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A Mapping and State-of-the-Art Survey on multi-objective optimization Methods for multi-Agent Systems

IEEE ACCESS 2023年 11卷 139728-139744页

作者： Naderi, Shokoufeh Blondin, Maude J. Univ Sherbrooke Dept Genie Elect & Genie Informat Sherbrooke PQ J1K 2R1 Canada

Over the last decades, researchers have studied the multi-objective optimization (MOO) problem for multi-Agent Systems (MASs). However, most of them consider the problem formulation to be the sum of objective functions, and no work has reviewed problems with the formulation of the prioritized sum of objective functions to facilitate the study of the subject and identify the needs arising from it. In the context of multi-Robot Systems (MRSs), most studies only focus on the mathematical development of their proposed MOO algorithm without paying attention to the real application. There is no comprehensive review to identify the reliable algorithms already applied to real platforms. Using a mapping and state-of-the-art review, this paper aims to fill these gaps by first offering a detailed overview of the discrete-time MOO methods for MASs. More specifically, we classify existing MOO methods based on the formulation of the problem into the sum of objective functions and the prioritized sum of objective functions. Secondly, we review the applications of these methods in MRSs and the practical implementation of MOO algorithms on real MRSs. Finally, we suggest future research directions to extend the existing methods to more realistic approaches, including open problems in the new research area of the prioritized sum of objective functions and practical challenges for implementing the existing methods in robotics. This work introduces the field of MAS to researchers and enables them to position themselves in the current research trends.

关键词： multi-agent systems multi-objective optimization multi-robot systems prioritized sum of objective functions

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Hybrid Renewable Energy Systems-A Review of optimization approaches and Future Challenges

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APPLIED SCIENCES-BASEL 2025年第4期15卷 1744-1744页

作者： Giedraityte, Akvile Rimkevicius, Sigitas Marciukaitis, Mantas Radziukynas, Virginijus Bakas, Rimantas Lithuanian Energy Inst Breslaujos St 3 LT-44403 Kaunas Lithuania

The growing need for sustainable energy solutions has propelled the development of Hybrid Renewable Energy Systems (HRESs), which integrate diverse renewable sources like solar, wind, biomass, geothermal, hydropower and tidal. This review paper focuses on balancing economic, environmental, social and technical criteria to enhance system performance and resilience. Using comprehensive methodologies, the review examines state-of-the-art algorithms such as multi-objective Particle Swarm optimization (MOPSO) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II), alongside Crow Search Algorithm (CSA), Grey Wolf Optimizer (GWO), Levy Flight-Salp Swarm Algorithm (LF-SSA), Mixed-Integer Linear Programming (MILP) and tools like HOMER Pro 3.12-3.16 and MATLAB 9.1-9.13, which have been instrumental in optimizing HRESs. Key findings highlight the growing role of advanced, multi-energy storage technologies in stabilizing HRESs and addressing the intermittency of renewable sources. Moreover, the integration of metaheuristic algorithms with machine learning has enabled dynamic adaptability and predictive optimization, paving the way for real-time energy management. HRES configurations for cost-effectiveness, environmental sustainability, and operational reliability while also emphasizing the transformative potential of emerging technologies such as quantum computing are underscored. This review provides critical insights into the evolving landscape of HRES optimization, offering actionable recommendations for future research and practical applications in achieving global energy sustainability goals.

关键词： hybrid renewable energy systems optimization techniques optimization criteria computation efficiency sustainable energy innovations

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multi-objective multi-Dimensional Resource Allocation for Categorized QoS Provisioning in Beyond 5G and 6G Radio Access Networks

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第3期72卷 1790-1803页

作者： Fu, Yongqin Wang, Xianbin Fang, Fang Western Univ Dept Elect & Comp Engn London ON N6A 5B9 Canada Western Univ Dept Comp Sci London ON N6A 5B9 Canada

To effectively meet the diverse Quality of Service (QoS) requirements from proliferating applications, a widely-adopted practical solution in radio access network (RAN) is categorized QoS provisioning, which utilizes virtual networks (i.e., tenants) to support a limited number of service categories. Apparently, one critical issue is RAN resource allocation among coexisting tenants. However, conventional single objective-based approaches cannot ensure fairness among different service categories. Moreover, except for radio resource, computing and storage resources also need to be considered. Besides, appropriate allocation of computing and storage resources could help mitigating backhaul network congestion. Hence, we aim to optimize the key QoS indicators of three main service categories and reduce backhaul bandwidth consumption simultaneously. We formulate the problem of multi-dimensional resource allocation from RAN to tenants as a multi-objective mixed-integer non-linear programming (MINLP) problem, which is challenging to solve directly due to the competing objectives and the mutual-influenced resources. For guaranteeing fairness, this problem is reformulated as a single-objective optimization problem using weighted sum approach. Moreover, a decoupling-based iterative optimization (DBIO) algorithm is proposed to decompose it into three subproblems to solve iteratively. Simulation results demonstrate that DBIO algorithm can achieve superior performance with much less time consumption, compared with three metaheuristic algorithms.

关键词： multi-objective multi-dimensional resource allocation categorized QoS provisioning radio access network mixed-integer non-linear programming decoupling-based iterative optimization

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Parallel multi objective Shortest Path Update Algorithm in Large Dynamic Networks

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第5期36卷 932-944页

作者： Shovan, S. M. Khanda, Arindam Das, Sajal K. Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA

The multi objective shortest path (MOSP) problem, crucial in various practical domains, seeks paths that optimize multiple objectives. Due to its high computational complexity, numerous parallel heuristics have been developed for static networks. However, real-world networks are often dynamic where the network topology changes with time. Efficiently updating the shortest path in such networks is challenging, and existing algorithms for static graphs are inadequate for these dynamic conditions, necessitating novel approaches. Here, we first develop a parallel algorithm to efficiently update a single objective shortest path (SOSP) in fully dynamic networks, capable of accommodating both edge insertions and deletions. Building on this, we propose DynaMOSP, a parallel heuristic for Dynamic multi objective Shortest Path searches in large, fully dynamic networks. We provide a theoretical analysis of the conditions to achieve Pareto optimality. Furthermore, we devise a dedicated shared memory CPU implementation along with a version for heterogeneous computing environments. Empirical analysis on eight real-world graphs demonstrates that our method scales effectively. The shared memory CPU implementation achieves an average speedup of 12.74x and a maximum of 57.22x, while on an Nvidia GPU, it attains an average speedup of 69.19x, reaching up to 105.39x when compared to state-of-the-art techniques.

关键词： Heuristic algorithms Pareto optimization Parallel algorithms Graphics processing units Wireless sensor networks optimization Linear programming Aerodynamics Search problems Scalability multi-objective shortest path dynamic graph GPU shared-memory SYCL programming model

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Energy and Time Trade-Off optimization for multi-UAV Enabled Data Collection of IoT Devices

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IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第6期32卷 5172-5187页

作者： Jia, Riheng Fu, Qiyong Zheng, Zhonglong Zhang, Guanglin Li, Minglu Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321017 Zhejiang Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai 200051 Peoples R China

In this work, we study the problem of dispatching multiple unmanned aerial vehicles (UAVs) for data collection in internet of things (IoT), where each UAV departs from its start point, visits some IoT devices for data collection and returns to its destination point. Considering the UAV's limited onboard energy and the time required to collect data from all IoT devices, it is essential to appropriately assign the data collection task for each UAV, such that none of the dispatched UAVs consumes excessive energy and the maximum task completion time among all UAVs is minimized. To optimize those two conflicting objectives, we focus on minimizing the maximum task completion time and the maximum energy consumption among all UAVs, by jointly designing the flight trajectory, hovering positions for data collection and flight speed of each UAV. We formulate this problem as a multi-objective optimization problem with the aim of obtaining a set of Pareto-optimal solutions in terms of time or energy dominance. Due to the NP-hardness and complexity of the formulated problem, we propose a multi-strategy multi-objective ant colony optimization algorithm (MSMOACO), which is developed based on a constrained ant colony optimization algorithm with a fitnessguided mutation strategy and an adaptive hovering strategy being delicately incorporated, to solve the problem. To accommodate the practical scenario, we also design a novel geometry-based collision avoidance strategy to reduce the possibility of collisions among UAVs. Extensive evaluations validate the effectiveness and superiority of the proposed MSMOACO, compared with previous approaches.

关键词： multi-UAV network multi-objective optimization cooperative trajectory planning speed control

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Soft computing approaches for dynamic multi-objective evaluation of computational offloading: a literature review

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第9期27卷 12459-12481页

作者： Khan, Sheharyar Jiangbin, Zheng Ali, Hassan Northwestern Polytech Univ Sch Software Xian 710072 Peoples R China Northwestern Polytech Univ Sch Management Xian 710072 Peoples R China

Optimizing computational offloading in Mobile Edge Computing (MEC) environments presents a multifaceted challenge requiring innovative solutions. Soft computing, recognized for its ability to manage uncertainty and complexity, emerges as a promising approach for addressing the dynamic multi-objective evaluation inherent in computational offloading scenarios. This paper conducts a comprehensive review and analysis of soft computing approaches for Dynamic multi-objective Evaluation of Computational Offloading (DMOECO), aiming to identify trends, analyze existing literature, and offer insights for future research directions. Employing a systematic literature review (SLR) methodology, we meticulously scrutinize 50 research articles and scholarly publications spanning from 2016 to November 2023. Our review synthesizes advancements in soft computing techniques, including fuzzy logic, neural networks, evolutionary algorithms, and probabilistic reasoning, as applied to computational offloading optimization within MEC environments. Within this comprehensive review, existing approaches are categorized and analyzed into distinct research lines based on methodologies, objectives, evaluation metrics, and application domains. The evolution of soft computing-based DMOECO strategies is emphasized, showcasing their effectiveness in dynamically balancing various computational objectives, including energy consumption, latency, throughput, user experience, and other pertinent factors in computational offloading scenarios. Key challenges, including scalability issues, lack of real-world deployment validation, and the need for standardized evaluation benchmarks, are identified. Insights and recommendations are provided to enhance computational offloading optimization. Furthermore, collaborative efforts between academia and industry are advocated to bridge the theoretical developments with practical implementations. This study pioneers the use of SLR methodology, offering valuable perspec

关键词： Soft computing Mobile cloud computing Computational offloading Hybrid computing Systematic literature review

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Balancing water, power, and carbon: A synergistic optimization framework for mega cascade reservoir operations

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RENEWABLE ENERGY 2025年 243卷

作者： Ning, Zhihao Zhou, Yanlai He, Juntao Tang, Chun Xu, Chong-Yu Chang, Fi-John Wuhan Univ State Key Lab Water Resources Engn & Management Wuhan 430072 Peoples R China Univ Oslo Dept Geosci POB 1047 Blindern N-0316 Oslo Norway Natl Taiwan Univ Dept Bioenvironm Syst Engn Taipei 106216 Taiwan

This study addresses the critical intersection of renewable energy production and carbon emission reduction in the context of intensified human activities and global climate change by proposing an innovative optimization framework for mega cascade reservoirs. Unlike traditional approaches that often prioritize hydropower output or carbon emissions, our framework uniquely integrates a multi-objective optimization model that simultaneously minimizes carbon emissions, mitigates flood risk, and maximizes hydropower output within the physical constraints of cascade reservoir operations. To assess the performance of various impoundment schemes, we apply the Technique for Order Preference by Similarity to Ideal Solution, demonstrating the model's versatility across different inflow scenarios using seven cascade reservoirs in the Yangtze River as case studies. Our findings reveal that, compared to practical operation scheme, the optimal scheme enhances hydropower output by 5.82 billion kW & sdot;h/a (5.32 %), increases water supply by 2.68 billion m3 (8.00 %), reduces carbon emissions by 17.31 GgC/a (14.66 %), and lowers carbon intensity by 0.63 kgCO2e/MW & sdot;h (15.22 %). This research advances theoretical frameworks for reservoir operations and offers practical implications for policymakers, enabling more informed decision-making to achieve sustainable development goals. The novel integration of water-carbon synergies within reservoir management contributes significantly to the discourse on sustainable energy systems and climate resilience.

关键词： Cascade reservoirs Hydropower optimization Carbon emission reduction multi-objective operation Non-dominated sorting genetic algorithm II (NSGA-II) Sustainable water management

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A fast convergence EO-based multi-objective optimization algorithm using archive evolution path and its application to engineering design problems

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JOURNAL OF SUPERCOMPUTING 2023年第16期79卷 18849-18885页

作者： Ghazaan, Majid Ilchi Ghaderi, Pedram Rezaeizadeh, Amirali Iran Univ Sci & Technol Sch Civil Engn POB 16846-13114 Tehran Iran

Time-consuming objective functions are inevitable in practical engineering optimization problems. This kind of function makes the implementation of metaheuristic methodologies challenging since the designer must compromise between the quality of the final solution and the overall runtime of the optimization procedure. This paper proposes a novel multi-objective optimization algorithm called FC-MOEO/AEP appropriate for highly time-consuming objective functions. It is based on an equilibrium optimizer equipped with an archive evolution path (AEP) mechanism. The AEP mechanism considers the evolutionary trajectory of the decision space and anticipates the potential regions for optimal solutions. Besides having a high convergence rate, the newly proposed approach also possesses an intelligent balance between exploration and exploitation capabilities, enabling the algorithm to effectively avoid getting stuck in the local Pareto. To assess the efficacy of the FC-MOEO/AEP, a range of mathematical optimization problems and three real-world structural design problems were employed. In order to gauge the method's performance against other approaches, a novel performance indicator known as convergence speed was introduced and utilized, in addition to standard metrics. The numerical findings demonstrate the robust and consistent performance of the FC-MOEO/AEP in tackling complex multi-objective problems.

关键词： multi-objective optimization Metaheuristic algorithm Evolution path Structural optimization Performance metrics Fast convergence

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Operational synchromodal transport planning methodologies: Review and roadmap

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 194卷

作者： Zhang, Yimeng Tan, Xiangrong Gan, Mi Liu, Xiaobo Atasoy, Bilge Southwest Jiaotong Univ Sch Transportat & Logist Chengdu Peoples R China Natl Engn Lab Integrated Transportat Big Data Appl Chengdu Peoples R China Delft Univ Technol Dept Maritime & Transport Technol Delft Netherlands Delft Univ Technol Dept Transport & Planning Delft Netherlands

This review aims to explore the potential for synchromodal transport planning at the operational level. Synchromodal transport planning involves the optimization of the movement of freights across multiple transport modes, with the objective of minimizing cost, improving efficiency, and promoting sustainability. Through this review, we provide a roadmap for methodological developments in the area of operational synchromodal transport planning research. The roadmap provides a comprehensive categorization of different fields and their trends. The fundamentals of synchromodal transport planning are evolved to more flexible planning approaches that take practical considerations and multiple objectives into account. Dynamic planning is evolving to become more adaptive and resilient to changing environments. Finally, collaborative planning will continue to integrate both vertical and horizontal collaboration with distributed optimization approaches. With dynamic and collaborative approaches considering preferences, the full potential of synchromodal transport planning can be unlocked towards efficient and sustainable freight transportation.

关键词： Synchromodal transport planning Operational planning multi-criteria optimization Dynamic optimization Collaborative planning

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A multi period portfolio optimization: Incorporating stochastic predictions and heuristic algorithms

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APPLIED SOFT COMPUTING 2025年 170卷

作者： Ahmadi, Seyedeh Asra Ghasemi, Peiman German Univ Technol Oman Dept Logist Tourism & Serv Management Muscat Oman Univ Vienna Dept Business Decis & Analyt Kolingasse 14-16 A-1090 Vienna Austria

In the field of economics and financial markets, optimal asset allocation strategies are essential for investor satisfaction and success. This paper delves into the complex landscape of multi-period portfolio selection, where the objective is to maximize wealth while minimizing investment risk. The core challenge of this research lies in addressing the complexity and uncertainty inherent in multi-period portfolio selection under stochastic conditions. The study introduces a framework for multi-period portfolio selection, considering N risky assets over T time periods. Stochastic return rates are modeled using a stochastic distribution, with the objective of maximizing wealth under risk constraints. The study presents an empirical case study involving the S&P500 market index, demonstrating the applicability of the proposed approach. Utilizing a random forest model, the paper predicts future returns, incorporating these predictions into a deterministic model via chance constraints. The contributions of the paper are substantial and multifaceted. Firstly, it introduces bankruptcy constraints, providing a more realistic approach to portfolio optimization and addressing an often-overlooked aspect of financial modeling. Secondly, transaction costs, a critical consideration in real-world scenarios, are integrated into the model, significantly enhancing the accuracy and practical relevance of portfolio optimization strategies. Thirdly, uncertainty management is rigorously tackled through stochastic approaches, ensuring the development of robust strategies that can accommodate varying market conditions. The paper also introduces risk-adjusted performance measures, enabling more informed decision-making by considering both risk and returns. Innovatively, this paper employs the Random Forest technique to predict return rates, thereby substantially enhancing the precision of investment predictions. Additionally, the Root System Growth Algorithm adds a heuristic dimension to pro

关键词： multi-Period Portfolio optimization (MPPO) Stochastic chance constraint Random Forest Root System Growth Algorithm (RSGA) Bankruptcy constraints

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