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27th World multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2023

作者： Ellili, Walid Marheel, Mahfoud Louati, Mahdi Loukil, Taïcir Moalla Computer Science Department University of Sfax Sfax3018 Tunisia

ISBN: (纸本)9781713880325

This article presents an optimization approach for the end-of-life product recycling supply chain using the Particle Swarm optimization (PSO) algorithm and the R programming language. The objective is to minimize the total cost associated with transportation, facility opening, and variable costs, while meeting the capacity constraints of collection, recycling, and landfill sites over multiple times. The PSO algorithm is utilized to find the optimal solution for the problem. The results demonstrate the effectiveness of the proposed approach in optimizing the end-of-life product recycling supply chain. The optimal solution yields a significant cost reduction compared to previous approaches. The article concludes with insights into the benefits and limitations of using the PSO algorithm and R language, and provides recommendations for future research in this area. Overall, this study contributes to the field of sustainable supply chain management by providing a practical and efficient solution for optimizing the end-of-life product recycling process. © 2023 Proceedings of World multi-Conference on Systemics, Cybernetics and Informatics, WMSCI. All rights reserved.

关键词： Optimal systems

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Parallel multi-objective optimization for expensive and inexpensive objectives and constraints

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 86卷

作者： de Winter, Roy Milatz, Bas Blank, Julian van Stein, Niki Back, Thomas Deb, Kalyanmoy Leiden Univ Leiden Inst Adv Comp Sci Niels Bohrweg 1 NL-2333 CA Leiden Netherlands Res & Dev C Job Naval Architects Reguluspl 1 NL-2132 JN Hoofddorp Netherlands Michigan State Univ Elect & Comp Engn 428 S Shaw Lane E Lansing MI 48824 USA

Expensive objectives and constraints are key characteristics of real -world multi -objective optimization problems. In practice, they often occur jointly with inexpensive objectives and constraints. This paper presents the Inexpensive objectives and Constraints Self -Adapting multi -objective Constraint optimization algorithm that uses Radial Basis function Approximations (IOC-SAMO-COBRA) for such problems. This is motivated by the recently proposed Inexpensive Constraint Surrogate -Assisted Non -dominated Sorting Genetic Algorithm II (ICSA-NSGA-II). These algorithms and their counterparts that do not explicitly differentiate between expensive and inexpensive objectives and constraints are compared on 22 widely used test functions. The IOC-SAMOCOBRA algorithm finds significantly better (identical/worse) Pareto fronts in at least 78% (6%/16%) of all test problems compared to IC-SA-NSGA-II measured with both the hypervolume and Inverted Generational Distance+ performance metric. The empirical cumulative distribution functions confirm this advantage for both algorithm variants that exploit the inexpensive constraints. In addition, the proposed method is compared against state-of-the-art practices on a real -world cargo vessel design problem. On this 17 -dimensional twoobjective practical problem, the proposed IOC-SAMO-COBRA outperforms SA-NSGA-II as well. From an algorithmic perspective, the comparison identifies specific strengths of both approaches and indicates how they should be hybridized to combine their best components.

关键词： multi-objective optimization Constraint optimization Surrogate-assisted optimization Parallel computing Ship design

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Constraint-aware and multi-objective optimization for micro-service composition in mobile edge computing

Constraint-aware and multi-objective optimization for micro-...

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作者： Wu, Jintao Zhang, Jingyi Zhang, Yiwen Wen, Yiping School of Software Nanjing University of Information Science and Technology Nanjing China School of Computer Science and Technology Anhui University Anhui China Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan University of Science and Technology Hunan China

As a new paradigm of distributed computing, mobile edge computing (MEC) has gained increasing attention due to its ability to expand the capabilities of centralized cloud computing. In MEC environments, a software application typically consists of multiple micro-services, which can be composed together in a flexible manner to achieve various user requests. However, the composition of micro-services in MEC is still a challenging research issue arising from three aspects. Firstly, composite micro-services constructed by ignoring the processing capabilities of different micro-services may cause waste of edge resources. Secondly, edge servers' limitations in terms of computational power can easily cause service occupancy between composite micro-services, severely affecting the user experience. Thirdly, in dynamic and unstable mobile environments, different edge users have different sensitivities to request latency, which increases the complexity of micro-service composition. In order to improve edge resource utilization and user experience on micro-service invocations, in this paper, we comprehensively consider the above three factors, and we first model the micro-services composition problem in MEC as a constrained multi-objective optimization problem. Then, a micro-service composition optimization method M3C combining graph search and branch-and-bound strategy is proposed to find a composition solution set with low energy consumption and high success rate for multiple edge users. Finally, we perform a series of experiments on two widely used datasets. Experimental results show that our proposed approach significantly outperforms the four competing baseline approaches, and that it is sufficiently efficient for practical deployment. © 2023 John Wiley & Sons Ltd.

关键词： multiobjective optimization

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multi-maneuver algorithms for multi-risk collision avoidance via nonconvex quadratic optimization 22

Multi-maneuver algorithms for multi-risk collision avoidance...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Masson, Matthieu Arzelier, Denis Joldes, Mioara Revelin, Bruno Thomassin, Jerome Univ Toulouse CNRS LAAS F-31400 Toulouse France Thales Serv Numer 290 All Lac F-31670 Labege France CNES 18 Ave Edouard Belin F-31400 Toulouse France

ISBN: (纸本)9781713872344

In this article, the problem of modeling and computing the optimal collision avoidance maneuvers for multiple short-term encounters is presented. Several metrics defining the overall collision risk are analyzed. These include imposing lower bounds on the miss-distance or the Mahalanobis distance at each corresponding time of closest approach (TCA) or upper-bounds on the collision probability. The avoidance maneuvers are modeled as impulsive ones in a single direction of the local frame and for a priori fixed dates for operational motivations. Station-keeping constraints are also imposed via linear inequalities on the relative states at each TCA. The specific nature of the imposed constraints is dictated by a practical framework provided by the French Space Agency (CNES). This results in formulating the maneuver design problem as an optimization problem with linear objective and non-convex quadratic constraints. Different algorithms are presented to solve this problem. Finally, the relative efficiency of the proposed approaches is evaluated and analyzed on a realistic conjunction built from data extracted from the CNES database and on academic random examples of higher complexity. Copyright (c) 2023 The Authors.

关键词： Collision Avoidance Maneuver multiple Short-term Encounters Impulsive Maneuvers Quadratically Constrained Nonconvex optimization

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A novel metamodel-based multi-objective optimization method using adaptive multi-regional ensemble of metamodels

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STRUCTURAL AND multiDISCIPLINARY optimization 2023年第4期66卷 95-95页

作者： Yin, Hanfeng Sha, Jiahui Zhou, Jun Yang, Xingfa Wen, Guilin Liu, Jie Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Hunan Peoples R China Hunan Univ Key Lab Adv Design & Simulat Tech Special Equipmen Minist Educ Changsha 410082 Hunan Peoples R China Changsha Univ Coll Mech & Elect Engn Changsha 410022 Hunan Peoples R China

The optimization of the engineering system is becoming more and more significant as time progresses, and a large number of metamodel-based multi-objective optimization methods have been proposed during the past decades. For a metamodel-based multi-objective optimization method, the accuracy of the solution is greatly influenced by the prediction accuracy of the metamodels. This study developed a novel metamodel-based multi-objective optimization method using an adaptive multi-regional ensemble of metamodels (AMEM) to get an optimization method with higher accuracy and efficiency. Two strategies were employed in this new metamodel-based multi-objective optimization method. One strategy is that several stand-alone metamodels, i.e., polynomial regression (PR), radial basis function (RBF), and Kriging (KRG) were combined into an ensemble of metamodels (EM) using the weight sum approach. The other strategy is that new design points were dynamically added into various local regions where the Pareto optimal designs are located, and each basis model and its weight factors were regenerated simultaneously, which adaptively improved the accuracy of the constructed adaptive multi-regional ensemble of metamodels. Besides, two novel approaches for selecting new design points from Pareto optimal set were proposed in this study. The performance of this novel metamodel-based multi-objective optimization method was evaluated using twelve mathematical functions and two practical engineering optimization problems. The twelve mathematical functions were typically selected from previous studies, and the two practical engineering optimization problems are crashworthiness optimization of triply periodic minimal surface (TPMS)-filled tube (one design variable) and buffer performance optimization of airbags used in the re-entry capsule (two design variables). To compare the advantages of this method, the optimization of these problems was also implemented by common metamodel-based methods. T

关键词： multi-objective optimization Adaptive Ensemble of metamodels multi-regional method Triply periodic minimal surface (TPMS)-filled tube Airbag

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An Improved multi-objective optimization Algorithm Based on Reinforcement Learning 13th

An Improved Multi-objective Optimization Algorithm Based on ...

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13th International Conference on Swarm Intelligence (ICSI)

作者： Liu, Jun Zhou, Yi Qiu, Yimin Li, Zhongfeng Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China Yingkou Inst Technol Sch Elect Engn Yingkou 115014 Peoples R China

ISBN: (纸本)9783031096778;9783031096761

multi-objective optimization (MOP) has been widely applied in various applications such as engineering and economics. MOP is an important practical optimization problem, and finding approaches to better solve it is of both practical and theoretical significance. The core of solving an MOP is to find the global optimal solution set efficiently and accurately. The current MOP algorithm has premature convergence or poor population diversity, and the solution set obtained falls easily into the local optimal or clustering phenomenon. In this study, an MOP algorithm based on the non-dominated sorting genetic algorithm based on reinforcement learning (RL-NSGA-II) is proposed;the algorithm is adopted based on the prediction, forecasting, Monte Carlo method, which is based on the action of population genetic information, and environment interaction information and Markov decision process in mathematical modeling. This is because using a non-dominated solution contains valuable information, which can be used to guide the evolution direction of the population and search the optimal solution set more accurately. The proposed RL-NSGA-II algorithm was evaluated on the ZDT and DTLZ test sets, and the experimental results verified the effectiveness of the proposed algorithm in solving MOPs.

关键词： Reinforcement learning NSGA-II Monte Carlo method multi-objective optimization problem

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multi-objective TMA management optimization using the point merge system

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AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2021年第1期93卷 15-24页

作者： Cecen, Ramazan Kursat Anadolu Univ Grad Sch Sci Alumnus Eskisehir Turkey

Purpose The purpose of this study is to provide conflict-free operations in terminal manoeuvre areas (TMA) using the point merge system (PMS), airspeed reduction (ASR) and ground holding (GH) techniques. The objective is to minimize both total aircraft delay (TD) and the total number of the conflict resolution manoeuvres (CRM). Design/methodology/approach The mixed integer linear programming (MILP) is used for both single and multi-objective optimization approaches to solve aircraft sequencing and scheduling problem (ASSP). Compromise programming and epsilon-constraint methods were included in the methodology. The results of the single objective optimization approach results were compared with baseline results, which were obtained using the first come first serve approach, in terms of the total number of the CRM, TD, the number of aircraft using PMS manoeuvres, ASR manoeuvres, GH manoeuvres, departure time updates and on-time performance. Findings The proposed single-objective optimization approach reduced both the CRM and TD considerably. For the traffic flow rates of 15, 20 and 25 aircraft, the improvement of CRM was 53.08%, 41.12% and 32.6%, the enhancement of TD was 54.2%, 48.8% and 31.06% and the average number of Pareto-optimal solutions were 1.26, 2.22 and 3.87, respectively. The multi-objective optimization approach also exposed the relationship between the TD and the total number of CRM. practical implications The proposed mathematical model can be implemented considering the objectives of air traffic controllers (ATCOs) and airlines operators. Also, the mathematical model is able to create conflict-free TMA operations and, therefore, it brings an opportunity for ATCOs to reduce frequency occupancy time. Originality/value The mathematical model presents the total number of CRM as an objective function in the ASSP using the MILP approach. The mathematical model integrates ATCOs' and airline operators' perspective together with new objective functions.

关键词： Aircraft sequencing and scheduling problem Point merge system Mixed-integer linear programming multi-objective optimization

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Optimizing UAV Network Efficiency: Integrative Strategies for Simultaneous Energy Management and Obstacle-Aware Routing

Optimizing UAV Network Efficiency: Integrative Strategies fo...

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AIAA SciTech Forum

作者： Basiri, Salar Tiwari, Dhananjay Papachristos, Christos Salapakal, Srinivasa Univ Illinois Dept Mech Sci & Engn Urbana IL 61801 USA Univ Nevada Dept Comp Sci & Engn Reno NV 89557 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This paper addresses a pivotal challenge in Unmanned Aerial Vehicle (UAV) networks crucial for sectors including delivery services, agriculture, and emergency response: optimizing UAV charging strategies for continuous operation in monitoring and inspection missions. We introduce a novel Maximum Entropy Principle (MEP) framework that employs mobile charging vehicles (MCVs), potentially Unmanned Ground Vehicles (UGVs), for in-field UAV charging. This MEP framework marks a significant advancement in the field by providing an integrated solution for multi-objective problems, including simultaneous obstacle-aware path planning and resource management under energy limitations. Distinguished from traditional heuristic approaches, our framework adeptly handles complex scenarios, significantly reducing optimization variables and facilitating robustness analysis. Through comprehensive simulations, our methodology has demonstrated significant advantages over existing algorithms in handling complex operational scenarios. Unlike other algorithms, which often struggle with large network sizes or intricate constraints, our solution consistently delivers robust performance. Empirical evidence indicates that our method achieves more than double the cost-effectiveness compared to its counterparts, coupled with multiple orders of magnitude faster operational speed. This research not only contributes to the theoretical understanding of autonomous UAV/UGV network planning but also has significant practical implications for real-time, field-deployable solutions.

关键词： Unmanned Aerial Vehicle Unmanned Ground Vehicle Entropy Path Planning Energy Management Heuristic Approach Cost Effectiveness Obstacle Avoidance Particle Swarm optimization Artificial Intelligence

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A Sensor Network Coverage Planning Based on Adjusted Single Candidate Optimizer

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Intelligent Automation & Soft Computing 2023年第9期37卷 3213-3234页

作者： Trong-The Nguyen Thi-Kien Dao Trinh-Dong Nguyen Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China University of Information Technology Ho Chi Minh CityVietnam Vietnam National University Ho Chi Minh City700000Vietnam

Wireless sensor networks(WSNs)are widely used for various practical applications due to their simplicity and *** quality of service in WSNs is greatly influenced by the coverage,which directly affects the monitoring capacity of the target ***,low WSN coverage and uneven distribution of nodes in random deployments pose significant *** study proposes an optimal node planning strategy for net-work coverage based on an adjusted single candidate optimizer(ASCO)to address these *** single candidate optimizer(SCO)is a metaheuristic algorithm with stable implementation ***,it has limitations in avoiding local optimum traps in complex node coverage optimization *** ASCO overcomes these limitations by incorporating reverse learning and multi-direction strategies,resulting in updated *** performance of the ASCO algorithm is compared with other algorithms in the literature for optimal WSN node *** results demonstrate that the ASCO algorithm offers efficient performance,rapid convergence,and expanded coverage ***,the ASCO achieves an archival coverage rate of 88%,while other approaches achieve coverage rates below or equal to 85%under the same conditions.

关键词： Wireless sensor network coverage and connection adapted single candidate optimizer objective function optimization

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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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