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multi-task assisted multi-objective optimization algorithm for autonomous underwater vehicle path planning

MEMETIC COMPUTING 2025年第1期17卷 1-22页

作者： Liu, Tianyu Wu, Yu Xu, He Shanghai Maritime Univ Sch Informat Engn Shanghai Peoples R China Zhejiang Univ Sch Informat Sci & Elect Engn Hangzhou Peoples R China

In recent years, the application of multi-objective optimization algorithms to craft feasible paths considering multiple factors has garnered significant attention in handling path planning problems for autonomous underwater vehicles. However, the construction of appropriate multi-objective problem models coupled with efficient search strategies emerges as a pivotal determinant influencing the performance of multi-objective path planning algorithms. This paper introduces a multi-task assisted multi-objective optimization algorithm (MAMO) tailored to address autonomous underwater vehicle path planning problems. The proposed multi-task framework encompasses two tasks: the original path planning task and a devised simple task. These two tasks have different decision spaces due to distinct encoding strategies. Additionally, two different yet interconnected multi-objective problem models are deployed in the above two tasks. Furthermore, two knowledge transfer strategies, domain mapping-based and reconstruction-based knowledge transfer strategies, are introduced to leverage the knowledge from the simple task to assist the original task. The efficacy of the proposed MAMO is compared against eight counterparts and evaluated on three autonomous underwater vehicle path planning cases with different numbers of obstacles. The empirical findings corroborate the efficacy of the algorithm proffered.

关键词： Autonomous underwater vehicle Path planning multi-objective optimization algorithm multi-task framework Knowledge transfer

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multi-objective optimization algorithm With Adaptive Resource Allocation for Truck-Drone Collaborative Delivery and Pick-Up Services

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第9期24卷 9642-9657页

作者： Luo, Qizhang Wu, Guohua Trivedi, Anupam Hong, Fangyu Wang, Ling Srinivasan, Dipti Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 119260 Singapore Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

To efficiently implement the truck-drone collaborative logistics system, we introduce a multi-objective truck-drone collaborative routing problem with delivery and pick-up services (MCRP-DP). A truck collaborating with a fleet of drones serves three types of customers that require delivery, pick-up, and simultaneous delivery & pick-up services, respectively. Different from most of the existing studies where the drone visits only one customer in a flight, we allow the drone to serve another customer requiring pick-up service when it completes a delivery service. Meanwhile, we simultaneously optimize three objectives: transportation costs, waiting time of vehicles (i.e., truck and drone), and service reliability. To solve MCRP-DP, we propose an objective space decomposition-based multi-objective evolutionary algorithm with adaptive resource allocation (ODEA-ARA) In ODEA-ARA, an objective space decomposition strategy is used to maintain the diversity while an adaptive resource allocation strategy is designed to improve convergence. We design an ensemble of relative improvement and relative contribution to assist the resource allocation and a variable neighborhood Pareto local search integrating 7 problem-specific neighborhood structures to improve the solution. Extensive computational experiments are carried out to evaluate the performance of ODEA-ARA. The experimental results show that ODEA-ARA outperforms its competitors. Meanwhile, several useful managerial insights are presented.

关键词： Drones Logistics Collaboration Routing Resource management optimization Traveling salesman problems delivery and pick-up services truck-drone collaboration vehicle routing problem multi-objective optimization algorithm local search

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multi-objective optimization algorithm for optimizing NVH performance of electric vehicle permanent agnet synchronous motors

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JOURNAL OF POWER ELECTRONICS 2022年第12期22卷 2039-2047页

作者： Zhu, Yidi Bai, Fengxian Sun, Jianzhong Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian Peoples R China

The NVH (noise, vibration, harshness) performance of a motor is one of the main problems affecting the comfort, safety, and reliability of electric vehicles. Electromagnetic force is the main cause of motor noise. Most of the existing research focuses on the overall noise level, and does not consider the impact of specific orders of electromagnetic force on noise, which results in a lack of applicability of noise reduction techniques. In this paper, a rotor with an auxiliary slot was used to weaken the electromagnetic force. A multi-objective optimization algorithm combining finite-element simulation with a response surface method was proposed. To determine the relationship between specific orders of electromagnetic force and the auxiliary slot parameters, simulation experiments were carried out with a large range and a large step size in finite-element analysis software. Then, the parameter range with a low value of electromagnetic force was selected. In this new range, the response surface method was used to establish the parameter and electromagnetic force expressions. Then, the linear weighting method in the multi-objective optimization algorithm was selected to determine the objective function of the multi-order electromagnetic force optimization. The weight of each order of electromagnetic force was set according to its contribution to the noise. Finally, the effectiveness of the proposed method was verified by simulations. Simulation results show that this method can quickly and effectively determine the optimal size of the auxiliary slot. In addition, the maximum value of the noise was reduced from 107.6 to 103.2 dB.

关键词： NVH performance Electromagnetic force Finite-element method Response surface method multi-objective optimization algorithm

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multi-objective optimization algorithm and Preference multi-objective Decision-Making Based on Artificial Intelligence Biological Immune System

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IEEE ACCESS 2020年 8卷 160221-160230页

作者： Bao, Juan Liu, Xiangyang Xiang, Zhengtao Wei, Gang Hubei Univ Med Sch Publ Hlth Ctr Hlth Adm & Dev Studies Shiyan 442000 Peoples R China Hubei Univ Automot Technol Sch Elect & Informat Engn Shiyan 442002 Peoples R China Hubei Univ Med Affiliated Peoples Hosp Shiyan 442000 Peoples R China

The operating mechanism of the biological immune system is often used for the development of intelligent technology. This research introduces the multi-functional optimization algorithm of the biological immune system into the VR image segmentation, and proposes a multi-purpose VR image segmentation method with more stable and better segmentation performance. In order to combine it with the characteristics of the VR image itself, a complementary feature extraction method combining filters and gray-level symbiosis probability is used. In addition, in order to enable the algorithm to solve the segmentation problem of huge pixel images, the best solution is found through communication and exchange between subgroups. Use the excellent genes of the memory genes as the imported genes and introduce the inferior individuals to strengthen the mining of the best solution of Pareto at the boundary of the impossible field. In order to verify the performance of the algorithm, 3 synthetic texture images and 2 actual VR images are used, 8 constrained targets and 4 unconstrained target benchmark functions are selected to test the optimization function of PCMIOA. multipoint parallel search uses two different search schemes, local and global. In this way, the domain value of the highest value can be searched globally, and the local best solution can be searched at the same time, realizing the global search mechanism. The relatively satisfactory target value is 98.25, and the deviation between the corresponding solution and the ideal solution is 0.093. The results of the research show that multi - objective optimization algorithm is an excellent demonstration of the diversity of Pareto-oriented methods and solutions. Compared with the previous prediction methods, this method has higher prediction accuracy and robustness. The choice of decision makers can be taken into consideration, and subjective willfulness can be reduced to make decision results more realistic and reliable.

关键词： Immune system optimization Image segmentation Artificial intelligence Tumors Biomedical imaging Artificial intelligence biological immune system multi-objective optimization algorithm preference multi-objective decision-making VR panorama

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multi-objective optimization algorithm analysis on supply chain resources integration decision in 4PL

Multi-objective optimization algorithm analysis on supply ch...

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IEEE International Conference on Automation and Logistics

作者： Liu, Liwen Yao, Hanming Tsinghua Univ Sch Econ & Management Beijing 100084 Peoples R China

ISBN: (纸本)9781424415304

At present, the successful operations of the fourth party logistics (4PL) in practice gradually demonstrate that it is an effective mode to integrate the supply chain's complicated resources rationally, efficiently and flexibly. In order to thoroughly analyze the supply chain resources integration problem in 4PL from a quantitative perspective and to guide the integration practice, based on the previous study results, this paper takes the multi-objective optimization action of the integration decision as an analysis pivot, builds a improved ant algorithm to resolve the integration decision optimization process. Finally, the reasonability and feasibility of the algorithm are validated through the simulation of a calculation case.

关键词： the fourth party logistics supply chain resources integration multi-objective optimization algorithm

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Back analysis of rock mass parameters in mechanized twin tunnels based on coupled auto machine learning and multi-objective optimization algorithm

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Journal of Rock Mechanics and Geotechnical Engineering 2025年

作者： Chengwen Wang Xiaoli Liu Jiubao Li Enzhi Wang Nan Hu Wenli Yao Zhihui He Department of Hydraulic Engineering State Key Laboratory of Hydroscience and Engineering Tsinghua University Beijing 100084 China YSD Rail Transit Construction Company Limited Guangzhou 510630 China

Accurate determination of rock mass parameters is essential for ensuring the accuracy of numerical simulations. Displacement back-analysis is the most widely used method; however, the reliability of the current approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameter back-analysis method, that considers the construction process and displacement losses is proposed and implemented through the coupling of numerical simulation, auto-machine learning (AutoML), and multi-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanized twin tunnels is developed, generating a dataset through extensive numerical simulations. Next, the AutoML method is utilized to establish a surrogate model linking rock parameters and displacements. The tunnel construction process is divided into multiple stages, transforming the rock mass parameter back-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithms are introduced to obtain the rock mass parameters. The newly proposed rock mass parameter back-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectiveness are demonstrated. Compared with traditional single-stage back-analysis methods, the proposed model decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving the accuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increases with the number of construction stages considered, the back analysis time is acceptable. This study provides a new method for displacement back analysis that is efficient and accurate, thereby paving the way for precise parameter determination in numerical simulations.

关键词： Back analysis of rock parameters auto machine learning multi-objective optimization algorithm mechanized twin tunnels parametric modeling

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MooSeeker: A Metabolic Pathway Design Tool Based on multi-objective optimization algorithm

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第6期20卷 3609-3622页

作者： Cao, Yahui Zhang, Tao Zhao, Xin Jia, Xue Li, Bingzhi Tianjin Univ Sch Elect & Informat Engn Tianjin 300222 Peoples R China Tianjin Univ Sch Chem Engn & Technol Tianjin 300222 Peoples R China

Recently, metabolic pathway design has attracted considerable attention and become an increasingly important area in metabolic engineering. Manual or computational methods have been introduced to retrieve the metabolic pathway. These methods model metabolic pathway design as a single-objective optimization problem with the weighted sum of a variety of criteria as the final score. While these methods have demonstrated promising results, the majority of current methods do not account for comparisons and competition among criteria. Here, we propose MooSeeker, a metabolic pathway design tool based on the multi-objective optimization algorithm that aims to trade off all the criteria optimally. The metabolic pathway design problem is characterized as a multi-objective optimization problem with three objectives including pathway length, thermodynamic feasibility and theoretical yield. In order to digitize the continuous metabolic pathway, MooSeeker develops the encoding strategy, BioCrossover and BioMutation operators to search for the candidate pathways. Finally, MooSeeker outputs the Pareto optimal solutions of the candidate metabolic pathways with three criterion values. The experiment results show that MooSeeker is capable of constructing the experimentally validated pathways and finding the higher-performance pathway than the single-objective-based methods.

关键词： optimization Compounds Pareto optimization Encoding Thermodynamics Statistics Sociology Metabolic engineering metabolic pathway design multi-objective optimization algorithm

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A multi-objective optimization algorithm for Bouc-Wen-Baber-Noori model to identify reinforced concrete columns failing in different modes

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART L-JOURNAL OF MATERIALS-DESIGN AND APPLICATIONS 2021年第9期235卷 2165-2182页

作者： Li, Zele Noori, Mohammad Zhao, Ying Wan, Chunfeng Feng, Decheng Altabey, Wael A. Southeast Univ Sch Civil Engn Nanjing Peoples R China Southeast Univ Int Inst Urban Syst Engn IIUSE Nanjing 210096 Jiangsu Peoples R China Calif Polytech State Univ San Luis Obispo Dept Mech Engn San Luis Obispo CA 93407 USA Alexandria Univ Dept Mech Engn Fac Engn Alexandria Egypt

Concrete columns are the most important load-bearing components in civil structures. The potential damage in reinforced concrete (RC) columns could be categorized into three different failure modes: flexural shear (FS) failure, flexural-failure (FF), and shear failure (SF). The corresponding hysteresis loops for each mode differ significantly. Therefore, a multi-parameter hysteretic restoring force model is needed to describe the hysteretic energy dissipation phenomenon and behavior. Identification of the optimal parameter values of a multi-parameter hysteresis model of RC columns under different failure modes is essential in the evaluation of structural inelastic seismic performance. In this paper, a multi-objective optimization algorithm called NSGA-II is employed to identify the parameters of Bouc-Wen-Baber-Noori model (BWBN) hysteresis model, this model has been used for describing the response and modelling restoring force behavior in several structural and mechanical engineering systems, that can fully describe the hysteretic restoring force characteristics of RC columns. An objective function for the restoring force is proposed to identify the parameters of BWBN model. In order to ensure the accuracy of identification, based on the sensitivity analysis, the parameters distribution law of RC columns in different failure modes is obtained. Furthermore, the reference values under different failure modes are proposed. The results presented in this paper will significantly reduce the calculation of subsequent identification. Twelve groups of experimental data are randomly selected to verify the feasibility of the above algorithm. It is demonstrated that using the multi-objective optimization algorithm leads to better identification accuracy with minimum prior experience. Performance of the algorithm is verified using simulated and experimental data. The experimental data of the RC columns were collected from the database of the Pacific Earthquake Engineering Resea

关键词： Bouc-Wen-Baber-Noori model hysteresis loop parameter identification multi-objective optimization algorithm

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A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting

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APPLIED ENERGY 2019年 241卷 519-539页

作者： Niu, Xinsong Wang, Jiyang Dongbei Univ Finance & Econ Sch Stat Dalian Peoples R China Macau Univ Sci & Technol Fac Informat Technol Macau Peoples R China

Short-term wind speed forecasting plays an important role in wind power generation and considerably contributes to decisions regarding control and operations. In order to improve the accuracy of wind speed forecasting, a large number of prediction methods have been proposed. However, existing prediction models ignore the role of data preprocessing and are susceptible to various limitations of the single individual model that can lead to low prediction accuracy. In this study, a developed combined model is proposed, including complete ensemble empirical mode decomposition with adaptive noise-a multi-objective grasshopper optimization algorithm based on a no-negative constraint theory-and several single models, including four neural network models and a linear model, to achieve accurate prediction results. The novel combined model considers the linear and nonlinear characteristics of the sequence, successfully overcomes the limitations of the single model, and obtains accurate and stable prediction results. In order to test the performance of combined model, the wind speed sequence of a wind farm from China is used for experiments and discussions. The results of the experiments and discussions show that the novel combined model has better forecasting performance than traditional prediction models.

关键词： Wind speed forecasting Combined model Artificial intelligence Data preprocessing strategy multi-objective optimization algorithm

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HULL SHAPE optimization OF SMALL UNDERWATER VEHICLE BASED ON KRIGING-BASED RESPONSE SURFACE METHOD AND multi-objective optimization algorithm

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BRODOGRADNJA 2022年第3期73卷 111-134页

作者： Hou, Shuping Zhang, Zejiang Lian, Hongtai Xing, Xiaodong Gong, Haixia Xu, Xiujun Harbin Engn Univ Coll Mech & Elect Engn Harbin 15000 Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China

Small underwater vehicles have unique advantages in ocean exploration. The resistance and volume of a vehicle are key factors affecting its operation time underwater. This paper aims to develop an effective method to obtain the optimal hull shape of a small underwater vehicle using Kriging-based response surface method (RSM) and multi-objective optimization algorithm. Firstly, the hydrodynamic performance of a small underwater vehicle is numerically investigated using computational fluid dynamics (CFD) method and the value range of related design variables is determined. The mesh convergence is verified to ensure the accuracy of the calculation results. Then, by means of the Latin hypercube sampling (LHS) design of simulation, the Kriging-based RSM model is developed according to the relation between each design variable of the vehicle and the output parameters applied to the vehicle. Based on the Kriging-based RSM model, the optimal hull shape of the vehicle is determined by using Screening and MOGA. As results, the vehicle resistance reduces and volume increases obviously.

关键词： resistance volume small underwater vehicle Kriging-based response surface method CFD multi-objective optimization algorithm

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