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An Inclined Scalable Grid System and an Improved heuristic algorithm for Multisatellite Joint Regional Coverage

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Li, Feng Wan, Qiuhua Li, Da Zhong, Xing Wen, Feifei He, Qien Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Daheng Coll Beijing 100049 Peoples R China Chang Guang Satellite Technol Co Ltd Changchun 130102 Peoples R China

Discrete grid systems are crucial for completing satellite observation tasks. However, there is currently no grid system dedicated to multisatellite joint regional coverage (MSJRC). In this letter, a new inclined grid system named Inclined Scalable Grid System (isGS) considering orbit inclination is constructed based on equal-area projection. Congruent square cells endow the grid with good scalability, making it more adaptable to different swaths in a constellation. Furthermore, to quickly solve the regional coverage problem, an improved heuristic algorithm combined with isGS is designed. Elements from real satellites and target regions are used to verify the proposed methods. Experimental results indicate that the mean value of covered cells under isGS increases 51.78% at maximum and the improved algorithm speeds up by up to 80.27% while ensuring accuracy. In summary, the methods and findings presented in this letter can increase the utilization efficiency of satellite resources, increase the frequency of satellite image acquisition, and provide reliable quantitative reference for remote sensing constellation construction.

关键词： Constellation coverage equal-area cylindrical projection heuristic algorithm inclined scalable grid system

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Optimal Configuration Strategy of heuristic algorithm in Complex Memory Usage Scenarios

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Procedia Computer Science 2025年 261卷 254-261页

作者： Ruixuan Lu Jin Zhang Zhen Yao Cong Hu Jiali Sun Cuicui Zhang Qi Sun Cuiling Liu Shichuan Ding State Grid Anhui Electric power co.Ltd. Information & Telecommunication branch Hefei 230009 China School of Electrical Engineering and Automation Anhui University Hefei 230039 China

With the advent of the big data era, the demand for processing complex memory usage scenarios is increasing, and traditional memory management strategies are no longer able to meet the requirements of high efficiency. This study focuses on the optimal configuration strategy of heuristic algorithms in complex memory usage environments. Firstly, the article defines the scenarios of complex memory usage and collects the memory access patterns and load characteristics of related systems. Secondly, by comparing and analyzing different heuristic algorithms such as genetic algorithm, simulated annealing, and particle swarm optimization, the article selects the algorithm framework that is most suitable for handling large-scale memory allocation. Through extensive experiments under different memory load scenarios, the data from this study shows that the improved genetic algorithm not only improves memory utilization, but also optimizes system response time and stability under low, medium, and high load conditions. Specifically, under high load conditions, the memory utilization of this algorithm is 3% higher than that of particle swarm optimization algorithm and 5% higher than that of simulated annealing algorithm. These results confirm the effectiveness of the improved genetic algorithm in handling complex dynamic memory allocation requirements, demonstrate its potential for application in highly variable memory management scenarios, and provide new directions and methods for the future development of memory management technology.

关键词： Memory Management heuristic algorithm Configuration Strategy Improved Genetic algorithm

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LTHS: A heuristic algorithm based on local two-hop search strategy for influence maximization in social networks

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第2期41卷 3161-3172页

作者： Qiu, Liqing Yang, Zhongqi Zhu, Shiwei Gu, Chunmei Tian, Xiangbo Shandong Univ Sci & Technol Coll Comp Sci & Engn Shandong Prov Key Lab Wisdom Mine Informat Techno Qingdao Peoples R China Qilu Univ Technol Shandong Acad Sci Jinan Peoples R China Shandong Acad Sci Informat Res Inst Jinan Peoples R China Natl Tech Univ Ukraine Igor Sikorsky Kyiv Polytech Inst Kiev Ukraine

Influence maximization is a classic network optimization problem, which has been widely used in the field of viral marketing. The influence maximization problem aims to find a fixed number of active nodes. After a specific propagation model, the number of active nodes reaches the maximum. However, the existing influence maximization algorithms are overly pursuing certain indicators of efficiency or accuracy, which cannot be well accepted by some researchers. This paper proposes an effective algorithm to balance the accuracy and efficiency of the influence maximization problem called local two-hop search algorithm (LTHS). The core of the proposed algorithm is a node not only be affected by one-hop neighbor nodes, but also by two-hop neighbor nodes. Firstly, this paper selects initial seed nodes according to the characteristics of the node degree. Generally, the high degree of nodes regards as influential nodes. Secondly, this paper proposes a node two-hop influence evaluate function called two-hop diffusion value (THDV), which can evaluate node influence more accurately. Furthermore, in order to seek higher efficiency, this paper proposes a method to reduce the network scale. This paper conducted full experiments on five real-world social network datasets, and compared with other four well-known algorithms. The experimental results show that the LTHS algorithm is better than the comparison algorithms in terms of efficiency and accuracy.

关键词： Social network influence maximization local influence heuristic algorithm

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An Evolutionary Multiobjective Route Grouping-Based heuristic algorithm for Large-Scale Capacitated Vehicle Routing Problems

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第8期51卷 4173-4186页

作者： Xiao, Jianhua Zhang, Tao Du, Jingguo Zhang, Xingyi Nankai Univ Res Ctr Logist Tianjin 300071 Peoples R China Anhui Univ Sch Comp Sci & Technol Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China

The capacitated vehicle routing problem (CVRP) has been extensively investigated in the past years due to their applications in a variety of real-world scenarios. However, it is still very challenging for most existing algorithms to tackle large-scale CVRPs (LSCVRPs), namely, CVRPs with hundreds or thousands of customers. In this article, we propose a heuristic algorithm, called EMRG-HA, to address LSCVRPs based on the framework of divide and conquer, where an evolutionary multiobjective route grouping (EMRG) method is suggested to decompose an LSCVRP into small subcomponents. The suggested EMRG adopts a multiobjective evolutionary algorithm for route grouping by simultaneously optimizing three well-defined objectives, intragroup distance, intergroup distance, and intergroup balance in size, which can obtain a set of promising decompositions of LSCVRPs. Based on the EMRG, a local search method is suggested to improve the quality of routes in groups, where only routes in one group instead of in all groups are improved which is determined according to the average serving cost of routes in each group. The performance of the proposed EMRG-HA is verified on 42 test instances from three popular benchmark suites. The experimental results show that the EMRG-HA is superior over eight existing algorithms on most test instances of LSCVRPs, in terms of both computational efficiency and solution quality.

关键词： heuristic algorithms Approximation algorithms Optimization Partitioning algorithms Signal processing algorithms Scalability Vehicle routing Divide and conquer evolutionary multiobjective optimization heuristic algorithm vehicle routing problem

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A heuristic algorithm for identical parallel machine scheduling: splitting jobs, sequence-dependent setup times, and limited setup operators

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2021年第4期33卷 992-1026页

作者： Lee, Jun-Ho Kim, Hyun-Jung Chungnam Natl Univ Sch Business Daejeon 34134 South Korea Korea Adv Inst Sci & Technol Dept Ind & Syst Engn Daejeon 34141 South Korea

We examine a parallel machine scheduling problem with a job splitting property, sequence-dependent setup times, and limited setup operators, for minimizing makespan. Jobs are split into arbitrary (job) sections that can be processed on different machines simultaneously. When a job starts to be processed on a machine, a setup that requires an operator is performed, and the setup time is sequence-dependent. The number of setup operators is limited, and hence not all of the machines can be set up at the same time. For this problem, we propose a mathematical programming model and analyze a lower bound. We then develop a simple but efficient heuristic algorithm so that it can be used in practice, and analytically derive a worst-case bound of the algorithm. We finally evaluate the performance of the proposed algorithm numerically with various scenarios.

关键词： Parallel machine scheduling Job splitting Setup operators Sequence-dependent setup times heuristic algorithm

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Mobile edge server placement based on meta-heuristic algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第5期40卷 8883-8897页

作者： Guo, Feiyan Tang, Bing Zhang, Jiaming Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Peoples R China Wuhan Univ Technol Sch Entrepreneurship Wuhan Peoples R China Wuhan Univ Technol Sch Informat Engn Wuhan Peoples R China

The rapid development of the Internet of Things and 5G networks have generated a large amount of data. By offloading computing tasks from mobile devices to edge servers with sufficient computing resources, network congestion and data transmission delays can be effectively reduced. The placement of edge server is the core of task offloading and is a multi-objective optimization problem with multiple resource constraints. Efficient placement approach can effectively meet the needs of mobile users to access services with low latency and high bandwidth. To this end, an optimization model of edge server placement has been established in this paper through minimizing both communication delay and load difference as the optimization goal. Then, an Edge Server placement based on meta-heuristic algorithm (ESH-GM) has been proposed to achieve multi-objective optimization. Firstly, the K-means algorithm is combined with the ant colony algorithm, and the pheromone feedback mechanism is introduced into the placement of edge servers by emulating the mechanism of ant colony sharing pheromone in the foraging process, and the ant colony algorithm is improved by setting the taboo table to improve the convergence speed of the algorithm. Then, the improved heuristic algorithm is used to solve the optimal placement of edge servers. Experimental results using Shanghai Telecom's real datasets show that the proposed ESH-GM achieves an optimal balance between low latency and load balancing, while guaranteeing quality of service, which outperforms several existing representative approaches.

关键词： Mobile edge computing server placement heuristic algorithm performance optimization

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A Continuous Space Location Model and a Particle Swarm Optimization-Based heuristic algorithm for Maximizing the Allocation of Ocean-Moored Buoys

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IEEE ACCESS 2021年 9卷 32249-32262页

作者： Song, Miaomiao Liu, Shixuan Li, Wenqing Chen, Shizhe Li, Wenwen Zhang, Keke Yu, Dingfeng Liu, Lin Wang, Xiaoyan Qilu Univ Technol Shandong Acad Sci Inst Oceanog Instrumentat Qingdao 266001 Peoples R China Qilu Univ Technol Shandong Acad Sci Sch Ocean Technol Sci Qingdao 266001 Peoples R China Shandong Prov Key Lab Marine Monitoring Instrumen Qingdao 266001 Peoples R China Arizona State Univ Sch Geog Sci & Urban Planning Tempe AZ 85287 USA Shandong Univ Sci & Technol Coll Geodesy & Geomat Qingdao 266001 Peoples R China

Ocean-moored buoys play an important role in global ocean environment monitoring. Motivated by building a sustainable ocean buoy observational network, a spatial optimization approach is proposed to site buoy stations to maximize spatial monitoring efficiency (SME). To achieve this goal, a non-linear, continuous maximum coverage location model named CMCP-Ocean was established, associated with a measurement method of the SME. Meanwhile, a heuristic framework based on the particle swarm optimization (PSO) algorithm was built to solve the CMCP-Ocean model, and optimization strategies including the multi-core parallel computing strategy, the particle velocity updating strategy based on spatial matching, and two potential station selection strategies related to the centroid-based random radiation method (CRRM) and random grid division method (RGDM) were established to improve computing performance. The effectiveness and efficiency of the PSO-based algorithm and the CMCP-Ocean model were verified by a series of experiments;the proposed computing schema named PSO-for-CMCP-Ocean has also proven to be practical and efficient. Finally, the PSO-for-CMCP-Ocean was applied to the buoy station selection of water mass monitoring in the Laizhou Bay of China, and a multi-scale sustainable site planning solution is reported.

关键词： Oceans Monitoring Computational modeling Optimization Particle swarm optimization heuristic algorithms Biological system modeling Continuous maximal coverage problem location modeling heuristic algorithm particle swarm optimization ocean-moored buoy spatial optimization decision-support system

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Hybrid heuristic algorithm for Better Energy Optimization and Resource Utilization in Cloud Computing

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WIRELESS PERSONAL COMMUNICATIONS 2021年第1期118卷 43-73页

作者： Al-Mahruqi, Ali Abdullah Hamed Morison, Gordon Stewart, Brian G. Athinarayanan, Vallavaraj Natl Univ Sci & Technol Dept Elect & Comp Engn Muscat Oman Glasgow Caledonian Univ Sch Engn & Built Environm Glasgow G4 0BA Lanark Scotland Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Lanark Scotland

Energy-efficient execution of the scientific workflow is a challenging task in cloud computing that demands high-performance computing to process growing datasets. Due to the interdependency of tasks in the scientific workflow applications, energy-efficient resource allocation is vital for large-scale applications running on heterogeneous physical machines. Thus, this paper proposes a hybrid heuristic algorithm based energy-efficient cloud computing service (HH-ECO) that offers a significant solution for resource allocation, task scheduling, and optimization of scientific workflows. To ensure the energy-efficient execution, the HH-ECO focuses on executing non-dominant workflow tasks through adaptive mutation and energy-aware migration strategy. HH-ECO adopts the chaotic based particle swarm optimization (C-PSO) principle to optimize the resource allocation, task scheduling, and resource migration by generating the global best plans without local convergence. C-PSO with adaptive mutation avoids the deterioration of global optima while finding the best host to place the virtual machine and ensures an appropriate resource allocation plan. By considering the workflow task precedence relationships during C-PSO based task scheduling, the novel hybrid heuristic method efficiently solves the multi-objective combinatorial optimization problem without dominance among the workflow tasks. The Cloudsim based simulation study delivers superior results compared to the existing methods such as the hybrid heuristic workflow scheduling algorithm (HHWS) and distributed dynamic VM management (DDVM). The proposed approach significantly improves the optimal makespan to 38.27% and energy conservation to 38.06% compared to the existing methods.

关键词： Green cloud computing VM migration Energy-efficient resource optimization Multi-objective optimization problem heuristic algorithm Load category

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Improving the efficiency of microseismic source locating using a heuristic algorithm-based virtual field optimization method

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GEOMECHANICS AND GEOPHYSICS FOR GEO-ENERGY AND GEO-RESOURCES 2021年第3期7卷 1-18页

作者： Zhou, Jian Shen, Xiaojie Qiu, Yingui Li, Enming Rao, Dijun Shi, Xiuzhi Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Univ Politecn Madrid ETSI Minas & Energia Rios Rosas 21 Madrid 28003 Spain

Fast and accurate microseismic locating methods, such as the virtual field optimization method (VFOM), are increasingly used by researchers and mine management personnel. The VFOM can accurately locate a microseismic source under a large picking error. However, due to the complexity of the objective function of the VFOM, especially when a large number of sensors are involved, this method may require substantial time for the locating process. To overcome this problem, heuristic algorithms were used to increase the locating speed of the VFOM, and the performances of two heuristic algorithms (particle swarm optimization algorithm (PSO) and genetic algorithm (GA)) for the VFOM were evaluated. In general, the performances of these algorithms are affected by many factors, such as the Number of generations (NG) and Number of populations (NP). To enhance the performance of heuristic algorithms, a parameter tuning method was used to determine the relevant parameters for these algorithms. In contrast to the traditional gradient-based algorithm, heuristic algorithms can greatly improve the location efficiency of the VFOM with almost no loss of accuracy and can avoid falling into the local optimal value. The results showed that the PSO can provide better location accuracy and computational efficiency for the VFOM than those obtained with the GA. Furthermore, the VFOM and traditional methods were compared to discuss the influence of the number of sensors and positioning of the source on the location identification and the superiority of the VFOM-based location identification was verified.

关键词： Virtual field optimization method (VFOM) heuristic algorithm Microseism Geophysics Source location

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Cost-location aware heuristic algorithm for hybrid SDN deployment

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2021年第8-9期89卷 875-897页

作者： Wei, Siew Hong Chin, Tan Saw Kwang, Lee Ching Multimedia Univ Fac Comp & Informat Cyberjaya Malaysia Multimedia Univ Fac Engn Cyberjaya Malaysia

Software-Defined Networking (SDN) has gained tremendous attention in the past few years for its advantages over network controllability. Nonetheless, the deployment of SDN in legacy network is likely to span multi-periods over months or years for budget consideration. Network operators, especially for a network consists of thousand or more nodes, are eager to understand how legacy networks can be deploying gradually towards SDN with variety of constraints. The reliability of network should be the utmost concern during SDN deployment in legacy network. Primarily, node exclusion is essential in SDN deployment process as the exclusion of critical node during the process greatly improves network stability and reliability. Therefore, we propose a heuristic algorithm for multi-periods SDN node migration in legacy network with respect to node exclusion and budget. We first formulate the aforementioned problem in Integer Linear Programming (ILP) model, and we evaluate the proposed ILP model and our proposed heuristic algorithm with regard to solution quality and processing time. The results show that our proposed algorithm maintains its overall correctness with O(N-2) polynomial time complexity. In addition to migration sequence computation, the results of our proposed algorithm reveal the impact of each node in SDN deployment as Key Node, Critical Node and Diminishing Return Analysis. These information offers better decision for network operator during hybrid SDN deployment. Ultimately, the results of our proposed algorithm offers valuable insight in which significantly reduce up to 83% of investment while reaping the most performance gain out of SDN deployment in legacy network.

关键词： Software-defined networking heuristic algorithm Hybrid SDN deployment Integer linear programming

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