clear whether parameter control mechanisms are able to find and maintain suitable values of $\lambda$. For OneMax we show that the answer crucially depends on the success rate $s$ (i.\,e.\ a one-$(s+1)$-th success rule). We prove that, if the success rate is appropriately small, the self-adjusting $(1,\lambda)$ EA optimises OneMax in $O(n)$ expected generations and $O(n \log n)$ expected evaluations, the best possible runtime for any unary unbiased black-box algorithm. A small success rate is crucial: we also show that if the success rate is too large, the algorithm has an exponential runtime on OneMax and other functions with similar characteristics., Comment: This is an extended version of a paper that appeared in the Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2021)

For severe sanding in the middle and late development stages of unconsolidated sandstone reservoirs, in order to address the conflicts such as more sandy wells, complex well condition and limited investment in sand control, Oil Region H prejudged the sanding condition, adjusted parameters for and performed preprocesses over wells with a high likelihood of sand production by using theoretical calculation and experimental analysis based on the field production technology. As an alternative, in order to enhance the field application of sanding management techniques, Oil Region H sought for the combination of wellbore sand control and formation sand control to specifically optimize the sanding management technique. A set of comprehensive sanding management techniques was ultimately derived from the integration of parameter control, wellbore sand drainage and formation sand control. Based on the application of traditional sand control techniques, these sanding management techniques are expected to provide a reference for maintaining a reasonable production management of sandy wells, provide a technical guarantee for production recovery of severe sandy wells, and achieve the highest productivity with the minimal investment.

In this study, a new parameter control scheme is proposed for the differential evolution algorithm. The developed linear bias reduction scheme controls the Lehmer mean parameter value depending on the optimization stage, allowing the algorithm to improve the exploration properties at the beginning of the search and speed up the exploitation at the end of the search. As a basic algorithm, the L-SHADE approach is considered, as well as its modifications, namely the jSO and DISH algorithms. The experiments are performed on the CEC 2017 and 2020 bound-constrained benchmark problems, and the performed statistical comparison of the results demonstrates that the linear bias reduction allows significant improvement of the differential evolution performance for various types of optimization problems.

In the era of Internet of things (IoT), the demand for wireless network (NW) that can accommodate the wide range of terminals at low cost is increasing. For this reason, the developments of low-power wide-area (LPWA) network is actively underway. In the future, this further increase in this developments is expected. In particular, since multiple LPWA systems which use sub-GHz unlicensed band are operated simultaneously in the same area, there is the concern that the radio interference problem between wireless communication systems conforming to different standards will become serious. Therefore, we are studying the analysis of radio wave environments using analysis results of radio interferences from other communication devices not managed by own NW, in addition to radio wave conditions from terminals and gateways managed by own NW. Furthermore, we are studying the communication parameter control method using the results of the radio wave environments analysis. No existing studies have examined such the communication parameter control. This paper presents results of studies on channel assignment, transmission power control, and spreading factor control in LPWA NW we have studied so far.

As for the batch process, it is needed to carry out a unique simulation for each group of parameters. The simplest method to achieve this goal is to uses the Simulink as the integration space. It cannot solve some complicated dynamic model set up in dynamic software. This paper introduces an intelligent batch program to enable the model solved in the dynamic software during the simulation. This paper also put forward the road equation for the suspended monorail, which has not been introduced before. The results of multi rigid body dynamics and vehicle bridge coupling dynamics are compared in this paper. It proves that the bridge vibration has little effect on the vehicle system. Through the batch process method, the effect of different parameters on the vehicle system could be obtained.

The OneMax problem, alternatively known as the Hamming distance problem, is often referred to as the "drosophila of evolutionary computation (EC)", because of its high relevance in theoretical and empirical analyses of EC approaches. It is therefore surprising that even for the simplest of all mutation-based algorithms, Randomized Local Search and the (1+1) EA, the optimal mutation rates were determined only very recently, in a GECCO 2019 poster. In this work, we extend the analysis of optimal mutation rates to two variants of the $(1+\lambda)$ EA and to the $(1+\lambda)$ RLS. To do this, we use dynamic programming and, for the $(1+\lambda)$ EA, numeric optimization, both requiring $\Theta(n^3)$ time for problem dimension $n$. With this in hand, we compute for all population sizes $\lambda \in \{2^i \mid 0 \le i \le 18\}$ and for problem dimension $n \in \{1000, 2000, 5000\}$ which mutation rates minimize the expected running time and which ones maximize the expected progress. Our results do not only provide a lower bound against which we can measure common evolutionary approaches, but we also obtain insight into the structure of these optimal parameter choices. For example, we show that, for large population sizes, the best number of bits to flip is not monotone in the distance to the optimum. We also observe that the expected remaining running time are not necessarily unimodal for the $(1+\lambda)$ EA$_{0 \rightarrow 1}$ with shifted mutation., Comment: This is an extended version of the paper accepted to the PPSN 2020 conference

IntroductionWe assessed the association between guideline adherence and outcomes of clinical parameter control and end-stage kidney disease (ESKD), and further studied the effect of parameter control on ESKD for Chinese patients with diabetic nephropathy (DN).Research design and methodsIn this retrospective study, 1128 patients with DN (15,374 patient-visit samples) diagnosed by renal biopsy were enrolled. Samples were classified as adherence and nonadherence based on whether prescribed drugs conformed to medication regimen and drug contraindication recommended by guidelines, including American Diabetes Association (ADA) and Chinese guidelines. Guideline adherence rate was calculated on all samples for antihyperglycemic, antihypertensive and lipid-lowering treatments. Clinical parameter control was compared after 3–6 months’ therapy between two groups by generalized estimating equation models. Time-dependent Cox models were applied to evaluate the influence of guideline adherence on ESKD. Latent class mixed model was used to identify distinct trajectories for parameters and their ESKD risks were compared using Cox proportional-hazards models.ResultsGuideline adherence rate of antihyperglycemic therapy was the highest, with 72.87% and 68.15% of samples meeting ADA and Chinese guidelines, respectively. Adherence was more likely to have good glycated hemoglobin A1c (HbA1c) control (ADA: OR 1.46, 95% CI 1.12 to 1.88; Chinese guideline: OR 1.42, 95% CI 1.09 to 1.85) and good blood pressure control (ADA: OR 1.35, 95% CI 1.03 to 1.78; Chinese guideline: OR 1.39, 95% CI 1.08 to 1.79) compared with nonadherence. The improvement of patient’s adherence showed the potential to reduce ESKD risk. For proteinuria, low-density lipoprotein cholesterol (LDL-C), systolic blood pressure and uric acid, patients in higher-value trajectory group had higher ESKD risk. Proteinuria and LDL-C trajectories were most closely related to ESKD risk, while the risk was not significantly different in HbA1c trajectories.ConclusionsGuideline adherence and good control of proteinuria and LDL-C in clinical practice are important and in need for improving clinical outcomes in patients with DN.

It is well known that evolutionary algorithms (EAs) achieve peak performance only when their parameters are suitably tuned to the given problem. Even more, it is known that the best parameter values can change during the optimization process. Parameter control mechanisms are techniques developed to identify and to track these values. Recently, a series of rigorous theoretical works confirmed the superiority of several parameter control techniques over EAs with best possible static parameters. Among these results are examples for controlling the mutation rate of the $(1+\lambda)$~EA when optimizing the OneMax problem. However, it was shown in [Rodionova et al., GECCO'19] that the quality of these techniques strongly depends on the offspring population size $\lambda$. We introduce in this work a new hybrid parameter control technique, which combines the well-known one-fifth success rule with Q-learning. We demonstrate that our HQL mechanism achieves equal or superior performance to all techniques tested in [Rodionova et al., GECCO'19] and this -- in contrast to previous parameter control methods -- simultaneously for all offspring population sizes $\lambda$. We also show that the promising performance of HQL is not restricted to OneMax, but extends to several other benchmark problems., Comment: To appear in the Proceedings of Parallel Problem Solving from Nature (PPSN'2020)

Recently, a kind of heuristic optimization algorithm named gravitational search algorithm (GSA) has been rapidly developed. In GSA, there are two main parameters that control the search process, namely, the number of applied agents (Kbest) and the gravity constant (G). To balance exploration and exploitation, a fuzzy system containing twelve fuzzy rules is proposed to intelligently control the parameter setting of the GSA. The proposed method can enhance the convergence ability and yield better optimization results. The performance of fuzzy GSA (FGSA) is examined by fifteen benchmark functions. Extensive experimental results are tested and compared with those of the original GSA, CGSA, CLPSO, NFGSA, PSGSA and EKRGSA.

We simulate the formation and dynamics of calcium waves in a distributed astrocyte network. We take into account volume effects induced by the real morphology of an astrocyte cell. Astrocytes are sponge-like structures which do not occupy the entire volume of its compartment: astrocyte leaflets are surrounded by the neuropil. We introduce the astrocyte volume fraction (AVF) parameter which shows the part of the 2D template occupied by an astrocyte versus neuropil and correlates with this ratio in a real 3D structure. To describe the difference in the processes in branchlets and leaflets of an astrocyte and the continuous transition between them we use the surface-to-volume ratio (SVR) parameter.

Pruning is the popular framework for preventing the dilemma of overfitting noisy data. This paper presents a new hybrid Ant-Miner classification algorithm and ant colony system (ACS), called ACS-AntMiner. A key aspect of this algorithm is the selection of an appropriate number of terms to be included in the classification rule. ACS-AntMiner introduces a new parameter called importance rate (IR) which is a pre-pruning criterion based on the probability (heuristic and pheromone) amount. This criterion is responsible for adding only the important terms to each rule, thus discarding noisy data. The ACS algorithm is designed to optimize the IR parameter during the learning process of the Ant-Miner algorithm. The performance of the proposed classifier is compared with related ant-mining classifiers, namely, Ant-Miner, CAnt-Miner, TACO-Miner, and Ant-Miner with a hybrid pruner across several datasets. Experimental results show that the proposed classifier significantly outperforms the other ant-mining classifiers.

The present work analyses the state and development prospects of metrological support and standardisation in the field of measuring surface density, thickness and chemical composition of coatings. The main characteristics of the State Primary Measurement Standard GET 168-2015 and developed multiparametric reference materials are described.

Among the existing techniques to improve the performance of metaheuristics in optimization problems, adaptive parameter control consists in varying one or more parameters of a given metaheuristic according to some indicator of the search conditions. This approach allows metaheuristics to change algorithmic behaviour during the search, and is particularly relevant for the optimization of dynamic problems. In this research we theoretically analyse in which ways the parameters of the ant colony optimization for continuous domains metaheuristic can be adapted, regarding how each parameter influences exploration and exploitation characteristics of the algorithm. Our experimental contributions include validating the colony success rate as a search condition estimator and choosing suitable maps from this estimator to the parameters q and \(\xi \) of the algorithm. Beyond that, we compare the performances of three proposed adaptive versions of the base metaheuristic and show the benefits of simultaneously adapting multiple parameters.

Differential evolution is one of the most popular evolutionary algorithms for continuous optimization. In this paper, we introduce a new algorithm named the adaptive differential evolution with ensembling populations. In the proposed algorithm, two sets of mutation and crossover operators are utilized to generate offspring to better balance the exploitation and exploration abilities of the algorithm. Besides, an adaptive parameter control strategy is integrated to dynamically adjust the parameter setting of the algorithm so as to further improve the search efficacy. In the experimental studies, it is demonstrated that the proposed algorithm presents competitive performance on benchmark functions as well as on the real-world wireless sensor localization application, in terms of global search ability and search efficiency.

Multi-objective evolutionary algorithm based on decomposition (MOEA/D) has been successfully applied in solving multi-objective optimization problems. However, the performance of MOEA/D could be severely influenced by its parameter settings. In this paper, we introduce reinforcement learning into MOEA/D as a generic parameter controller. The resulting algorithm, reinforcement learning enhanced MOEA/D (RL-MOEA/D), is used to adaptively control the neighborhood size T and the differential evolutionary operators used in MOEA/D. RL-MOEA/D is first compared with MOEA/D with a random parameter control mechanism and MOEA/Ds with some fixed parameter settings on ten widely used multi-objective test instances. Then, RL-MOEA/D is compared with FRRMAB to show the effectiveness of the proposed algorithm. The experimental results indicate that RL-MOEA/D is very competitive. Finally, the characteristics of RL-MOEA/D are studied.

Electrospinning can produce nanofibers with extremely high surface-to-volume ratio and well tunable properties. The technique has been widely used in different disciplines. To fabricate fibers with required properties, parameters of fabrication should be well controlled and adjusted according to specific applications. Modification of electrospinning devices to align fibers in highly ordered architectures could improve their functions. Enhanced efficiency have also been obtained through the upscaling modification of spinnerets. With the outstanding efficiency, electrospinning has exhibited huge potentials to construct various nanostructures, such as artificial vessel, membrane for desalination and so on.

In this paper, we study the existence and bifurcation of subharmonic solutions of a four-dimensional slow–fast system with time-dependent perturbations for the unperturbed system in two cases: one is a Hamilton system and the other has a singular periodic orbit, respectively. We perform the curvilinear coordinate transformation and construct a Poincare map for both cases. Then some of sufficient conditions and necessary conditions of the existence and bifurcation of subharmonic solutions are obtained by analyzing the Poincare map. We apply them to study the bifurcation of multiple subharmonic solutions of a honeycomb sandwich plate dynamics system and to discuss the number of subharmonic solutions in different bifurcation regions induced by two parameters. The maximum number of subharmonic solutions of the honeycomb sandwich plate system is 7 and the relative parameter control condition is obtained.

Considering a substantial increase in the international seaborne containerized trade volumes, marine container terminal operators have to improve efficiency of the processes inside their terminals in order to meet the growing demand. An efficient berth scheduling is of a high importance for the terminal’s performance, as it significantly influences the turnaround time of vessels. This paper proposes a novel Evolutionary Algorithm to assist with berth scheduling at marine container terminals that, unlike published to date studies on berth scheduling, applies a parameter control strategy. Specifically, an adaptive mechanism is developed for the mutation operator, in which the mutation rate is altered based on feedback from the search. The objective of the proposed mixed integer model aims to minimize the total weighted vessel service cost. A set of numerical experiments are conducted to assess performance of the developed algorithm based on a comparison against a typical Evolutionary Algorithm that applies a constant mutation rate value, determined from the parameter tuning analysis. Results indicate that the optimality gap does not exceed 0.80% for both algorithms. Furthermore, deployment of the adaptive mechanism for the mutation operator yields an average of 5.4% and 8.5% savings in terms of the total weighted vessel service cost for medium and large size problem instances, respectively, without a significant increase in the computational time.