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An optimization-based Monte Carlo method for estimating the two-terminal survival signature of networks with two component classes

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NAVAL RESEARCH LOGISTICS 2025年第2期72卷 169-186页

作者： da Silva, Daniel B. Lopes Sullivan, Kelly M. Clemson Univ Dept Ind Engn Clemson SC USA Univ Arkansas Dept Ind Engn Fayetteville AR 72701 USA

Evaluating two-terminal network reliability is a classical problem with numerous applications. Because this problem is #P$$ \#\mathsf{P} $$-Complete, practical studies involving large systems commonly resort to approximating or estimating system reliability rather than evaluating it exactly. Researchers have characterized signatures, such as the destruction spectrum and survival signature, which summarize the system's structure and give rise to procedures for evaluating or approximating network reliability. These procedures are advantageous if the signature can be computed efficiently;however, computing the signature is challenging for complex systems. With this motivation, we consider the use of Monte Carlo (MC) simulation to estimate the survival signature of a two-terminal network in which there are two classes of i.i.d. components. In this setting, we prove that each MC replication to estimate the signature of a multi-class system entails solving a multi-objective maximum capacity path problem. For the case of two classes of components, we adapt a Dijkstra's-like bi-objective shortest path algorithm from the literature for the purpose of solving the resulting bi-objective maximum capacity path problem. We perform computational experiments to compare our method's efficiency against intuitive benchmark approaches. Our computational results demonstrate that the bi-objective optimization approach consistently outperforms the benchmark approaches, thereby enabling a larger number of MC replications and improved accuracy of the reliability estimation. Furthermore, the efficiency gains versus benchmark approaches appear to become more significant as the network increases in size.

关键词： bi-objective optimization Monte Carlo simulation network reliability survival signature two-terminal reliability

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Application of multi-objective optimization in 3D Image Reconstruction

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TRAITEMENT DU SIGNAL 2024年第3期41卷 1419-1427页

作者： Zhang, Fengli Heze Univ Sch Math & Stat Heze 274015 Peoples R China

3D image reconstruction technology holds significant potential for applications in medical imaging, industrial inspection, and virtual reality, offering more intuitive and precise internal structure visualization. However, due to the complexity of human anatomy and the diversity of medical imaging data, traditional 3D reconstruction methods often struggle to achieve optimal results in terms of reconstruction accuracy, computational efficiency, and structural continuity simultaneously. The application of multi -objective optimization in 3D image reconstruction can comprehensively consider multiple objectives, providing more comprehensive and optimized reconstruction results. However, current research methods still have some deficiencies, primarily neglecting the trade-offs between different objectives and experiencing high computational load and low efficiency when handling complex medical imaging data. This study includes the development of image -target 3D reconstruction algorithms in trajectory space and the establishment and solution of a multiobjective optimization -based 3D image reconstruction model. The research content of this paper aims to improve the quality of reconstruction results and provide more reliable technical support for practical applications, in the hopes of enriching the theoretical foundation of 3D image reconstruction as well as offering new technical approaches for practical applications, having significant theoretical and practical value.

关键词： 3D image reconstruction multi-objective optimization medical imaging computational efficiency structural continuity

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Joint Spectral and Power Efficiency optimization in Uplink Radio Stripes

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第8期72卷 5209-5225页

作者： Conceicao, Filipe Gomes, Marco Silva, Vitor Dinis, Rui Antunes, Carlos Henggeler Univ Coimbra Inst Telecomun IT P-3030290 Coimbra Portugal Univ Coimbra Dept Elect & Comp Engn DEEC P-3030290 Coimbra Portugal Univ Nova Lisboa UNL Inst Telecomun IT P-2829516 Caparica Portugal Univ Nova Lisboa UNL Fac Ciencias & Tecnol FCT P-2829516 Caparica Portugal Univ Coimbra Dept Elect & Comp Engn DEEC P-3030290 Coimbra Portugal Inst Syst Engn & Comp Coimbra INESC P-3030290 Coimbra Portugal

The radio stripe (RS) concept has been emerging as a practical implementation of the cell-free (CF) massive multiple-input-multiple-output (MMIMO) network where all access points (APs) that perform the signal processing required to serve the users' equipment (UEs) share a common stripe. In such a system, an uplink (UL) power optimization must be performed to increase the system's spectral and power efficiencies (SE and PE). In this work, we develop a set of multi-objective UL power optimization models and algorithms based on the differential evolution (DE) meta-heuristic (MH) considering the optimal sequential linear processing (OSLP) and maximum ratio combining (MRC) detection schemes. The bi-objective approaches aim at deriving nondominated solutions in models to: 1) maximize the max-min fairness (MMF) and max-sum rate (MSR) metrics;2) maximize the MMF and minimize transmission power (MTP);3) maximize the MSR and minimize the MTP. A tri-objective approach is also developed to maximize the MMF and MSR and minimize the MTP. Solutions displaying interesting trade-offs are obtained with the proposed models and algorithmic approaches, compared with the case where the UEs employ full power in the UL. Results show that improved SE and PE can be attained with the proposed bi-objective and tri-objective optimization approaches.

关键词： optimization Resource management Measurement Precoding Uplink Computer architecture Programming Radio stripe (RS) uplink power optimization max-min fairness (MMF) max-sum rate (MSR) minimum transmission power (MTP) multi-objective differential evolution (DE)

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Beyond Diagonal RIS for multi-Band multi-Cell MIMO Networks: A practical Frequency-Dependent Model and Performance Analysis

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第1期24卷 749-766页

作者： de Sena, Arthur Sousa Rasti, Mehdi Mahmood, Nurul Huda Latva-aho, Matti Univ Oulu Ctr Wireless Commun Oulu 90570 Finland

This paper delves into the unexplored frequency-dependent characteristics of beyond diagonal reconfigurable intelligent surfaces (BD-RISs). A generalized practical frequency-dependent reflection model is proposed as a fundamental framework for configuring fully-connected and group-connected RISs in a multi-band multi-base station (BS) multiple-input multiple-output (MIMO) network. Leveraging this practical model, multi-objective optimization strategies are formulated to maximize the received power at multiple users connected to different BSs, each operating under a distinct carrier frequency. By relying on matrix theory and exploiting the symmetric structure of the reflection matrices inherent to BD-RISs, relaxed tractable versions of the challenging problems are achieved for scenarios with obstructed and unobstructed direct channel links. The relaxed solutions are then combined with codebook-based approaches to configure the practical capacitance values for the BD-RISs. Simulation results reveal the frequency-dependent behaviors of different RIS architectures and demonstrate the effectiveness of the proposed schemes. Notably, BD-RISs exhibit high reflection performance across the intended frequency range, remarkably outperforming conventional single-connected RISs. Moreover, the proposed optimization approaches prove effective in enabling the targeted operation of BD-RISs across one or more carrier frequencies. The results also shed light on the potential for harmful interference in the absence of synchronization between RISs and adjacent BSs.

关键词： Beyond diagonal reconfigurable intelligent surface (BD-RIS) frequency-dependent RIS multi-band MIMO networks Beyond diagonal reconfigurable intelligent surface (BD-RIS) frequency-dependent RIS multi-band MIMO networks

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On optimistic, pessimistic and mixed fuzzy-programming based approaches to solve multi-objective fully intuitionistic fuzzy linear fractional programming problems

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ANNALS OF OPERATIONS RESEARCH 2025年第2期346卷 1399-1443页

作者： Malik, Manisha Gupta, S. K. Indian Inst Technol Roorkee Dept Math Roorkee 247667 Uttarakhand India

A decision-maker often encounters a state of uncertainty as well as hesitancy due to various unpredictable factors while solving real-world optimization problems. Due to ambiguity in the decision-making process, the parameters and decision variables of an optimization model generally fail to have the exact values. To deal with such realistic uncertain conditions, the concept of intuitionistic fuzzy numbers has been employed widely. In this article, a model of the linear fractional programming problem having multiple objectives with all the parameters and decision variables expressed as intuitionistic fuzzy numbers has been presented. To solve the problem, we first linearize the fractional model using appropriate transformations, and thereafter utilizing the accuracy function and intuitionistic fuzzy programming, the equivalent optimization model is obtained. The conflicting nature of the multiple objectives in the linearized model has been handled using the linear and exponential membership/non-membership functions applying normal, optimistic, pessimistic, and mixed approaches. Moreover, to validate the various equivalent constructions of the original problem, the underlying theorems are also proved at the relevant places. In last, a practical application related to production process in the textile industry has been constructed and solved using the proposed algorithm, and further, a comparison is drawn among various approaches to establish the efficacy of the proposed modelling and developed algorithm.

关键词： multi-objective optimization Intuitionistic fuzzy numbers Membership functions Linear fractional optimization Intuitionistic fuzzy programming Optimistic and pessimistic approaches

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multi-objective Patient Appointment Scheduling Framework (MO-PASS): a data-table input simulation-optimization approach

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2023年第4期99卷 363-383页

作者： Dehghanimohammadabadi, Mohammad Rezaeiahari, Mandana Seif, Javad Northeastern Univ Dept Mech & Ind Engn Boston MA USA Univ Arkansas Med Sci Fay W Boozman Coll Publ Hlth Little Rock AR USA Calif State Polytech Univ Pomona Dept Ind & Mfg Engn Pomona CA USA Northeastern Univ Dept Mech & Ind Engn 360 Huntington Ave Boston MA 02115 USA

Appointment scheduling is one of the critical factors for improving patient satisfaction with healthcare services. A practical and robust appointment scheduling solution allows clinics to efficiently utilize medical devices, equipment, and other resources. This study introduces a multi-objective Patient Appointment Scheduling (MO-PASS) framework to enhance clinic operations and quality of care. The proposed framework integrates three modules: (1) optimization (using MATLAB), (2) Data-Exchange (MS Excel), and (3) Simulation (Simio). To implement MO-PASS, the multi-objective Particle Swarm optimization (MOPSO) algorithm is coded in MATLAB, and a Simio API is developed, which exchanges simulated scenarios with MOPSO via Excel. The efficiency of the proposed framework is evaluated in a breast cancer clinic with multiple physicians and patient types. Two objective functions are defined for evaluating the solutions of the AS problem: (1) minimizing the total service time and (2) maximizing the number of (admitted) patients with zero overtime. Finally, the performance of MO-PASS is tested against three heuristic approaches with respect to objective functions. The computational experiment results show that the proposed MO-PASS outperforms the existing heuristic benchmarks. Also, the framework is accompanied by all the necessary details to make it practical and easy to implement.

关键词： multi-appointment scheduling template scheduling multi-objective scheduling simulation-based optimization simheuristics multi-objective Particle Swarm optimization (MOPSO)

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

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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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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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