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Data-driven Discrete Simulation-based Dynamic Modeling and continuous optimization for Comprehensive Carbon Efficiency of Batch Hobbing

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY 2025年第1期12卷 19-42页

作者： Yi, Qian Hu, Chunhui Li, Congbo Luo, Yusong Yi, Shuping Zhuo, Junkang Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China

Low-carbon manufacturing is an inevitable requirement for the green transformation of enterprises. For batch hobbing, continuous improvement of process parameters is an important way to achieve low-carbon optimization under the constraints of limited data and time-varying machining configurations. This is the research gap that needs to be filled. Therefore, in this paper, a dynamic modeling and continuous optimization method for comprehensive carbon efficiency (CCE) of hobbing based on data-driven discrete simulation is proposed. Specifically, the study integrates ML (meta-learning) and DEVS (discrete event system specification) in the hobbing process to create a dynamic model of CCE. The dynamic model combines the generalization of the data-driven approach and the capability to abstract events of the discrete simulation approach, which can autonomously adapt to the current machining configuration and output machining results in real time. On this basis, a modified multi-objective seagull optimization algorithm (MOSOA) is used for the continuous optimization of CCE in batch hobbing. Finally, the effectiveness and superiority of the proposed method are verified by a case study and comparative analysis. Moreover, this paper analyzes the effect of process parameters on CCE under different working conditions and provides guidance for gear hobbing.

关键词： Dynamic modeling Data-driven discrete simulation continuous optimization Meta-learning Batch hobbing

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Best Subset Solution Path for Linear Dimension Reduction Models Using continuous optimization

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BIOMETRICAL JOURNAL 2025年第1期67卷 e70015页

作者： Liquet, Benoit Moka, Sarat Muller, Samuel Macquarie Univ Sch Math & Phys Sci Sydney Australia Univ Pau & Pays Adour Lab Math & Leurs Applicat Pau France Univ New South Wales Sch Math & Stat Sydney Australia Univ Sydney Sch Math & Stat Sydney Australia

The selection of best variables is a challenging problem in supervised and unsupervised learning, especially in high-dimensional contexts where the number of variables is usually much larger than the number of observations. In this paper, we focus on two multivariate statistical methods: principal components analysis and partial least squares. Both approaches are popular linear dimension-reduction methods with numerous applications in several fields including in genomics, biology, environmental science, and engineering. In particular, these approaches build principal components, new variables that are combinations of all the original variables. A main drawback of principal components is the difficulty to interpret them when the number of variables is large. To define principal components from the most relevant variables, we propose to cast the best subset solution path method into principal component analysis and partial least square frameworks. We offer a new alternative by exploiting a continuous optimization algorithm for best subset solution path. Empirical studies show the efficacy of our approach for providing the best subset solution path. The usage of our algorithm is further exposed through the analysis of two real data sets. The first data set is analyzed using the principle component analysis while the analysis of the second data set is based on partial least square framework.

关键词： best subset solution path continuous optimization partial least square principal component sparsity

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continuous optimization via simulation using Golden Region search

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2011年第1期208卷 19-27页

作者： Kabirian, Alireza Olafsson, Sigurdur Univ Alaska Anchorage Coll Business & Publ Policy Anchorage AK 99508 USA Iowa State Univ Dept Ind & Mfg Syst Engn Ames IA 50011 USA

Simulation optimization (SO) is a class of mathematical optimization techniques in which the objective function can only be numerically evaluated through simulation. In this paper, a new SO approach called Golden Region (GR) search is developed for continuous problems. GR divides the feasible region into a number of (sub) regions and selects one region in each iteration for further search based on the quality and distribution of simulated points in the feasible region and the result of scanning the response surface through a metamodel. Monte Carlo experiments show that the GR method is efficient compared to three well-established approaches in the literature. We also prove the asymptotic convergence in probability to a global optimum for a large class of random search methods in general and GR in particular. (C) 2010 Elsevier B.V. All rights reserved.

关键词： continuous optimization Simulation Indifference Zone Probabilistic search Golden Region Metamodeling

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continuous optimization methods for ground-state searches of spin clusters

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POLYHEDRON 2011年第18期30卷 3218-3223页

作者： Oda, Akifumi Kawakami, Takashi Kitagawa, Yasutaka Okumura, Mitsutaka Takahashi, Ohgi Tohoku Pharmaceut Univ Fac Pharmaceut Sci Aoba Ku Sendai Miyagi 9818558 Japan Osaka Univ Dept Chem Grad Sch Sci Toyonaka Osaka 5600043 Japan

Computational methods were developed for ground-state searches of Heisenberg model spin clusters in which spin sites were represented by classical spin vectors. Simulated annealing, continuous genetic algorithm, and particle swarm optimization methods were applied for solving the problems. Because the results of these calculations were influenced by the settings of optimization parameters, effective parameter settings were also investigated. The results indicated that a continuous genetic algorithm is the most effective method for ground-state searches of Heisenberg model spin clusters, and that a mutation operator plays an important role in this genetic algorithm. These results provide useful information for solving physically or chemically important continuous optimization problems. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Spin cluster Heisenberg model Ground state continuous optimization Genetic algorithm

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continuous optimization methods for structure alignments

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MATHEMATICAL PROGRAMMING 2008年第1期112卷 93-124页

作者： Andreani, Roberto Martinez, Jose Mario Martinez, Leandro Yano, Flavio Univ Estadual Campinas IMECC Dept Appl Math BR-13081970 Campinas SP Brazil Univ Estadual Campinas Inst Chem Campinas Brazil

Structural Alignment is an important tool for fold identification of proteins, structural screening on ligand databases, pharmacophore identification and other applications. In the general case, the optimization problem of superimposing two structures is nonsmooth and nonconvex, so that most popular methods are heuristic and do not employ derivative information. Usually, these methods do not admit convergence theories of practical significance. In this work it is shown that the optimization of the superposition of two structures may be addressed using continuous smooth minimization. It is proved that, using a Low Order-Value optimization approach, the nonsmoothness may be essentially ignored and classical optimization algorithms may be used. Within this context, a Gauss-Newton method is introduced for structural alignments incorporating (or not) transformations (as flexibility) on the structures. Convergence theorems are provided and practical aspects of implementation are described. Numerical experiments suggest that the Gauss-Newton methodology is competitive with state-of-the-art algorithms for protein alignment both in terms of quality and speed. Additional experiments on binding site identification, ligand and cofactor alignments illustrate the generality of this approach. The softwares containing the methods presented here are available at http://***/similar to martinez/lovoalign.

关键词： Structural Alignment protein alignment Gauss-Newton method continuous optimization Order-Value optimization

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continuous optimization Benchmarks by Simulation 16th

Continuous Optimization Benchmarks by Simulation

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16th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Zaefferer, Martin Rehbach, Frederik TH Koln Inst Data Sci Engn & Analyt D-51643 Gummersbach Germany

ISBN: (纸本)9783030581114;9783030581121

Benchmark experiments are required to test, compare, tune, and understand optimization algorithms. Ideally, benchmark problems closely reflect real-world problem behavior. Yet, real-world problems are not always readily available for benchmarking. For example, evaluation costs may be too high, or resources are unavailable (e.g., software or equipment). As a solution, data from previous evaluations can be used to train surrogate models which are then used for benchmarking. The goal is to generate test functions on which the performance of an algorithm is similar to that on the real-world objective function. However, predictions from data-driven models tend to be smoother than the ground-truth from which the training data is derived. This is especially problematic when the training data becomes sparse. The resulting benchmarks may not reflect the landscape features of the ground-truth, are too easy, and may lead to biased conclusions. To resolve this, we use simulation of Gaussian processes instead of estimation (or prediction). This retains the covariance properties estimated during model training. While previous research suggested a decomposition-based approach for a small-scale, discrete problem, we show that the spectral simulation method enables simulation for continuous optimization problems. In a set of experiments with an artificial ground-truth, we demonstrate that this yields more accurate benchmarks than simply predicting with the Gaussian process model.

关键词： Simulation Benchmarking Test function continuous optimization Gaussian process regression Kriging

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continuous optimization Path of Hydraulic Engineering Project Management Based on TOC

Continuous Optimization Path of Hydraulic Engineering Projec...

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International Conference on Modern Hydraulic Engineering (CMHE)

作者： Jiang Xin Liu Xiaopei China Three Gorges Univ Coll Hydraul & Environm Engn Yichang 443002 Peoples R China

ISBN: (纸本)9781627485845

To successfully implement hydraulic engineering projects which are under the agent-construction system, the key is to constantly improve the project management standard of the agent-construction units. Conducting project post-evaluation is currently an effective method of improving project managing standard and achieving continuous optimization, but there are still some shortcomings in the practice of this method. By analyzing the shortcomings and introducing the advantages of TOC in constantly improving project management, the continuous optimization path of agent-construction units' project management is reconstructed. The path can effectively improve the management efficiency and the market competitiveness of agent-construction units. (c) 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Society for Resources, Environment and Engineering

关键词： TOC Hydraulic engineering Project management continuous optimization Path

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continuous optimization methods for structure alignments

Continuous optimization methods for structure alignments

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19th International Symposium on Mathematical Programming

关键词： Structural Alignment protein alignment Gauss-Newton method continuous optimization Order-Value optimization

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Artificial bee colony algorithm with variable search strategy for continuous optimization

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INFORMATION SCIENCES 2015年第1期300卷 140-157页

作者： Kiran, Mustafa Servet Hakli, Huseyin Gunduz, Mesut Uguz, Harun Selcuk Univ Fac Engn Dept Comp Engn TR-42075 Konya Turkey

The artificial bee colony (ABC) algorithm is a swarm-based optimization technique proposed for solving continuous optimization problems. The artificial agents of the ABC algorithm use one solution update rule during the search process. To efficiently solve optimization problems with different characteristics, we propose the integration of multiple solution update rules with ABC in this study. The proposed method uses five search strategies and counters to update the solutions. During initialization, each update rule has a constant counter content. During the search process performed by the artificial agents, these counters are used to determine the rule that is selected by the bees. Because the optimization problems and functions have different characteristics, one or more search strategies are selected and are used during the iterations according to the characteristics of the numeric functions in the proposed approach. By using the search strategies and mechanisms proposed in the present study, the artificial agents learn which update rule is more appropriate based on the characteristics of the problem to find better solutions. The performance and accuracy of the proposed method are examined on 28 numerical benchmark functions, and the obtained results are compared with various classical versions of ABC and other nature-inspired optimization algorithms. The experimental results show that the proposed algorithm, integrated and improved with search strategies, outperforms the basic variants and other variants of the ABC algorithm and other methods in terms of solution quality and robustness for most of the experiments. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Artificial bee colony continuous optimization Search strategy Integration

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An improved artificial bee colony algorithm for balancing local and global search behaviors in continuous optimization

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2020年第9期11卷 2051-2076页

作者： Hakli, Huseyin Kiran, Mustafa Servet Necmettin Erbakan Univ Dept Comp Engn TR-42090 Konya Turkey Konya Tech Univ Dept Comp Engn TR-42250 Konya Turkey

The artificial bee colony, ABC for short, algorithm is population-based iterative optimization algorithm proposed for solving the optimization problems with continuously-structured solution space. Although ABC has been equipped with powerful global search capability, this capability can cause poor intensification on found solutions and slow convergence problem. The occurrence of these issues is originated from the search equations proposed for employed and onlooker bees, which only updates one decision variable at each trial. In order to address these drawbacks of the basic ABC algorithm, we introduce six search equations for the algorithm and three of them are used by employed bees and the rest of equations are used by onlooker bees. Moreover, each onlooker agent can modify three dimensions or decision variables of a food source at each attempt, which represents a possible solution for the optimization problems. The proposed variant of ABC algorithm is applied to solve basic, CEC2005, CEC2014 and CEC2015 benchmark functions. The obtained results are compared with results of the state-of-art variants of the basic ABC algorithm, artificial algae algorithm, particle swarm optimization algorithm and its variants, gravitation search algorithm and its variants and etc. Comparisons are conducted for measurement of the solution quality, robustness and convergence characteristics of the algorithms. The obtained results and comparisons show the experimentally validation of the proposed ABC variant and success in solving the continuous optimization problems dealt with the study.

关键词： Artificial bee colony continuous optimization Numeric function Search strategy

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