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Adaptive multiple selection strategy for multi-objective particle swarm optimization

INFORMATION SCIENCES 2023年第1期624卷 235-251页

作者： Han, Honggui Zhang, Linlin Yinga, A. Qiao, Junfei Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Beijing Univ Technol Engn Res Ctr Digital Community Minist Educ Beijing 100124 Peoples R China China Natl Heavy Duty Truck Grp Co LTD Automot Res Inst Jinan 250102 Peoples R China Minist Publ Secur Res Inst 1 Beijing 100048 Peoples R China

multiple-swarm approach is a quite successful evolutionary computation framework for multi-objective particle swarm optimization algorithm (MOPSO) to solve multi-objective optimization problems (MOPs). However, the main challenge of using this framework lies in the lack of leader selection, resulting in the optimal solutions being distributed loosely, as well as far away from the true Pareto-optimal front. To overcome this problem, a multi-swarm MOPSO with an adaptive multiple selection strategy (MOPSO-AMS) is investigated in this paper. This proposed MOPSO-AMS is able to guide each swarm with a suitable lea-der to improve the evolutionary performance. The novelties and advantages of MOPSO-AMS include the following three aspects. First, a hierarchical evolutionary state detection mechanism, based on the distribution and dominance information of non-dominated solu-tions, is designed to obtain the evolutionary state of current iteration. Then, the require-ments of evolutionary process can be detected. Second, an adaptive multiple selection strategy, using the evolutionary state information and spatial features of candidate solu-tions, is developed to select leaders of sub-swarms in multiple evolutionary states. Then, suitable leaders can be selected to keep the balance between convergence and diversity. Third, an adaptive parameter adjustment mechanism, based on the dominance relationship of each particle, is introduced to further improve the evolutionary performance of MOPSO-AMS. Finally, numerical simulations and a practical application are used to validate the analytical results and demonstrate the significant improvement of MOPSO-AMS.(c) 2022 Elsevier Inc. All rights reserved.

关键词： multi-objective particle swarm optimization multi-objective optimization problem Adaptive multiple selection strategy Evolutionary state detection

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An approach for solving the three-objective arc welding robot path planning problem

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ENGINEERING optimization 2023年第4期55卷 650-667页

作者： Zhou, Xin Wang, Xuewu Gu, Xingsheng East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai Peoples R China

multi-objective welding robot path planning is becoming an important problem owing to the developing requirements of industrial production intelligence. An approach with path planning and optimization is proposed for solving the problem of arc welding. A rapidly exploring random tree* (RRT*)-based local path search algorithm is applied to generate a set of collision-free paths between any two welding seams, and the global search algorithm based on the decomposition-based multi-objective evolutionary algorithm (MOEA/D) framework is introduced to improve welding production efficiency by optimizing the requested contradictory objectives, namely, path length, trajectory smoothness and energy consumption. Both proposed algorithms are tested in different instances and on an actual welding workpiece, and the results prove that the proposed method could be useful in industrial production.

关键词： Welding robot path planning multi-objective optimization problem RRT MOEA D

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multi-objective optimization Approaches for Physical Layer Secure Communications Based on Collaborative Beamforming in UAV Networks

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IEEE-ACM TRANSACTIONS ON NETWORKING 2023年第4期31卷 1902-1917页

作者： Li, Jiahui Sun, Geng Kang, Hui Wang, Aimin Liang, Shuang Liu, Yanheng Zhang, Ying Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Singapore Univ Technol & Design Pillar Engn Syst & Design Singapore 487372 Singapore Northeast Normal Univ Sch Informat Sci & Technol Changchun 130024 Peoples R China Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

Unmanned aerial vehicle (UAV) communications and networks are promising technologies in the forthcoming 5G/6G wireless communications. However, they have challenges for realizing secure communications. In this paper, we consider to construct a virtual antenna array consists UAV elements and use collaborative beamforming (CB) to achieve the UAV secure communications with different base stations (BSs), subject to the known and unknown eavesdroppers on the ground. To achieve a better secure performance, the UAV elements can fly to optimal positions with optimal excitation current weights for performing CB transmissions. However, this leads to extra motion energy consumption. We formulate a physical layer secure communication multi-objective optimization problem (MOP) of UAV networks to simultaneously improve the total secrecy rates, total maximum sidelobe level (SLL) and total motion energy consumption of UAVs by jointly optimizing the positions and excitation current weights of UAVs, and the order of communicating with different BSs. Due to the complexity and NP-hardness of the formulated MOP, we propose an improved multi-objective dragonfly algorithm with chaotic solution initialization and hybrid solution update operators (IMODACH) and a parallel-IMODACH (P-IMODACH) to solve the problem. Simulation results verify that the proposed approaches can effectively solve the formulated MOP and it has better performance than some other benchmark algorithms and approaches. Moreover, some unexpected circumstances are considered and discussed.

关键词： Trajectory Jamming Autonomous aerial vehicles Transmitters Receivers optimization Energy consumption UAV communications collaborative beamforming physical layer security multi-objective optimization problem evolutionary computation

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multi-objective Energy Efficient Resource Allocation in Massive multiple Input multiple Output-Aided Heterogeneous Cloud Radio Access Networks

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IEEE ACCESS 2023年 11卷 33480-33497页

作者： Amani, Nahid Parsaeefard, Saeedeh Yanikomeroglu, Halim ICT Res Inst Dept Commun Technol Tehran *** Iran Univ Toronto Dept Elect & Comp Engn M5S Toronto ON Canada Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

In this work, a novel energy efficient multi-objective resource allocation algorithm for heterogeneous cloud radio access networks (H-CRANs) is proposed where the trade-off between increasing throughput and decreasing operation cost is considered. H-CRANs serve groups of users through femto-cell access points (FAPs) and remote radio heads (RRHs) equipped with massive multiple input multiple output (MIMO) connected to the base-band unit (BBU) pool via front-haul links with limited capacity. We formulate an energy-efficient multi-objective optimization (MOO) problem with a novel utility function. Our proposed utility function simultaneously improves two conflicting goals as total system throughput and operation cost. With this MOO, we jointly assign the sub-carrier, transmit power, access point (AP)(RRH/FAP), RRH, front-haul link, and BBU. To address the conflicting objectives, we convert the MOO problem into a single-object optimization problem using an elastic-constraint scalarization method. With this approach, we flexibly adjust trade-off parameters to choose between two objective functions. To propose an efficient algorithm, we deploy successive convex approximation (SCA) and complementary geometric programming (CGP) approaches. Finally, via simulation results we discuss how to select the values of trade-off parameters, and we study their effects on conflicting objective functions (i.e., throughput and operation cost in MOO problem). Simulation results also show that our proposed approach can offload traffic from C-RANs to FAPs with low transmit power and thereby reduce operation costs by switching off the under-utilized RRHs and BBUs. It can be observed from the simulation results that the proposed approach outperforms the traditional approach in which each user is associated to the AP (RRHs/FAPs) with the largest average value of signal strength. The proposed approach reduces operation costs by 30 % and increases throughput index by 25 % which in turn leads to

关键词： 5G multi-objective optimization problem elastic-constraints method

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optimization of high-dimensional expensive multi-objective problems using multi-mode radial basis functions

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COMPLEX & INTELLIGENT SYSTEMS 2025年第2期11卷 1-22页

作者： Shen, Jiangtao Wang, Xinjing He, Ruixuan Tian, Ye Wang, Wenxin Wang, Peng Wen, Zhiwen Northwestern Polytech Univ Sch Marine Sci & Technol Youyi West Rd Xian 710072 Shaanxi Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Minist Educ Key Lab Intelligent Comp & Signal Proc Jiulong Rd Hefei 230601 Anhui Peoples R China Xian Precis Machinery Res Inst Jinye Rd Xian 710072 Shaanxi Peoples R China

Numerous surrogate-assisted evolutionary algorithms are developed for multi-objective expensive problems with low dimensions, but scarce works have paid attention to that with high dimensions, i.e., generally more than 30 decision variables. In this paper, we propose a multi-mode radial basis functions-assisted evolutionary algorithm (MMRAEA) for solving high-dimensional expensive multi-objective optimization problems. To improve the reliability, the proposed algorithm uses radial basis functions based on three modes to cooperate to provide the qualities and uncertainty information of candidate solutions. Meanwhile, bi-population based on competitive swarm optimizer and genetic algorithm are applied for better exploration and exploitation in high-dimensional search space. Accordingly, an infill criterion based on multi-mode of radial basis functions that comprehensively considers the quality and uncertainty of candidate solutions is proposed. Experimental results on widely-used benchmark problems with up to 100 decision variables demonstrate the effectiveness of our proposal. Furthermore, the proposed method is applied to the structure optimization of the blended-wing-body underwater glider (BWBUG) and gets impressive solutions.

关键词： multi-objective optimization problem High-dimensional Expensive optimization Surrogate ensemble Structure design of BWBUG

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A comparative study of novel object-based geostatistical algorithm and direct sampling method on fracture network modeling

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2023年第2期37卷 777-793页

作者： Shakiba, Sima Ardejani, Faramarz Doulati Univ Tehran Coll Engn Sch Min Tehran Iran Univ Tehran Mine Environm & Hydrogeol Res Lab MEHR Lab Tehran Iran

Accurate discrete fracture network modeling is a significant requirement for fluid flow simulation in various applications such as managing groundwater resources, simulating oil and gas reservoirs, and modeling geothermal energy resources. The existing fracture network modeling approaches are often unsuccessful in regenerating spatial variability and can only characterize the fracture geometries by statistical probability distributions. In addition, the alternative geostatistical methods to address these limitations suffer from a smoothing effect and reproducing fracture patterns due to the use of the two-point statistics technique. In this paper, a comparative study between the new object-based iterative fracture network modeling algorithm and the geostatistical direct sampling (DS) method is performed. The presented algorithm starts with an initial configuration to directly model the statistical geometry of the fracture network and uses particle swarm optimization algorithm to impose four different constraints and include its spatial variability. Each constraint is defined in the form of the difference between spatial properties of the reference configuration and of the generated model using L2-norm criterion characterized by common specific filtering functions in the image processing. Both employed methods are applied on a real 2-Dimentional fracture network image from an exposed wall and their performance is assessed by four different criteria including classification correctness rate (CCR), indicator variogram (gamma), degree of consistency (r), and average relative error (ARE). Results show the superiority of the presented algorithm over the DS method in regenerating the real fracture network configuration with CCR = 0.99, r = 0.994, ARE = 2.63, and the same indicator variogram function.

关键词： Discrete fracture network Spatial characteristics Direct sampling (DS) simulation multi-objective optimization problem Particle swarm optimization

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A multi-objective Evolutionary Algorithm With Hierarchical Clustering-Based Selection

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IEEE ACCESS 2023年 11卷 2557-2569页

作者： Zhou, Shenghao Chen, Ze Li, Qi Gu, Mengjun Bao, Zhoucheng He, Wenda Sheng, Weiguo Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou 311121 Peoples R China Hangzhou Normal Univ Engn Res Ctr Mobile Hlth Management Syst Hangzhou 311121 Peoples R China China Telecom Corp Ltd Zhejiang Branch Hangzhou 310022 Peoples R China

This paper proposes an evolutionary algorithm with hierarchical clustering based selection for multi-objective optimization. In the proposed algorithm, a hierarchical clustering is employed to design the environmental and mating selections, named local coverage selection and local area selection, respectively, for multi-objective evolutionary algorithm. The local coverage selection strategy aims to preserve well-distributed individuals with good convergence. While, the local area selection strategy is devised to deliver a balanced evolutionary search. This is achieved by encouraging individuals for exploration or exploitation according to the I-epsilon+ indicator. In both strategies, a hierarchical clustering method is employed to discover the population structure. Based on the clustering results, in local coverage selection, the individuals of different clusters will be retained according to their coverage areas and crowding distances, such that distributing as evenly as possible in the Pareto front. In local area selection, the individual(s) with the best value of I-epsilon+ indicator in each cluster will be selected to perform mating, with the purpose of achieving a balanced exploration and exploitation. The proposed algorithm has been evaluated on 26 bench-mark problems and compared with related methods. The results clearly show the significance of the proposed method.

关键词： multi-objective evolutionary algorithm multi-objective optimization problem hierarchical clustering

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Tasks Scheduling with Load Balancing in Fog Computing: a Bi-level multi-objective optimization Approach

Tasks Scheduling with Load Balancing in Fog Computing: a Bi-...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Kouka, Najwa Piuri, Vincenzo Samarati, Pierangela Univ Milan Dipartimento Informat Milan Italy

ISBN: (纸本)9798400704949

Fog computing is characterized by its proximity to edge devices, allowing it to handle data near the source. This capability alleviates the computational burden on data centers and minimizes latency. Ensuring high throughput and reliability of services in Fog environments depends on the critical roles of load balancing of resources and task scheduling. A significant challenge in task scheduling is allocating tasks to optimal nodes. In this paper, we tackle the challenge posed by the dependency between optimally scheduled tasks and the optimal nodes for task scheduling and propose a novel bi-level multi-objective task scheduling approach. At the upper level, which pertains to task scheduling optimization, the objective functions include the minimization of makespan, cost, and energy. At the lower level, corresponding to load balancing optimization, the objective functions include the minimization of response time and maximization of resource utilization. Our approach is based on an Improved multi-objective Ant Colony algorithm (IMOACO). Simulation experiments using iFogSim confirm the performance of our approach and its advantage over existing algorithms, including heuristic and meta-heuristic approaches.

关键词： Task scheduling Load-balancing fog computing multi-objective optimization problem and ant colony optimization

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An Evolutionary Algorithm with Variable-Length Chromosome for multi-objective Minimalistic Attack 2

An Evolutionary Algorithm with Variable-Length Chromosome fo...

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2nd IEEE Conference on Artificial Intelligence (CAI)

作者： Zhou, Chengyu Hou, Yaqing Ma, Wenqiang Yu, Hua Ge, Hongwei Dalian Univ Technol Dept Comp Sci Dalian Peoples R China

ISBN: (纸本)9798350354102;9798350354096

Evolutionary Algorithms (EAs) have acquired significant achievements in multi-objective optimization. Canonical EAs are mainly based on fixed-length chromosome. However, certain optimization problems require variable-length chromosome based EAs to solve. In this paper, our interest lies in solving the minimalistic attack problem which is formulated as a multi-objective optimization problem aiming to apply perturbations on the input (pictures) of well-trained deep reinforcement learning (DRL) policies. The objective is to mislead the DRL agent to alter its predictions. To achieve this, we propose a novel evolutionary algorithm with variable-length chromosome that dynamically adapts the chromosome length. Experiments show that the proposed algorithm converges more quickly and achieves better results than the baseline algorithm.

关键词： multi-objective optimization problem evolutionary algorithm variable length chromosome multi-objective minimalistic attack

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3D Path Planning of the Solar Powered UAV in the Urban-Mountainous Environment with multi-objective and multi-Constraint Based on the Enhanced Sparrow Search Algorithm Incorporating the Levy Flight Strategy

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Guidance, Navigation and Control 2024年第1期4卷 149-175页

作者： Pengyang Xie Ben Ma Bingbing Wang Jian Chen Gang Xiao College of Engineering China Agricultural UniversityBeijing 100083P.R.China State Key Laboratory of Clean Energy Utilization Zhejiang UniversityHangzhou 310027P.R.China Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhou 310058P.R.China Key Laboratory of Smart Agricultural Technology(Yangtze River Delta) Ministry of Agriculture and Rural AffairsNanjing 210044P.R.China Key Laboratory of Smart Agricultural Technology in Tropical South China Ministry of Agriculture and Rural AffairsGuangzhou 510642P.R.China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191P.R.China

In response to practical application challenges in utilizing solar-powered unmanned aerial vehicle(UAV)for remote sensing,this study presents a three-dimensional path planning method tailored for urban-mountainous *** into account constraints related to the solar-powered UAV,terrain,and mission objectives,a multi-objective trajectory optimization model is transferred into a single-objective optimization problem with weight factors and multiconstraint and is developed with a focus on three key indicators:minimizing trajectory length,maximizing energy flow efficiency,and minimizing regional risk ***,an enhanced sparrow search algorithm incorporating the Levy flight strategy(SSA-Levy)is introduced to address trajectory planning challenges in such complex *** simulation,the proposed algorithm is compared with particle swarm optimization(PSO)and the regular sparrow search algorithm(SSA)across 17 standard test functions and a simplified simulation of urban-mountainous *** results of the simulation demonstrate the superior effectiveness of the designed improved SSA based on the Levy flight strategy for solving the established single-objective trajectory optimization model.

关键词： Solar powered UAV multi-objective optimization problem single-objective optimization problem multi-constraint sparrow search algorithm Levy flight strategy

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