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distributed genetic algorithm for application placement in the compute continuum leveraging infrastructure nodes for optimization

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 160卷 154-170页

作者： Guerrero, Carlos Lera, Isaac Juiz, Carlos Univ Balear Isl Comp Sci Dept Crta Valldemossa Km 7-5 E-07121 Palma De Mallorca Spain

The increasing complexity of Compute Continuum environments calls for efficient resource optimization techniques. In this paper, we propose and evaluate three distributed designs of a genetic algorithm (GA) for resource optimization, within an increasing degree of distribution. The designs leverage the execution of the GA in the infrastructure devices themselves by dealing with the specific features of this domain: constrained resources and wide geographical distribution of the devices. For their evaluation, we implemented a benchmark case using the NSGA-II for the specific problem of optimizing the application placement, according to the guidelines of our three distributed designs. These three experimental scenarios were compared against a control case, representing a traditional centralized version of this GA algorithm, evaluating solution quality and network overhead. The results show that the design with the lowest distribution degree, which keeps centralized storage of the objective space, achieves comparable solution quality to the traditional approach but incurs a higher network load. The second design, which completely distributes the population between the workers, reduces network overhead but exhibits lower solution diversity while keeping enough good results in terms of optimization objective minimization. The second design demonstrates the highest overall efficiency in optimization performance and network cost. Finally, the proposal with a distributed population that only interchanges solutions between the workers' neighbors achieves the lowest network load but with compromised solution quality.

关键词： Cloud-edge continuum Fog computing distributed genetic algorithm Resource optimization Multi-objective optimization Application placement

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Patient assignment optimization in cloud healthcare systems: a distributed genetic algorithm

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HEALTH INFORMATION SCIENCE AND SYSTEMS 2023年第1期11卷 30页

作者： Pang, Xinyu Ge, Yong-Feng Wang, Kate Traina, Agma J. M. Wang, Hua Guangdong Technion Israel Inst Technol Shantou Peoples R China Victoria Univ Inst Sustainable Ind & Liveable Cities Melbourne Australia RMIT Univ Sch Hlth & Biomed Sci Melbourne Australia Univ Sao Paulo Inst Math & Comp Sci Sao Paulo Brazil

Integrating Internet technologies with traditional healthcare systems has enabled the emergence of cloud healthcare systems. These systems aim to optimize the balance between online diagnosis and offline treatment to effectively reduce patients' waiting times and improve the utilization of idle medical resources. In this paper, a distributed genetic algorithm (DGA) is proposed as a means to optimize the balance of patient assignment (PA) in cloud healthcare systems. The proposed DGA utilizes individuals as solutions for the PA optimization problem and generates better solutions through the execution of crossover, mutation, and selection operators. Besides, the distributed framework in the DGA is proposed to improve its population diversity and scalability. Experimental results demonstrate the effectiveness of the proposed DGA in optimizing the PA problem within the cloud healthcare systems.

关键词： Patient assignment distributed genetic algorithm Cloud healthcare system Evolutionary algorithm

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Enhancing the curvature mode shape method for structural damage severity estimation by means of the distributed genetic algorithm

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ENGINEERING OPTIMIZATION 2021年第4期53卷 683-701页

作者： Bagherkhani, Ali Baghlani, Abdolhossein Shiraz Univ Technol Dept Civil & Environm Engn Shiraz Iran

An important challenge in damage detection based on the curvature mode shape (CMS) method is recognizing damage severity in damaged structures. Here, an efficient approach is proposed based on the finite element (FE) method, which detects the existence, location and severity of damage using the distributed genetic algorithm in conjunction with CMS. First, using the mode shape of the structure, curvature is calculated at the nodes of the structure in the FE model. By comparing the curvature between the intact and damaged structures, the existence and location of damage are determined. Next, by defining an optimization problem, damage severity in the damaged elements is determined. Results of applying the proposed approach on beam and frame structures under various damage scenarios and an experimental set-up show that this method is very effective in damage detection of such structures. Furthermore, the performance and accuracy of the proposed method are examined for various noise levels.

关键词： Damage detection curvature mode shape optimization distributed genetic algorithm finite element method

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distributed genetic algorithm on Cluster of Intel Xeon Phi Co-processors 1

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5th International Conference on Future Data and Security Engineering (FDSE)

作者： Nguyen Quang-Hung Anh-Tu Ngoc Tran Nam Thoai Vietnam Natl Univ Ho Chi Minh City Ho Chi Minh City Univ Technol Fac Comp Sci & Engn Ho Chi Minh City Vietnam

ISBN: (数字)9783030031923

ISBN: (纸本)9783030031923;9783030031916

This paper presents a study to parallelize genetic algorithm on cluster of Intel Xeon Phi co-processors. Our study investigates using hybrid MPI and OpenMP to parallelize genetic algorithm (GA) onto high performance computing (HPC) servers included Intel Xeon Phi co-processors. We will revise sequential GA and parallelize the GA into parallel GA, which a highly portable, modern code and optimized in any MIC architectures like Knights Corner (KNC), Knights Landing (KNL), or even the up-coming processors of Intel like Knights Hill by using Intel compiler, MPI and several OpenMP directives. Our implementation is experimented on built-in 50-TFlops Supernode-XP, which is cluster of HPC servers (each server has two Intel Xeon Phi cards). Our simulated result shows that the proposed distributed GA onto cluster of Intel Xeon Phi co-processors (MICs) reduces significant execution time in comparison with sequential GA.

关键词： distributed genetic algorithm Intel Xeon Phi MIC

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A Driving Behavior Awareness Model based on a Dynamic Bayesian Network and distributed genetic algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2018年第1期11卷 469-482页

作者： Xie, Guotao Gao, Hongbo Huang, Bin Qian, Lijun Wang, Jianqiang Hefei Univ Technol Dept Automot Engn Hefei 230009 Anhui Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Collaborat Innovat Ctr Elect Vehicles Beijing Beijing 100084 Peoples R China

It is necessary for automated vehicles (AVs) and advanced driver assistance systems (ADASs) to have a better understanding of the traffic environment including driving behaviors. This study aims to build a driving behavior awareness (DBA) model that can infer driving behaviors such as lane change. In this study, a dynamic Bayesian network DBA model is proposed, which includes three layers, namely, the observation, hidden and behavior layer. To enhance the performance of the DBA model, the network structure is optimized by employing a distributed genetic algorithm (GA). Using naturalistic driving data in Beijing, the comparison between the optimized model and other non-optimized models such as the hidden Markov model (HMM) and HMM with a mixture of Gaussian outputs (GM-HMM) indicates that the optimized model could estimate driving behaviors earlier and more accurately.

关键词： Automated Vehicle Advanced Driver Assistance System Driving Behavior Awareness Dynamic Bayesian Network distributed genetic algorithm

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Design and Implementation of a Hybrid Intelligent System Based on Particle Swarm Optimization, Hill Climbing and distributed genetic algorithm for Node Placement Problem in WMNs: A Comparison Study 32

Design and Implementation of a Hybrid Intelligent System Bas...

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32nd IEEE International Conference on Advanced Information Networking and Applications (AINA)

作者： Sakamoto, Shinji Barolli, Admir Barolli, Leonard Takizawa, Makoto FIT Grad Sch Engn Higashi Ku 3-30-1 Wajirohigashi Fukuoka Fukuoka 8110295 Japan Aleksander Moisiu Univ Durres Dept Informat Technol L-1Rruga Currilave Durres Albania FIT Dept Informat & Commun Engn Higashi Ku 3-30-1 Wajirohigashi Fukuoka Fukuoka 8110295 Japan Hosei Univ Fac Sci & Engn Dept Adv Sci Kajino Machi Koganei Tokyo 1848584 Japan

ISBN: (纸本)9781538621950

The Wireless Mesh Networks (WMNs) have low cost and high speed wireless Internet connectivity, therefore they are becoming an important networking infrastructure. In our previous work, we implemented a hybrid intelligent system based on Particle Swarm Optimization (PSO) and Hill Climbing (HC), called WMN-PSOHC, and a simulation system based on genetic algorithm (GA), called WMN-GA, for solving node placement problem in WMNs. In this paper, we implement a new hybrid simulation system based on PSOHC and distributed GA (DGA), called WMN-PSOHC-DGA. We evaluate WMN-PSOHC-DGA system by computer simulations. The simulation results show that the WMN-PSOHC-DGA system has a better performance compared with WMN-PSODGA.

关键词： Wireless Mesh Networks Particle Swarm Optimization Hill Climbing distributed genetic algorithm Hybrid Intelligent algorithm

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Solving the Energy Efficient Coverage Problem in Wireless Sensor Networks: A distributed genetic algorithm Approach with Hierarchical Fitness Evaluation

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ENERGIES 2018年第12期11卷 3526-3526页

作者： Wang, Zi-Jia Zhan, Zhi-Hui Zhang, Jun Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou 510006 Guangdong Peoples R China

This paper proposed a distributed genetic algorithm (DGA) to solve the energy efficient coverage (EEC) problem in the wireless sensor networks (WSN). Due to the fact that the EEC problem is Non-deterministic Polynomial-Complete (NPC) and time-consuming, it is wise to use a nature-inspired meta-heuristic DGA approach to tackle this problem. The novelties and advantages in designing our approach and in modeling the EEC problems are as the following two aspects. Firstly, in the algorithm design, we realized DGA in the multi-processor distributed environment, where a set of processors run distributed to evaluate the fitness values in parallel to reduce the computational cost. Secondly, when we evaluate a chromosome, different from the traditional model of EEC problem in WSN that only calculates the number of disjoint sets, we proposed a hierarchical fitness evaluation and constructed a two-level fitness function to count the number of disjoint sets and the coverage performance of all the disjoint sets. Therefore, not only do we have the innovations in algorithm, but also have the contributions on the model of EEC problem in WSN. The experimental results show that our proposed DGA performs better than other state-of-the-art approaches in maximizing the number of disjoin sets.

关键词： wireless sensor networks energy efficient coverage distributed genetic algorithm

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Music recommendation system improvement using distributed genetic algorithm 8

Music recommendation system improvement using distributed ge...

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Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) / 17th International Symposium on Advanced Intelligent Systems (ISIS)

作者： Inoue, Masayoshi Takenouchi, Hiroshi Tokumaru, Masataka Kansai Univ Grad Sch 3-3-35 Yamate Cho Suita Osaka 5648680 Japan Fukuoka Inst Technol Fukuoka 8110295 Japan Kansai Univ 3-3-35 Yamate Cho Suita Osaka 5648680 Japan

ISBN: (纸本)9781509026784

Existing music recommendation systems don't suit user subjectivity and are not naturally intuitive. We previously proposed a music recommendation system to address these problems. Our music recommendation system uses Kansei agents and a Kansei retrieval system. The Kansei agents employ a Kansei model and are characterized by a three-layered neural network. Music fluctuation properties are defined as inputs to the neural networks of the Kansei agents. Our previously proposed system returns recommendations based on user subjectivity. However, this system incurs significant user evaluation load. In this study, we introduce a distributed genetic algorithm (DGA) as an optimization method to address this problem. We compare the proposed DGA method to our previous method by simulation. The simulation results indicate that the proposed DGA method significantly outperforms previous method.

关键词： distributed genetic algorithm Kansei retrieval Music recommendation system Neural network

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distributed Stochastic algorithm Based on Enhanced genetic algorithm for Path Planning of Multi-UAV Cooperative Area Search

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第8期24卷 8290-8303页

作者： Zheng, Jibin Ding, Minghui Sun, Lu Liu, Hongwei Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Dalian Maritime Univ Inst Informat Sci Technol Dept Commun Engn Dalian 16026 Peoples R China

Multiple unmanned aerial vehicle (Multi-UAV) cooperative area search is an important and effective means of intelligence acquisition and disaster rescue. Search path planning is a critical factor to improve multi-UAV search performance. Aiming at the search inefficiency resulting from insufficient cooperation between UAVs in existing researches, we present a novel distributed real-time search path planning method based on distributed model predictive control (DMPC) framework. Firstly, we formulate the overall search objective function in finite time domain, considering not only repeated searches, but also maintenance of connectivity and collision avoidance between UAVs. Secondly, we decompose the overall search objective function to establish a distributed constrained optimization problem (DCOP) model, so that all UAVs optimize the overall search objective by interacting with neighbors. Thirdly, aiming at the problem of falling into the local optima in existing algorithms, distributed stochastic algorithm based on enhanced genetic algorithm (DSA-EGA) is proposed to solve the established DCOP model. We design a point crossover operator and introduce anytime local search (ALS) framework that stores the global optimal solution explored. Finally, the simulation results of different benchmark problems demonstrate that the proposed DSA-EGA outperforms other state-of-the-art algorithms in terms of the quality of solution. The simulation results of cooperative area search problems illustrate that the established DCOP model improves the search efficiency by 7.7%, and DSA-EGA improves the search efficiency by 4.3% at least. In addition, we also verify that our method has high scalability.

关键词： Multi-UAV cooperative area search distributed model predictive control distributed constrained optimization problem distributed genetic algorithm

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DSGA: A distributed Segment-Based genetic algorithm for Multi-Objective Outsourced Database Partitioning

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INFORMATION SCIENCES 2022年 612卷 864-886页

作者： Ge, Yong-Feng Zhan, Zhi-Hui Cao, Jinli Wang, Hua Zhang, Yanchun Lai, Kuei-Kuei Zhang, Jun Victoria Univ Inst Sustainable Ind & Liveable Cities Melbourne 3011 Australia South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China La Trobe Univ Dept Comp Sci & Informat Technol Melbourne 3083 Australia Peng Cheng Lab Shenzhen 518055 Peoples R China Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou 510006 Peoples R China Chaoyang Univ Technol Dept Business Adm Taichung 413 Taiwan Hanyang Univ Ansan 15588 South Korea

The outsourced distributed database is frequently used to tackle the large amounts of data in various smart city scenarios. The data partition technique is a significant research topic in the outsourced distributed database because it can directly affect the database perfor-mance in data exchange and sharing. Multiple objectives, including communication cost, load balance, and data privacy, should be considered during data partition. Previous approaches remain narrow in dealing with one of these objectives. However, the emphasis on any single objective cannot improve the entire performance of the outsourced dis-tributed database. In this paper, a distributed segment-based genetic algorithm (DSGA) is proposed, which can protect data privacy as well as achieve the trade-off between com-munication cost and load balance. For privacy protection, a digit-based anonymity strategy is proposed based on the characteristic of attributes, which can maintain information integrity and achieve fuzzy identification. After that, a three-layer distributed framework is proposed for the multi-objective optimization to enhance the search efficiency and to achieve the trade-off between communication cost and load balance. Specifically, two segment-based operators, i.e., segment-based recombination and segment-based muta-tion, are proposed to sufficiently exchange evolutionary information to accelerate the con-vergence speed and to maintain population diversity to cover the whole multi-objective space, respectively. The performance of the proposed DSGA is verified in terms of solution accuracy and convergence speed. The effect of proposed strategies and operators in DSGA is also confirmed.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Multi-objective optimization Evolutionary computation Database partitioning distributed genetic algorithm Smart cities

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