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Knowledge hierarchy-based dynamic multi-objective optimization method for AUV path planning in cooperative search missions

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OCEAN ENGINEERING 2024年第Part3期312卷

作者： Wang, Yinhuan Liu, Kaizhou Geng, Lingbo Zhang, Shaoze Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

This study focuses on path planning for Autonomous Underwater Vehicles (AUVs) in underwater cooperative search missions. The complexities of the ocean environment, the uncertainty of target movements, and limited communication capabilities render underwater cooperative search missions dynamic multi-objective optimization challenges. Studies focusing on communication-constrained search strategies still lack reliable metrics for assessing underwater communication quality and do not consider communication dead zones. Addressing these deficiencies, this paper introduces a novel communication optimization strategy utilizing packet error rate as a metric for assessing communication efficacy, complemented by a potential field-based method for navigating out of communication dead zones. To tackle the inherently dynamic nature of cooperative search missions for mobile underwater targets, we propose a dynamic multi-objective optimization algorithm that employs a knowledge hierarchy strategy. This method enhances the NSGA-II algorithm's efficiency by extracting effective gene segments from historical Pareto solution set and generating a new initial population through recombination, hierarchy, and prediction. Distinct from other advanced dynamic multi-objective optimization approaches that are limited to theoretical problems, our approach is directly applicable to practical scenarios. The effectiveness and practicality of the proposed method are validated through a series of simulation experiments considering the impact of underwater acoustic communication. These results demonstrate that this research is not only innovative in theory but also holds significant engineering value and practical prospects in real-world applications.

关键词： Autonomous underwater vehicle (AUV) Cooperative search Dynamic multi-objective optimization NSGA-II Restricted communication Underwater acoustic channel model

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Leveraging trust for joint multi-objective and multi-fidelity optimization

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2024年第1期5卷 015056页

作者： Irshad, Faran Karsch, Stefan Doepp, Andreas Ludwig Maximilian Univ Munich Fac Phys Coulombwall 1 D-85748 Garching Germany

In the pursuit of efficient optimization of expensive-to-evaluate systems, this paper investigates a novel approach to Bayesian multi-objective and multi-fidelity (MOMF) optimization. Traditional optimization methods, while effective, often encounter prohibitively high costs in multi-dimensional optimizations of one or more objectives. multi-fidelity approaches offer potential remedies by utilizing multiple, less costly information sources, such as low-resolution approximations in numerical simulations. However, integrating these two strategies presents a significant challenge. We propose the innovative use of a trust metric to facilitate the joint optimization of multiple objectives and data sources. Our methodology introduces a modified multi-objective (MO) optimization policy incorporating the trust gain per evaluation cost as one of the objectives of a Pareto optimization problem. This modification enables simultaneous MOMF optimization, which proves effective in establishing the Pareto set and front at a fraction of the cost. Two specific methods of MOMF optimization are presented and compared: a holistic approach selecting both the input parameters and the fidelity parameter jointly, and a sequential approach for benchmarking. Through benchmarks on synthetic test functions, our novel approach is shown to yield significant cost reductions-up to an order of magnitude compared to pure MO optimization. Furthermore, we find that joint optimization of the trust and objective domains outperforms sequentially addressing them. We validate our findings with the specific use case of optimizing particle-in-cell simulations of laser-plasma acceleration, highlighting the practical potential of our method in the Pareto optimization of highly expensive black-box functions. Implementation of the methods in existing Bayesian optimization frameworks is straightforward, with immediate extensions e.g. to batch optimization possible. Given their ability to handle various continuous

关键词： Bayesian optimization multi-objective optimization computational physics

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Investor Preference-Aware Portfolio optimization with Deep Reinforcement Learning

Investor Preference-Aware Portfolio Optimization with Deep R...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Li, Zhenglong Tam, Vincent Yeung, Kwan L. Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

ISBN: (纸本)9798331521165

Recently, deep reinforcement learning (RL) approaches have been adopted to optimize financial portfolios with the objective of maximizing total profits while reducing potential risks by spreading investment capital across a variety of assets. Despite achieving great advances in the trade-off between profits and risks, the existing deep RL-based frameworks rarely consider practical trading constraints when making decisions in real-world financial markets, which cannot fulfill the customized requirements of specific users and may violate market regulations. Accordingly, a multi-Agent and Self-Adaptive trading framework for Constrained portfolio optimization, namely the MASAC, is proposed in which the deep RL-based agent dynamically explores profit-maximization policies while the heuristic-based agent conducts min-conflict search to ensure the generated trading strategies satisfying all concerned constraints. Through sharing knowledge within the trading system, the agents of the proposed framework cooperatively produce new portfolios that can maximize overall profits while satisfying all investor requirements throughout the trading period. The experimental results reveal the advantages of the MASAC framework against many state-of-the-art approaches in investment performance and trading constraint satisfaction on the challenging data sets of real-world markets. © 2025 IEEE.

关键词： Risk management

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multi-Stage Battery Charging Scheduling optimization at Battery Swapping Stations 16

Multi-Stage Battery Charging Scheduling Optimization at Batt...

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16th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2024

作者： Yv, Hao Han, Weiji China-UK Low Carbon College Shanghai Jiao Tong University Shanghai201306 China

ISBN: (纸本)9798350386127

Battery swapping has become a prominent method of energy supply for electric vehicles, featuring short service time and streamlined charging management. This paper explores efficient battery charging scheduling (BCS) strategies at battery swapping stations (BSSs). By analyzing the operational characteristics of batteries, a multi-stage BCS model is proposed. Unlike the conventional constant-power single-stage BCS model, the proposed multi-stage BCS model allows batteries to be charged to different expected state of energy (SoE) levels in different stages, and the charging power rate can be adaptively adjusted to avoid over-voltage or accelerated degradation when the battery approaches its highest SoE level. Considering both the battery SoEs and real-time swapping demands and taking the batteries to be charged in each stage as control variables, a multi-objective integer programming problem is formulated to minimize the average charging cost as well as to maximize the average SoE of charged batteries. This problem is solved using the Gurobi solver, and the effectiveness and advantages of the proposed BCS strategy are demonstrated through a case study of a practical BSS for heavy-duty electric trucks. © 2024 IEEE.

关键词： Charging stations

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Machine learning framework to extract the biomarker potential of plasma IgG N-glycans towards disease risk stratification

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2024年 23卷 1234-1243页

作者： Flevaris, Konstantinos Davies, Joseph Nakai, Shoh Vuckovic, Frano Lauc, Gordan Dunlop, Malcolm G. Kontoravdi, Cleo Imperial Coll London Dept Chem Engn London SW7 2AZ England Genos Glycosci Res Lab Zagreb 10000 Croatia Univ Zagreb Fac Pharm & Biochem Dept Biochem & Mol Biol Zagreb Croatia Univ Edinburgh Inst Genet & Canc Canc Res UK Scotland Ctr Colon Canc Genet Grp Edinburgh Scotland Med Res Council Human Genet Unit Edinburgh Scotland

Effective management of chronic diseases and cancer can greatly benefit from disease -specific biomarkers that enable informative screening and timely diagnosis. IgG N-glycans found in human plasma have the potential to be minimally invasive disease -specific biomarkers for all stages of disease development due to their plasticity in response to various genetic and environmental stimuli. Data analysis and machine learning (ML) approaches can assist in harnessing the potential of IgG glycomics towards biomarker discovery and the development of reliable predictive tools for disease screening. This study proposes an ML -based N-glycomic analysis framework that can be employed to build, optimise, and evaluate multiple ML pipelines to stratify patients based on disease risk in an interpretable manner. To design and test this framework, a published colorectal cancer (CRC) dataset from the Study of Colorectal Cancer in Scotland (SOCCS) cohort (1999 -2006) was used. In particular, among the different pipelines tested, an XGBoost-based ML pipeline, which was tuned using multi -objective optimisation, calibrated using an inductive Venn-Abers predictor (IVAP), and evaluated via a nested cross -validation (NCV) scheme, achieved a mean area under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.771 when classifying between age-, and sex -matched healthy controls and CRC patients. This performance suggests the potential of using the relative abundance of IgG N-glycans to define populations at elevated CRC risk who merit investigation or surveillance. Finally, the IgG N-glycans that highly impact CRC classification decisions were identified using a global model -agnostic interpretability technique, namely Accumulated Local Effects (ALE). We envision that open -source computational frameworks, such as the one presented herein, will be useful in supporting the translation of glycan-based biomarkers into clinical applications.

关键词： Glycosylation Cancer multi-objective optimization Probability calibration Interpretable machine learning

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Incomplete risk-preference information in portfolio decision analysis

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第3期304卷 1084-1098页

作者： Liesio, Juuso Kallio, Markku Argyris, Nikolaos Aalto Univ Dept Informat & Serv Management Sch Business Espoo Finland Loughborough Univ Sch Business & Econ Loughborough Leics England

Portfolio decision analysis models support decisions on the allocation of resources among assets with un-certain outcomes (e.g., investments, projects or stocks). These models require information on the decision maker's risk-preferences which can be difficult to obtain in practice. Stochastic dominance criteria show promise in this regard as they can compare portfolios without exact specification of risk-preferences, but the current literature lacks practical approaches for generating the efficient frontier, i.e., the set of those portfolios that are not stochastically dominated by any other portfolio. We address this gap by develop-ing models to identify sets of portfolios that are efficient in the sense of second-or third-order stochastic dominance (SSD, TSD). These models provide novel insights into the composition of portfolios belong-ing to the efficient frontier by, e.g., identifying those assets that are included in all efficient portfolios. Moreover, the identification of the efficient frontier makes it possible to utilize additional information on the decision maker's risk-preferences to further reduce the set of admissible portfolio alternatives, and to analyze the implications this information has on the amount of capital that should be allocated to each individual asset. We illustrate the usefulness of these models with applications in project portfolio selection and financial portfolio diversification. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： Decision support systems Portfolio decision analysis Stochastic dominance multi -objective programming optimization

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Effect of music on the life of a student using probabilistic approaches

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SOFT COMPUTING 2023年

作者： Zhang, Dan Binzhou Univ Art Coll Binzhou 256600 Shandong Peoples R China

With the implementation of computer-grounded technologies in the music sector, it is now very reliable and efficient to generate and compose music of high quality. Music can be considered an integral part of many daily activities, and the same can be applied to the life of a student. The current research is an analysis and overview of the various impacts of music on the educational career and life outside of academics of a learner. It was found that music can be very helpful in enhancing learners' performance and engagement in the classroom. Various studies have shown that music is an effective cure for reducing anxiety about exams and other competitions in a student's life. For a healthy and psychologically balanced life, classical music was found to be a very affordable and reliable source. Nursing students can use music as the best candidate for relaxation before the practical examination. Research shows that students should avoid unlawful usage of music and that it is necessary to respect the piracy of a music source. The music can enhance the students' sleep quality and help in the treatment of disorders like insomnia. The study then selected the features that were by far the most prevalent after compiling several relevant traits from the corpus of prior research. Then, these attributes are evaluated using the Analytical Hierarchy Process (AHP). The choices were graded using the multi-objective optimization on the Basis of Research Analysis (MOORA) method.

关键词： Music Learner's performance Insomnia Classical music Anxiety

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Accelerating multi-objective optimization of Composite Structures Using multi-Fidelity Surrogate Models and Curriculum Learning

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MATERIALS 2025年第7期18卷 1469-1469页

作者： Miller, Bartosz Ziemianski, Leonard Rzeszow Univ Technol Fac Civil & Environm Engn & Architecture Al Powstancow Warszawy 12 PL-35959 Rzeszow Poland

The optimization of multilayer composite structures requires balancing mechanical performance, economic efficiency, and computational feasibility. This study introduces an innovative approach that integrates Curriculum Learning (CL) with a multi-fidelity surrogate model to enhance computational efficiency in engineering design. A multi-fidelity strategy is introduced to generate training data efficiently, leveraging a high-fidelity finite element model for accurate simulations and a low-fidelity model to provide a larger dataset at reduced computational cost. Unlike conventional surrogate modeling approaches, the proposed method applies CL to iteratively refine the surrogate model, enabling step-by-step learning of complex structural patterns and improving prediction accuracy. Genetic algorithms (GAs) are then applied to optimize structural parameters while minimizing computational expense. The integration of CL and multi-fidelity modeling allows for a reduction in computational burden while preserving accuracy, demonstrating practical applicability in real-world structural design problems. The effectiveness of this methodology is validated by evaluating Pareto front quality using selected performance indicators. Results demonstrate that the proposed approach reduces optimization burden while achieving accurate predictions, highlighting the benefits of integrating surrogate modeling, multi-fidelity analysis, CL, and GAs for efficient composite structure optimization. This work contributes to the advancement of optimization methodologies by providing a scalable framework applicable to complex engineering problems requiring high computational efficiency.

关键词： multi-objective optimization composite multi-fidelity models surrogate models deep neural networks genetic algorithms curriculum learning

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Handling objective preference and variable uncertainty in evolutionary multi-objective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 94卷

作者： Yadav, Deepanshu Ramu, Palaniappan Deb, Kalyanmoy Indian Inst Technol Madras Dept Engn Design Chennai 600036 India Michigan State Univ Dept Elect & Comp Engn E Lansing MI USA

Evolutionary algorithms (EAs) are widely employed in multi-objective optimization (MOO) to find a well- distributed set of near-Pareto solutions. Among various types of practicalities that demand standard evolutionary multi-objective optimization (EMO) algorithms to be modified suitably, we propose here a framework for handling two important ones: (i) decision-making to choose one or more preferred Pareto regions, rather than finding the entire Pareto set, and (i) uncertainty in variables and parameters of the problem which is inevitable in any practical problem. While the first practicality will allow a focused set of preferred solutions to be found, the second practicality will enable finding robust yet high-performing non-dominated solutions. We propose and analyze four different approaches for finding preferred and robust solutions for handling both practicalities simultaneously. Our results on a number of two to 10-objective tests and engineering problems indicate the superiority of one specific approach. Fora comprehensive evaluation of new EMO algorithms for finding a preferred and robust solution set, we also propose anew performance metric by identifying and utilizing a number of desired properties of such trade-off solutions. The study is comprehensive and should encourage researchers to develop more competitive EMO algorithms for finding preferred and robust Pareto solutions.

关键词： Evolutionary algorithms Pareto front Robust front multi-criteria decision-making Preference-based EMO

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multi-UAV Cooperative Path-Planning in Complex Terrain: A multi-objective optimization Approach

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IETE JOURNAL OF RESEARCH 2025年

作者： Sil, Mitali Rakshit, Pratyusha Chatterjee, Sayan Roy, Abhishek Ghosh Chowdhury, Archana Jadavpur Univ Dept Elect & Telecommun Engn Kolkata India Tata Adv Syst Airborne Platforms & Syst Bengaluru India Christian Coll Engn & Technol Dept Comp Sci Bhilai India

Unmanned aerial vehicles (UAVs) are increasingly utilized in domains such as environmental monitoring, military operations, and disaster response, where real-time coordination among multiple UAVs is crucial. However, cooperative multi-UAV path planning remains a complex challenge due to the need to balance conflicting objectives such as obstacle avoidance, inter-UAV collision prevention and path optimality. Most existing approaches address this problem through single-objective optimization using weighted aggregations, which often fail to fairly represent the tradeoffs and can lead to sub-optimal solutions. To overcome these limitations, this paper formulates the cooperative multi-UAV path-planning problem as a multi-objective optimization (MOO) task. A novel optimization algorithm - non-dominated sorting firefly algorithm (NSFA) - is proposed, which integrates non-dominated sorting into the classical firefly algorithm to guide the search toward the Pareto-optimal front efficiently. NSFA includes a new movement control mechanism that dynamically balances exploration and exploitation, thereby accelerating convergence while maintaining solution diversity and quality. Extensive experiments conducted in various simulated environments demonstrate that NSFA outperforms existing algorithms in both solution quality and convergence speed. In the best-case scenario, the proposed NSFA-based path planner demonstrates a 37% improvement in reducing the target objective function values. Its superior performance is further evidenced by a 20% reduction in task completion time compared to its competitors. Furthermore, the algorithm is successfully implemented on Parrot Mambo Mini drones in real-world scenarios, yielding results consistent with simulations. These findings validate the effectiveness and practical applicability of the proposed NSFA-based approach for cooperative path planning in multi-UAV systems.

关键词： Firefly algorithm multi-objective optimization Path-planning Parrot mambo mini drone Unmanned aerial vehicles

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