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optimization of drilling parameters using combined multi-objective method and presenting a practical factor

COMPUTERS & GEOSCIENCES 2023年第1期175卷

作者： Delavar, Mohammad Reza Ramezanzadeh, Ahmad Gholami, Raoof Sanei, Manouchehr Shahrood Univ Technol Dept Min Petr & Geophys Engn Shahrood Iran Univ Stavanger Dept Energy Resources Stavanger Norway Yazd Univ Dept Min & Met Engn Yazd Iran

Several optimization approaches have been developed to enhance the efficiency of drilling performance. The characteristics of the problem require a procedure capable of a precise definition of the model while optimizing the factors. Therefore, a hybrid machine learning approach was proposed using random forest (RF) and multiobjective particle swarm optimization (MOPSO) to optimize the drilling factors. The objectives were to achieve a higher rate of penetration (ROP) and lower torque on bit (TOB). The geomechanical and energy-based features were involved in the settings of the optimization process. At first, a dataset was made consisting of four wells and 7231 points with carbonated reservoirs. Next, the inputs including vital controllable parameters were preprocessed based on requirements to reveal their relationships with the targets. The flow rate of tools was the most influential factor as suggested by RF's permutation analysis. The process followed with proposing the models of ROP and TOB through the training section using RF learning. The models of training and testing parts showed a correlation coefficient of about 0.97 and 0.84 for the TOB and ROP of wells, respectively. The expression of the constraints and cost functions is of crucial significance in every optimization scheme. Here, the cost functions were established via RF models and constraints defined as the ratio of drilling efficiency (DE) to weight on bit (WOB). DE is related to confined compressive strength (CCS) and mechanical specific energy (MSE) thus, the combined RF-MOPSO is implemented by specifications of the formations and drilling aspects. The introduced practical ratio revealed the zones with the least drilling efficiency, devoted to the testing section and optimization target. As outcomes of RF-MOPSO, the ROP was considerably improved by 43.8% while TOB showed a reduction to 9% on average of the wells. Another advantage of the multi-objective optimization corresponded to factors of means

关键词： Drilling optimization Hybrid machine learning Random forest multi -objective optimization Rate of penetration Torque on bit

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Optimizing Forest Management: Balancing Environmental and Economic Goals Using Game Theory and multi-objective approaches

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FORESTS 2024年第11期15卷 2044页

作者： Amiri, Neda Limaei, Soleiman Mohammadi Univ Guilan Fac Nat Resources Dept Forestry Sowmeh Sara *** Iran Mid Sweden Univ Dept Econ Geog Law & Tourism Sundsvall Sweden

Forests are complex ecosystems that require integrated management to balance economic, social, and environmental dimensions. Conflicting objectives among stakeholders make optimal decision-making particularly challenging. This study seeks to balance the economic gains of forest harvesting with the goals of environmental conservation, with a focus on the Shafarood forest in Northern Iran. We applied multi-objective optimization and game theory to maximize the net present value (NPV) of forest harvesting while enhancing carbon sequestration. The research utilized data on stumpage prices, harvesting costs, tree density, volume per ha, growth rates, interest rates, carbon sequestration, and labour costs. Applying the epsilon-constraint method, we derived Pareto optimal solutions for a bi-objective model, and game theory was applied to negotiate between economic and environmental stakeholders. In the fifth round of bargaining, a Nash equilibrium was achieved between the two players. At this equilibrium point, the economic player achieved NPV from forest harvesting of 9001.884 (IRR 10,000/ha) and amount of carbon sequestration of 159.9383 tons/ha. Meanwhile, the environmental player achieved NPV from forest harvesting of 7861.248 (IRR 10,000/ha), along with a carbon sequestration of 159.9731 tons/ha. Results indicate significant trade-offs but reveal potential gains for both economic and environmental goals. These findings provide a robust framework for sustainable forest management and offer practical tools to support informed decision-making for diverse stakeholders.

关键词： sustainable forest management economic benefits environmental impact decision-making multi-objective optimization game theory Nash equilibrium carbon sequestration stakeholder alignment

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Heuristics for multi-objective Service System Designing 16

Heuristics for Multi-objective Service System Designing

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16th IEEE International Scientific Conference on Informatics, Informatics 2022

作者： Kvet, Marek University of Žilina Faculty of Management Science and Informatics Žilina010 26 Slovakia

ISBN: (纸本)9798350310344

This paper brings a short overview of heuristics developed for solving the problem of multi-objective service system designing. Even though there are plenty of effective exact and approximate solving tools for different mathematical models, they are usually not able to manage more than one objective function simultaneously. Furthermore, multi-criteria optimization assumes producing a set of non-dominated solutions instead of one resulting center deployment. Since obtaining the complete Pareto front of non-dominated solutions is extremely demanding, the development of efficient heuristics is necessary from the practical point of view. In this paper, the quality of the resulting set of solutions is studied together with the dynamics of the heuristic itself. All suggested approaches are experimentally verified making use of a dataset from real world. © 2022 IEEE.

关键词： multiobjective optimization

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Pretomanid dose selection for pulmonary tuberculosis: An application of multi-objective optimization to dosage regimen design

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CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021年第3期10卷 211-219页

作者： Lyons, Michael A. Colorado State Univ Mycobacteria Res Labs Dept Microbiol Immunol & Pathol 1682 Campus Delivery Ft Collins CO 80523 USA

Clinical development of combination chemotherapies for tuberculosis (TB) is complicated by partial or restricted phase II dose-finding. Barriers include a propensity for drug resistance with monotherapy, practical limits on numbers of treatment arms for component dose combinations, and limited application of current dose selection methods to multidrug regimens. A multi-objective optimization approach to dose selection was developed as a conceptual and computational framework for currently evolving approaches to clinical testing of novel TB regimens. Pharmacokinetic-pharmacodynamic (PK-PD) modeling was combined with an evolutionary algorithm to identify dosage regimens that yield optimal trade-offs between multiple conflicting therapeutic objectives. The phase IIa studies for pretomanid, a newly approved nitroimidazole for specific cases of highly drug-resistant pulmonary TB, were used to demonstrate the approach with Pareto optimized dosing that best minimized sputum bacillary load and the probability of drug-related adverse events. Results include a population-typical characterization of the recommended 200 mg once daily dosage, the optimality of time-dependent dosing, examples of individualized therapy, and the determination of optimal loading doses. The approach generalizes conventional PK-PD target attainment to a design problem that scales to drug combinations, and provides a benefit-risk context for clinical testing of complex drug regimens.

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Energy aware scheduling in flexible flow shops with hybrid particle swarm optimization

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COMPUTERS & OPERATIONS RESEARCH 2021年 125卷 105088-105088页

作者： Ding, Junwen Schulz, Sven Shen, Liji Buscher, Udo Lu, Zhipeng Huazhong Univ Sci & Technol Wuhan Peoples R China Tech Univ Dresden Chair Business Management Esp Ind Management Dresden Germany WHU Otto Beisheim Sch Management Vallendar Germany

This paper integrates energy awareness in the flexible flow shop scheduling system, where two objectives are minimized simultaneously: total tardiness and electric power costs. We also consider practical settings including variable processing speeds and time-of-use (TOU) electricity prices. A novel hybrid particle swarm optimization (HPSO) algorithm is developed which incorporates several distinguishing features: Particles are represented based on job operation and machine assignment, which are updated directly in the discrete domain. More importantly, we introduce a multi-objective tabu search procedure and a position based crossover operator to balance global exploration and local exploitation. Experiments are conducted to verify the performance of the proposed HPSO algorithm compared to the well-known approaches in the literature. Results show the significance of HPSO in terms of the number and quality of non-dominated solutions and computational efficiency. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Flexible flow shop Energy aware scheduling multi-objective optimization TOU tariffs

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Reliability-based design optimization of a cradle-type double rotary table for 5-axis machining center

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MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2025年

作者： Ma, Mingfei Huang, Xianzhen Liu, Hongqiang Wu, Xiaojie Hua, Chunlei Zhang, Yimin Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China Genertec Machine Tool Engn Res Inst CO LTD Shenyang Peoples R China Zhaoqing Univ Coll Mech & Automot Engn Zhaoqing Peoples R China

In practical engineering, the inevitable presence of certain uncertainties in target systems due to differences in material properties, inaccurate characterization of load conditions, and manufacturing errors. Therefore, this paper considers the influence of random factors and defines the ability of the rotary table to resist deformation under load as the reliability of the rotary table system. We propose a reliability-based design optimization (RBDO) model that comprehensively considers mass, mechanical performance, and carbon emissions. First, a mechanical performance model and a carbon emission model for the cradle-type double rotary table have been established. Considering the assembly relationships within the internal structure of the rotary table, the sensitivity indicators for each component's structural parameters are obtained through sensitivity analysis, and these indicators are used to guide the optimization process. During the optimization process, an adaptive Kriging surrogate model is employed to reconstruct the functional relationship between the structural parameters of the rotary table and its performance. Given the high dimensionality and wide range of the design variables, a multi-stage optimization method has been proposed to overcome the impact of training data density on the fitting accuracy of the surrogate model and improve the convergence effect of the optimization. The optimization results indicate that the multi-stage optimization method can achieve better outcomes in a shorter time compared to traditional optimization approaches. Moreover, compared to deterministic design optimization, RBDO significantly enhances the reliability of the system, providing a new solution for the optimized design of the double rotary table and other complex mechanical structures.

关键词： Cradle-type double rotary table low-carbon design multi-objective optimization reliability assessment rigidity

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Emerging Technologies for Advanced Power Electronics and Machine Design in Electric Drives

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APPLIED SCIENCES-BASEL 2024年第24期14卷 11559页

作者： Rihar, Andraz Nemec, Mitja Lavric, Henrik Zajec, Peter Voncina, Danjel Nedeljkovic, David Ambrozic, Vanja Drobnic, Klemen Univ Ljubljana Fac Elect Engn Dept Mechatron Ljubljana 1000 Slovenia

The paper presents a comprehensive overview of recent advancements in power electronics and electric machine design, focusing on novel topologies, semiconductor technologies, and integrated design techniques for electric drives. New drive topologies are gradually moving from the research phase to practical application, aiming to increase the rated power, efficiency, and reliability of electric drives. Specifically, these topologies can be categorized into series, which focus on increasing the operating voltage;parallel, which aim at enhancing the operating current and adding redundancy;and multiphase, known for offering significant benefits such as improved fault tolerance, higher torque generation, the possibility of synthetic loading, and diverse winding layout options. Emerging wide bandgap semiconductors, such as silicon carbide and gallium nitride, allow for operation at higher frequencies and lower power losses, enabling further drive integration. In terms of design practices, higher computational power, supported by advanced software, enables simulation and analysis in multiple domains (thermal, mechanical, electromagnetic) using multiphysics co-simulation, as well as multi-objective optimization concepts to achieve rapid prototyping of optimized drive systems. All the approaches described are important steps towards further improving electric drives for numerous applications in industry, consumer electronics, and transportation.

关键词： multiphase machines multilevel converters semiconductors design (co-)simulation multi-objective optimization

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SceneNet: Remote sensing scene classification deep learning network using multi-objective neural evolution architecture search

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2021年 172卷 171-188页

作者： Ma, Ailong Wan, Yuting Zhong, Yanfei Wang, Junjue Zhang, Liangpei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430074 Peoples R China

The scene classification approaches using deep learning have been the subject of much attention for remote sensing imagery. However, most deep learning networks have been constructed with a fixed architecture for natural image processing, and they are difficult to apply directly to remote sensing images, due to the more complex geometric structural features. Thus, there is an urgent need for automatic search for the most suitable neural network architecture from the image data in scene classification, in which a powerful search mechanism is required, and the computational complexity and performance error of the searched network should be balanced for a practical choice. In this article, a framework for scene classification network architecture search based on multi-objective neural evolution (SceneNet) is proposed. In SceneNet, the network architecture coding and searching are achieved using an evolutionary algorithm, which can implement a more flexible hierarchical extraction of the remote sensing image scene information. Moreover, the computational complexity and the performance error of the searched network are balanced by employing the multi-objective optimization method, and the competitive neural architectures are obtained in a Pareto solution set. The effectiveness of SceneNet is demonstrated by experimental comparisons with several deep neural networks designed by human experts.

关键词： Scene classification deep neural network remote sensing multi-objective optimization evolutionary algorithm neural architecture search

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Conditional probability based multi-objective cooperative task assignment for heterogeneous UAVs

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartC期123卷

作者： Gao, Xiaohua Wang, Lei Yu, Xinyong Su, Xichao Ding, Yu Lu, Chen Peng, Haijun Wang, Xinwei Dalian Univ Technol Sch Math Sci Dalian 116024 Liaoning Peoples R China Naval Aeronaut & Astronaut Univ Dept Airborne Vehicle Engn Yantai 264001 Peoples R China Sci & Technol Reliabil & Environm Engn Lab Beijing 100191 Peoples R China Beihang Univ Inst Reliabil Engn Beijing 100191 Peoples R China Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China Dalian Univ Technol Dept Engn Mech State Key Lab Struct Anal Optimizat & CAE Software Dalian 116024 Liaoning Peoples R China

In actual air combat, there is an inevitable risk that an unmanned aerial vehicle (UAV) will be destroyed. However, this risk is rarely considered in the mission planning phase. In this paper, we focus on cooperative mission assignment for heterogeneous UAVs. We develop a multi-objective optimization model to find a balance between mission gains and UAV losses. The objective function is expressed using conditional probability theory by introducing the probabilities of mission success and UAV loss. Munitions loading capacity, time constraints, and priority constraints are modeled as constraints. To solve this combinatorial problem, an improved multi -objective genetic algorithm, which incorporates a natural chromosome encoding format and specially designed genetic operators, is developed. An efficient unlocking method is constructed to address the unavoidable dead-lock phenomenon meanwhile maintaining the population randomness. Numerical simulations for different problem sizes and ammunition stocks are performed, and the proposed algorithm is compared with the multi -objective Particle Swarm optimization and the multi-objective Grey Wolf optimization, respectively, using different unlocking approaches. The simulation and comparison results demonstrate the practical value and effectiveness of the developed model and the proposed algorithm.

关键词： Unmanned aerial vehicle Cooperative task assignment multi-objective optimization Genetic algorithm

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A Digital Twin Framework With Meta- and Transfer Learning for Scalable multi-Machine Modeling and optimization in Semiconductor Manufacturing

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IEEE Access 2025年 13卷 84380-84398页

作者： Lin, Chin-Yi Tseng, Tzu-Liang Tsai, Tsung-Han Department of Industrial Manufacturing and Systems Engineering The University of Texas at EL Paso El PasoTX79968 United States Institute of Information and Decision Sciences National Taipei University of Business Taipei10051 Taiwan

Despite recent advances in Digital Twin (DT) technologies for semiconductor manufacturing, no existing research convincingly demonstrates a unified, rapidly scalable, and data-efficient DT framework that can effectively handle stringent multi-objective optimization under severe data scarcity. This absence of a comprehensive approach impedes widespread industry adoption, given the pressing need for flexible, universal solutions that rapidly adapt to diverse machines and processes. This study introduces MOODFG-MLTL, an innovative algorithm that integrates Meta-Learning and Transfer Learning within a multi-objective optimization using Deep-Feature Gaussian Processes (MOODFG) architecture. By harnessing a centralized meta-model repository and dynamically refining surrogate models, the proposed framework efficiently leverages limited data and transfers knowledge across heterogeneous manufacturing configurations. In our experimental validation with Epi SiC processes, MOODFG-MLTL achieves near-optimal wafer thickness and doping control using only 15 samples—reducing data requirements by approximately 50% while converging up to 40% faster compared to conventional approaches. Experimental validation in epitaxial silicon carbide (Epi SiC) scenarios demonstrates that the MOODFG-MLTL not only maintains robust performance under constrained data conditions but also accelerates the deployment of data-efficient, multi-objective DT solutions. This advancement provides a critical foundation for achieving universal, intelligent DT implementations in the semiconductor industry’s evolving landscape. © 2013 IEEE.

关键词： Silicon carbide

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