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An intelligent design for Ni-based superalloy based on machine learning and multi-objective optimization

MATERIALS & DESIGN 2022年 221卷

作者： Deng, Yuedan Zhang, Yu Gong, Xiufang Hu, Wang Wang, Yucheng Liu, Ying Lian, Lixian Sichuan Univ Coll Mat Sci & Engn 24 South Sect 1Yihuan Rd Chengdu 610065 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn 2006 Xiyuan Ave Chengdu 611731 Peoples R China State Key Lab Long Life High Temp Mat 666 Jinshajing West Rd Deyang 618000 Peoples R China Dongfang Elect Corp Dongfang Turbine Co Ltd 666 Jinshajing West Rd Deyang 618000 Peoples R China

The simultaneous consideration of multiple conflicting properties in alloy optimization design is necessary yet remains challenging. A comprehensive strategy of machine learning (ML) and multi-objective optimization based on thermodynamic simulation data was proposed to accelerate the composition design of Ni-based superalloys. The microscopic parameters were determined by Pearson correlation analysis and domain knowledge as the key affect factors of tensile strength and elongation. The multiobjectives evolutionary algorithm (MOEA) was adopted to search the well-built surrogate by ML metaheuristically for the Pareto front of three objectives and its responding Pareto optimal solution set of composition. Furthermore, nine high-performance superalloy samples selected from the obtained Pareto front were well verified by fabricating and testing in the laboratory. Specially, a new composition among the nine as-fabricated samples was the best one according to the pre-defined design preferences with the gamma' solvus temperature, gamma' volume fraction, and TCP phases content approximating to 1210 degrees C, 65%, and 0.01%, respectively. This intelligent cooperation strategy based on ML and MOEA extends the methodology for multi-composition and multi-property design materials, which can optimize multiple conflicting objectives simultaneously rather than do one by one. (C) 2022 The Authors. Published by Elsevier Ltd.

关键词： Ni-based superalloys Microscopic parameters multi-objective evolutionary algorithm Machine learning Thermo-Calc

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Mining high average-utility sequential rules to identify high-utility gene expression sequences in longitudinal human studies

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期193卷 116411-116411页

作者： Segura-Delgado, Alberto Anguita-Ruiz, Augusto Alcala, Rafael Alcala-Fdez, Jesus Univ Granada Dept Comp Sci & Artificial Intelligence DaSCI Granada 18071 Spain Barcelona Inst Global Hlth ISGlobal Barcelona 08003 Spain

High-utility sequential pattern mining techniques have demonstrated good performance in identifying associations between mRNA levels in microarray experiments taking into account both the biological context of each gene and the temporal characteristics of the dataset. However, these patterns do not provide information about how likely it is that the events in the pattern occur in the order indicated, therefore causal relationships cannot be established between of them. This reduces their predictive ability, making difficult its direct applicability to the field of gene expression dynamic modeling. An alternative to sequential patterns which takes the confidence of the forecast into account is the discovery of sequential rules. Their natural and seamless relation to human behavior makes them very suitable to understand complex models without missing the possibility of using the generated rules as a standalone prediction model. This contribution proposes an evolutionary algorithm optimizing multiple objectives for mining biologically relevant high average-utility sequential rules from longitudinal human gene expression data with a good compromise through average-utility and explainability. This proposal enhances the well-known NSGA-II to learn, by evolutionary optimization, the rules maximizing two objectives: Utility and Interestingness. Moreover, a restarting mechanism and an external population have been particularly designed and included in order to encourage diversity in the search process preserving all the rules found. The quality of our approaches has been analyzed using external biological resources, statistical analysis and comparing with other proposals from the literature.

关键词： High average-utility sequential rules multi-objective evolutionary algorithm eXplainable artificial intelligence Gene expression patterns Obesity

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A framework for constrained large-scale multi-objective white-box problems based on two-scale optimization through decision transfer

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INFORMATION SCIENCES 2024年 665卷

作者： Wang, Qingzhu Li, Tianyang Meng, Fanqi Li, Bin Northeast Elect Power Univ Sch Comp Sci Jilin 132012 Peoples R China Guangdong Atv Acad Performing Arts Sch Informat Engn Dongguan 523710 Peoples R China

Most existing constrained multi-objective evolutionary algorithms (CMOEAs) are not so efficient when handling constrained large-scale multi-objective problems (CLSMOPs). To overcome whitebox CLSMOPs with definitive objective functions, a two-scale optimization framework based on decision transfer, which integrates dimensionality reduction of large-scale decision variables and constraint handling technology, is proposed. The Lagrange multiplier is first used to construct the two-scale optimization model, which bridges original large-scale decision space of variables and small-scale (2-scale) decision space of objective-constraint parameter. The decision transfer algorithm is then designed to switch between large-scale original decision space and small-scale parametric decision space, while achieving the maximum dimensionality reduction. Finally, the two-scale evolution strategy is proposed for the alternative optimizations in the two decision spaces, which emphasize objectives and constraints, respectively. In summary, the optimization in the large-scale space pushes the population to unconstrained Pareto front (PF), the optimization in the small-scale space helps the population cross the infeasible areas to approach constrained PF, and the offspring generation by Lagrange multiplier is beneficial to both objectives and constraints. Eight representative and state-of-the-art CMOEAs have been embedded into the CLDTEA framework to demonstrate its effectiveness through comparative experiments on CLSMOPs with equality and inequality constraints and 1000 decision variables. Experimental results show that CLDTEA can significantly improve the performance of these basic CMOEAs.

关键词： Constrained large-scale multi-objective problem multi-objective evolutionary algorithm Decision transfer Lagrange multiplier

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Hybrid loading situation vehicle routing problem in the context of agricultural harvesting: A reconstructed MOEA/D with parallel populations

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SWARM AND evolutionary COMPUTATION 2024年 91卷

作者： Guo, Xiang Miao, Zhong-Hua Pan, Quan-Ke He, Xuan Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Shanghai Maritime Univ Sch Logist Engn Shanghai 201306 Peoples R China

With the increasing level of agricultural automation, the combination of agriculture and intelligent vehicle technology is propelling the development of smart agriculture. Although this technology has already been applied widely for various agricultural production tasks, inefficient vehicle scheduling still hasn't been resolved satisfactorily. Oriented towards agricultural harvesting scenarios, a hybrid loading situation vehicle routing problem (HLSVRP) model is proposed to minimize total energy consumption and maximum completion time. A reconstructed multi-objective evolutionary algorithm based on decomposition (R-MOEA/D) is developed to solve the problem. Eight solution representations tailored specifically to the problem are introduced by R-MOEA/D, allowing an extensive exploration of the solution space. A modified Clarke & Wright (MCW) heuristic is designed to generate a high-quality initial population. A novel problem-specific parallel population updating mechanism based on the four crossover and two mutation combinations is also provided to improve the exploration ability. A collaborative search strategy is employed to facilitate cooperation among parallel populations. Finally, a series of comparative experiments conducted on various task scales and vehicle scales verify the effectiveness of the proposed algorithmic components and the exceptional performance for solving HLSVRP.

关键词： Agricultural harvesting Hybrid loading situations multi-objective evolutionary algorithm Problem-specific parallel population Collaborative search

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Computational Intelligence Nonmodel-Based Calibration Approach for Internal Combustion Engines

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JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME 2018年第4期140卷 041002-041002页

作者： Ma, He Li, Ziyang Tayarani, Mohammad Lu, Guoxiang Xu, Hongming Yao, Xin Univ Birmingham Sch Engn Edgbaston B15 2TT England Univ Birmingham Sch Comp Sci Edgbaston B15 2TT England Univ Birmingham Sch Comp Sci Nat Computat Grp Edgbaston B15 2TT England

Over the past 20 years, with the increase in the complexity of engines, and the combinatorial explosion of engine variables space, the engine calibration process has become more complex, costly, and time consuming. As a result, an efficient and economic approach is desired. For this purpose, many engine calibration methods are under development in original equipment manufacturers and universities. The state-of-the-art model-based steady-state design of experiments (DOE) technique is mature and is used widely. However, it is very difficult to further reduce the measurement time. Additionally, the increasingly high requirements of engine model accuracy and robust testing process with high data quality by high-quality testing facility also constrain the further development of model-based DOE engine calibration. This paper introduces a new computational intelligence approach to calibrate internal combustion engine without the need for an engine model. The strength Pareto evolutionary algorithm 2 (SPEA2) is applied to this automatic engine calibration process. In order to implement the approach on a V6 gasoline direct injection (GDI) engine test bench, a SIMULINK real-time based embedded system was developed and implemented to engine electronic control unit (ECU) through rapid control prototyping (RCP) and external ECU bypass technology. Experimental validations prove that the developed engine calibration approach is capable of automatically finding the optimal engine variable set which can provide the best fuel consumption and particulate matter (PM) emissions, with good accuracy and high efficiency. The introduced engine calibration approach does not rely on either the engine model or the massive test bench experimental data. It has great potential to improve the engine calibration process for industries.

关键词： automated engine calibration computational intelligence multi-objective evolutionary algorithm rapid control prototyping simulation

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The quantitative techno-economic comparisons and multi-objective capacity optimization of wind-photovoltaic hybrid power system considering different energy storage technologies

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ENERGY CONVERSION AND MANAGEMENT 2021年 229卷 113779-113779页

作者： He, Yi Guo, Su Zhou, Jianxu Wu, Feng Huang, Jing Pei, Huanjin Hohai Univ Nanjing 210098 Jiangsu Peoples R China

To provide investors with a selection method of energy storage technology, this paper proposes a quantitative techno-economic comparison method of battery, thermal energy storage, pumped hydro storage and hydrogen storage in wind-photovoltaic hybrid power system from the perspective of multi-objective capacity optimization. The multi-objective capacity optimization models are developed based on minimizing the levelized cost of energy (economy) and loss of power supply probability (reliability) simultaneously. Comprehensive metrics based on hypervolume are proposed to compare the performance of four multi-objective evolutionary algorithms. Moreover, the operation characteristics of devices is considered in the model to improve the simulation accuracy. The performance comparisons of algorithms show that the average rank of nondominated sorting genetic algorithm, multi-objective evolutionary algorithm based on decomposition, multi-objective particle swarm optimization and strength Pareto evolutionary algorithm are 2.8, 3.6, 1.8 and 1.8 respectively, which demonstrates that multi-objective particle swarm optimization and strength Pareto evolutionary algorithm have relatively better overall performance when applied in capacity optimization problems. The quantitative techno-economic comparisons of energy storage show that the levelized cost of energy of thermal energy storage, battery, hydrogen storage and pumped hydro storage under the same reliability are 0.1224 $/kWh, 0.1812 $/kWh, 0.1863 $/kWh and 0.2225 $/kWh respectively, which demonstrates that thermal energy storage is the most cost-effective alternative. Furthermore, the sensibility analysis demonstrates that thermal energy storage is always the most cost-effective alternative for different load profile, different resources level and different energy storage cost. Finally, the conclusions can help investors to select a cost-effective and reliable energy storage technology.

关键词： Energy storage Quantitative techno-economic comparison Capacity optimization Operation characteristics multi-objective evolutionary algorithm

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Optimal capacity allocation of wind-light-water multi-energy complementary capacity based on improved multi-objective optimization algorithm

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FRONTIERS IN ENERGY RESEARCH 2023年 10卷

作者： Wang, Ying Liu, Jiajun Xian Univ Technol Sch Elect Engn Xian Peoples R China Yinchuan Univ Energy Sch Elect Power Yinchuan Peoples R China

As a basic industry for national economic development, the power industry is closely related to the overall economic and environmental development of China. At present, China is still dominated by thermal power generation. In order to reduce carbon emissions, promote the realization of the "double carbon " goal, and improve the level of clean energy utilization and the operating efficiency of the power system, a wind-light-water storage complementary power generation system is built, and a mathematical model of multi energy complementation is established. The minimum economic cost and the minimum battery capacity are proposed as the objective functions of system capacity configuration. Then a multi-objective evolutionary algorithm based on Pareto optimal space of the NDWA-GA and the PCA is proposed for optimal capacity allocation of multi energy complementary systems in this paper. Compared with the traditional multi-objective optimization algorithm, the correctness and effectiveness of the proposed method are verified. In addition, according to the actual research object, the optimal capacity configuration of the multi energy complementary system is given, which can guide the production and has an important promotion significance for energy saving and emission reduction.

关键词： multi energy complementary model multi-objective evolutionary algorithm capacity configuration conserve energy reduce emissions production guidance

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MIMOA: A membrane-inspired multi-objective algorithm for green vehicle routing problem with stochastic demands

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SWARM AND evolutionary COMPUTATION 2021年 60卷

作者： Niu, Yunyun Zhang, Yongpeng Cao, Zhiguang Gao, Kaizhou Xiao, Jianhua Song, Wen Zhang, Fangwei China Univ Geosci Beijing Sch Informat Engn Beijing 100083 Peoples R China Natl Univ Singapore Dept Ind Syst Engn & Management Singapore 117576 Singapore Macau Univ Sci & Technol Macau Inst Syst Engn Macau 999078 Peoples R China Nankai Univ Res Ctr Logist Tianjin 300071 Peoples R China Shandong Univ Inst Marine Sci & Technol Jinan 250100 Peoples R China Shanghai Maritime Univ Coll Transport & Commun Shanghai 201306 Peoples R China

Nowadays, an increasing number of vehicle routing problem with stochastic demands (VRPSD) models have been studied to meet realistic needs in the field of logistics. In this paper, a bi-objective vehicle routing problem with stochastic demands (BO-VRPSD) was investigated, which aims to minimize total cost and customer dissatisfaction. Different from traditional vehicle routing problem (VRP) models, both the uncertainty in customer demands and the nature of multiple objectives make the problem more challenging. To cope with BO-VRPSD, a membrane-inspired multi-objective algorithm (MIMOA) was proposed, which is characterized by a parallel distributed framework with two operation subsystems and one control subsystem, respectively. In particular, the operation subsystems leverage a multi-objective evolutionary algorithm with clustering strategy to reduce the chance of inferior solutions. Meanwhile, the control subsystem exploits a guiding strategy as the communication rule to adjust the searching directions of the operation subsystems. Experimental results based on the ten 120-node instances with real geographic locations in Beijing show that, MIMOA is more superior in solving BO-VRPSD to other classical multi-objective evolutionary algorithms.

关键词： Vehicle routing problem Stochastic demand Membrane-inspired algorithm Clustering strategy multi-objective evolutionary algorithm

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multi-objective Volleyball Premier League algorithm

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KNOWLEDGE-BASED SYSTEMS 2020年 196卷 105781-105781页

作者： Moghdani, Reza Salimifard, Khodakaram Demir, Emrah Benyettou, Abdelkader Persian Gulf Univ Computat Intelligence & Intelligent Optimizat Res Bushehr Iran Cardiff Univ Cardiff Business Sch Cardiff Wales USTOran Dept Informat BP 1505 El Mnaouer Oran Algeria

This paper proposes a novel optimization algorithm called the multi-objective Volleyball Premier League (MOVPL) algorithm for solving global optimization problems with multiple objective functions. The algorithm is inspired by the teams competing in a volleyball premier league. The strong point of this study lies in extending the multi-objective version of the Volleyball Premier League algorithm (VPL), which is recently used in such scientific researches, with incorporating the well-known approaches including archive set and leader selection strategy to obtain optimal solutions for a given problem with multiple contradicted objectives. To analyze the performance of the algorithm, ten multi-objective benchmark problems with complex objectives are solved and compared with two well-known multi-objective algorithms, namely multi-objective Particle Swarm Optimization (MOPSO) and multi-objective evolutionary algorithm Based on Decomposition (MOEA/D). Computational experiments highlight that the MOVPL outperforms the two state-of-the-art algorithms on multi-objective benchmark problems. In addition, the MOVPL algorithm has provided promising results on well-known engineering design optimization problems. (C) 2020 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm Global optimization Pareto solution Engineering design optimization problems

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A Type 2 Fuzzy Logic-Based Maintenance Solution for Power System in Renewable Energy Applications

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FRONTIERS IN ENERGY RESEARCH 2021年 9卷

作者： Xiang, Li Sang, Haitao Qu, Fayi Lingnan Normal Univ Sch Elect & Elect Engn Zhanjiang Peoples R China Harbin Inst Technol Sch Astronaut Harbin Peoples R China

Power systems are crucial for low-carbon energy applications. Condition maintenance plays a vital role in reducing the maintenance cost of renewable power systems without sacrificing system reliability. This paper proposes a hybrid method to effectively deal with the operational changes and uncertainties of state maintenance within the power system of renewable energy applications. Specifically, a multi-objective evolutionary algorithm is first adopted to maintain key components when only considering system variables and overall performance. During operation, numerous variations in offshore substations are detected from power grids and other equipment, such as continuous aging, weather, load factors, measurement, and human-judgment factors. Then, the advisor implements a system optimization maintenance plan in the substation, which can predict changes in load reliability based on the type 2 fuzzy logic and hidden Markov model technology. The reliability of the load point of each substation would also be obtained. Illustrative results indicate that these serious deteriorations would cause substation for the re-optimization maintenance and optimization activities to meet expected reliability. Through connecting an offshore substation to a medium-sized offshore substation, the uncertainties in condition-based maintenance of renewable energy applications can be well handled.

关键词： renewable energy power system offshore substation multi-objective evolutionary algorithm type 2 fuzzy logic minimum cut set

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