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Multi-objective optimization with recommender systems: A systematic review

INFORMATION SYSTEMS 2023年 117卷

作者： Zaizi, Fatima Ezzahra Qassimi, Sara Rakrak, Said Cadi Ayyad Univ Fac Sci & Tech Dept Comp Sci Lab L2IS Marrakech Morocco

Recommender systems have become essential in modern information systems and Internet applications by delivering personalized and pertinent content to users. While conventional recommendation algorithms usually prioritize optimizing a single objective, it is now evident that considering additional metrics is crucial for improving the overall user experience. Despite the importance of considering multiple objectives, conventional recommendation models face the challenge of balancing these objectives, which can sometimes conflict with each other. To tackle this challenge, there is a growing interest in multi-objective recommender systems (MORS) that consider multiple objectives simultaneously and provide a more personalized and varied set of recommendations. MORS can optimize recommendations based on various metrics, including accuracy, diversity, novelty, and user satisfaction, leading to more efficient and personalized recommendation systems. The objective of this paper is to conduct a systematic review study to assess the current state of research in the field of MORS and identify potential avenues for future exploration. The study selection procedure includes 78 primary studies published from 2019 to January 2023. These preliminary studies are categorized based on different variables to address the research questions outlined in this study. The findings of this systematic review study reveal a diverse range of applications, objectives, datasets, methodologies, and evaluation metrics utilized in the field of MORS. Additionally, this review offers a crucial overview of the current state of research in this area, highlighting the existing challenges and future directions for enhancing the efficiency of MORS. These outcomes can benefit both professionals and academic researchers in the development and implementation of effective MORS. & COPY;2023 Elsevier Ltd. All rights reserved.

关键词： Multi-objective optimization Recommender systems Multi-objective recommendation Systematic review MORS optimization algorithms Personalization

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Improved methods for optimization of power systems with renewable generation using electromagnetic transient simulators

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第1期220卷

作者： Kuranage, D. Filizadeh, S. Muthumuni, D. Univ Manitoba Dept Elect & Comp Engn Winnipeg MB R3T 5V6 Canada Manitoba HVDC Res Ctr 211 Commerce Dr Winnipeg MB R3P 1A3 Canada

This paper introduces new techniques for efficient use of electromagnetic transient simulators combined with optimization algorithms to optimize power systems with converter-tied renewable resources. This work is motivated by several challenges that must be overcome for simulation-based optimal design, including high computational burden of simulating large switching systems, repetitive nature of the design cycle, and large number of variables that need to be handled. Two screening methods are proposed in this paper to identify the parameters that do not influence the optimal solution significantly and hence can be ignored. Moreover, hybridization of optimization algorithms and parallel processing techniques are explored to achieve additional computational benefits. Case studies of systems with different complexity and number of variables are used to demonstrate the effectiveness of the proposed techniques.

关键词： Electromagnetic transient simulation optimization algorithms Parallel processing Renewable generation

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A Dynamic Mission Planning Method of Multi-Satellite Cooperative Observation for Highly Time-Sensitive Targets 15

A Dynamic Mission Planning Method of Multi-Satellite Coopera...

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15th Asia Conference on Mechanical and Aerospace Engineering, ACMAE 2024

作者： Jiaxin, Hu Demin, Zhao Yanwei, Zhu College of Aerospace Science National University of Defense Technology Changsha410000 China DFH Satellite Co. Ltd. Beijing10094 China

ISBN: (纸本)9781643685892

To address the challenges posed by highly time-sensitive targets with uncertainty and unpredictability in multi-satellite cooperative observation, conventional intelligent optimization algorithms often suffer from time-consuming and unstable issues. This paper presents a novel dynamic mission planning method that cleverly incorporates waiting time for tracking and tracked time as heuristics to guide task assignment. Furthermore, plannable and robustness indicators are introduced to facilitate global trade-offs in resource conflicts among multiple targets. Initially, the constraints specifically suitable for time-sensitive target observation are analyzed, followed by the modeling of the planning problem. Subsequently, a dynamic mission planning approach is proposed that utilizes dynamic priorities, offering a linear time solution for task assignment. Through simulation verification, the efficacy of the proposed method is demonstrated, showcasing its ability to enhance multi-satellite cooperative observation effectively. Additionally, it maximizes resource utilization in the face of diverse conflicts. © 2025 The Authors.

关键词： optimization algorithms

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Sensitivity of Online Feedback optimization to time-varying parameters

arXiv

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arXiv 2025年

作者： Zagorowska, Marta Imsland, Lars Department of Engineering Cybernetics Norwegian University of Science and Technology Norway

Online Feedback optimization uses optimization algorithms as dynamic systems to design optimal control inputs. The results obtained from Online Feedback optimization depend on the setup of the chosen optimization algorithm. In this work we analyse the sensitivity of Online Feedback optimization to the parameters of projected gradient descent as the algorithm of choice. We derive closed-form expressions for sensitivities of the objective function with respect to the parameters of the projected gradient and to time-varying model mismatch. The formulas are then used for analysis of model mismatch in a gas lift optimization problem. The results of the case study indicate that the sensitivity of Online Feedback optimization to the model mismatch depends on how long the controller has been running, with decreasing sensitivity to mismatch in individual timesteps for long operation times. © 2025, CC BY.

关键词： optimization algorithms

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Selection of projects? primary and secondary mitigation actions through optimization methods in nuclear decommissioning projects

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NUCLEAR ENGINEERING AND DESIGN 2023年第1期407卷

作者： Mariani, Costanza Mancini, Mauro Politecn Milan Dept Management Econ & Ind Engn Via Lambruschini 4-B I-20156 Milan MI Italy

The growing interest in nuclear power has brought attention back to the general condition of nuclear power plants. In fact, according to the main intergovernmental organisations responsible for nuclear energy, more than 150 basic nuclear facilities (in-service reactors, downgraded reactors, fuel fabrication plants, reprocessing plants, and waste storage areas) should be seriously checked for safety reasons, while many others are close to the end of their lifecycles (lasting generally around 50-60 years) - thus, there is an urgent need for research into the management of Nuclear Decommissioning Projects (NDPs). In particular, the high complexity of these projects makes it fundamental to implement strong risk management procedures, aimed at identifying and analyzing all possible hazards, and finding and implementing the appropriate risk response actions. This paper focuses on the selection of mitigation actions and proposes optimizationisation algorithms to select the most time-effective set of risk responses for a nuclear decommissioning project. A single case study of an Italian completed NDP was employed to investigate the application of optimization techniques in the mitigation action selection phase, considering also secondary risks and secondary mitigation action. The results show that the performance that would have been achieved through the optimization algorithm would have been superior, both from the point of view of a reduced time delay, and in terms of a more effective balance between overall risk coverage and implementation costs.

关键词： optimization algorithms Nuclear decommissioning projects Mitigation action selection

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Application and Prospect of Artificial Intelligence Methods in Signal Integrity Prediction and optimization of Microsystems

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MICROMACHINES 2023年第2期14卷 344-344页

作者： Shan, Guangbao Li, Guoliang Wang, Yuxuan Xing, Chaoyang Zheng, Yanwen Yang, Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China Beijing Inst Aerosp Control Devices Beijing 100039 Peoples R China

Microsystems are widely used in 5G, the Internet of Things, smart electronic devices and other fields, and signal integrity (SI) determines their performance. Establishing accurate and fast predictive models and intelligent optimization models for SI in microsystems is extremely essential. Recently, neural networks (NNs) and heuristic optimization algorithms have been widely used to predict the SI performance of microsystems. This paper systematically summarizes the neural network methods applied in the prediction of microsystem SI performance, including artificial neural network (ANN), deep neural network (DNN), recurrent neural network (RNN), convolutional neural network (CNN), etc., as well as intelligent algorithms applied in the optimization of microsystem SI, including genetic algorithm (GA), differential evolution (DE), deep partition tree Bayesian optimization (DPTBO), two stage Bayesian optimization (TSBO), etc., and compares and discusses the characteristics and application fields of the current applied methods. The future development prospects are also predicted. Finally, the article is summarized.

关键词： microsystem signal integrity neural network optimization algorithms

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Hybrid MPPT control using hybrid pelican optimization algorithm with perturb and observe for PV connected grid

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Frontiers in Energy Research 2024年 12卷

作者： Abdullah, Burhan U. Din Dhar, Suman Lata Jaiswal, Shiva Pujan Gulzar, Muhammad Majid Alqahtani, Mohammad Khalid, Muhammad Electrical Electronics and Communication Engineering Department Sharda University Greater Noida India Dhahran Saudi Arabia KFUPM Dhahran Saudi Arabia Department of Industrial Engineering and Center for Engineering and Technology Innovations King Khalid University Abha Saudi Arabia Electrical Engineering Department KFUPM Dhahran Saudi Arabia

Introduction: Photovoltaic systems offer immense potential as a future energy source, yet maximizing their efficiency presents challenges, notably in achieving optimal voltage due to their nonlinear behavior. Operating current and voltage fluctuations, driven by temperature and radiation changes, significantly impact power output. Traditional Maximum Power Point Tracking (MPPT) methods struggle to adapt accurately to these dynamic environmental conditions. While Artificial Intelligence (AI) and optimization techniques show promise, their implementation complexity and longer attainment times for Maximum Power Point (MPP) hinder widespread adoption. Method: This paper proposes a hybrid MPPT technique that integrates the Pelican optimization algorithm (POA) with the Perturb and Observe algorithm (P&O) for a grid-connected photovoltaic system (PV). The proposed technique consists of two loops: PO as the reference point setting loop (inner loop) and POA as a fine-tuning (outer)loop. The combination of inner and outer loops minimizes oscillations by adjusting the perturbation direction and enhancing the convergence speed of the MPPT. Results and Discussion: To validate the efficacy of the proposed MPPT technique for different environmental conditions, a comprehensive comparison is conducted between the proposed hybrid pelican and perturb and observe (HPPO) technique and other MPPT algorithms. The proposed technique has optimized PV and grid outputs with an MPPT efficiency of 99%, best tracking speed, and total harmonic distortion (THD) for all conditions below 5% agree with IEEE 519 standards. Copyright © 2025 Abdullah, Dhar, Jaiswal, Gulzar, Alqahtani and Khalid.

关键词： optimization algorithms

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Design optimization of Hydraulic Machinery Based on ISIGHT Software: A Review of Methods and Applications

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WATER 2023年第11期15卷 2100页

作者： Tian, Fei Yang, Chen Zhang, Erfeng Sun, Dehua Shi, Weidong Chen, Yonghua Jiangsu Univ Sch Energy & Power Engn Zhenjiang 212013 Peoples R China Nantong Univ Sch Mech Engn Nantong 226019 Peoples R China Yatai Pump & Valve Co Ltd Taixing 225400 Peoples R China

Optimizing hydraulic machinery is a critical research area within the field of fluid mechanics, aiming to enhance product design efficiency and improve performance while reducing development time. The application of intelligent algorithms and combinatorial optimization strategies has become increasingly prevalent in this domain, providing a comprehensive understanding of optimization-related theoretical developments. Recently, the emergence of ISIGHT software as a new technology for software integration platforms has opened new avenues for optimization in hydraulic machinery. By leveraging intelligent algorithms and combinatorial optimization strategies, ISIGHT software provides a comprehensive framework for optimizing hydraulic machinery. This paper serves as an introduction to ISIGHT software, highlighting its advantages in addressing optimization problems. It presents a detailed examination of the process and technology involved in hydraulic machinery optimization based on ISIGHT software, along with its practical application. Furthermore, the paper summarizes the future development trends of ISIGHT software, offering engineers a theoretical foundation and reference for optimizing hydraulic machinery performance. Overall, this paper provides a valuable contribution to the field of hydraulic machinery optimization, showcasing the potential of ISIGHT software.

关键词： hydraulic machinery design optimization optimization algorithms ISIGHT software

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Generating synthetic ground motions reaching target spectrum with the optimization approach

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STRUCTURES 2023年 58卷

作者： Tavakoli, Pouya Rahami, Hossein Univ Tehran Coll Engn Sch Civil Engn Tehran Iran Univ Tehran Coll Engn Sch Engn Sci Tehran Iran

Due to the scarcity of earthquake records and the necessity of the earthquakes matched with the predefined design response spectrum (DRS) for time history analysis, designers need to generate artificial earthquakes. The smoothness of the DRS prevents conformity in the majority of methods, and deviation happens, especially in the constant region. To overcome this condition, the present study intends to incorporate the optimization algorithm with the synthetic earthquake method to reach the ASCE DRS. Indeed, the statistical algorithm is utilized to explore the global optimum with more accuracy and faster convergence in comparison with other algorithms. The results demonstrate that the proposed method is ideally compatible with the DRS with trivial errors. Furthermore, the novel approach has been applied to Kobe and Tabas earthquakes in order to extract the envelope function to improve compatibility in the time domain. In essence, the proposed method is not only compatible with the DRS simply but also appropriate for simulating real earthquakes. Eventually, with regard to the recommendation of seismic regulations for applying several records in dynamic analysis, one of the robust advantages of this method is to generate various earthquakes compatible with the specific target spectrum.

关键词： Synthetic ground motion Artificial earthquake Artificial ground motion Artificial accelerogram Target spectrum Covariance matrix adaptation evolution strategy (CMA-ES) method Genetic programming optimization algorithms Envelope function

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Meerkat optimization algorithm: A new meta-heuristic optimization algorithm for solving constrained engineering problems

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期231卷

作者： Xian, Sidong Feng, Xu Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Intelligent Anal & Decis Complex Syst Chongqing 400065 Peoples R China

In this paper, we proposed a meerkat optimization algorithm(MOA) by simulating the behavior pattern of meerkats in nature. MOA is mainly inspired by the survival strategies of meerkat populations, whose sentinel mechanism controls meerkats to switch between different behavior patterns. Some mathematical properties of meerkat optimization algorithm are proved, and the advantages of MOA are verified with classical optimization test functions. MOA is applied to solve real-world engineering problems with constraints, which proves the effectiveness and superiority of MOA in solving such problems.

关键词： Meerkat optimization algorithm Swarm intelligence optimization algorithms Meta-heuristics Nature-inspired algorithms Constrained engineering problems

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