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

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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distributed genetic real-time pricing for multiseller-multibuyer smart grid based on bilevel programming considering random fluctuation of electricity consumption

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 135卷 359-367页

作者： Tao, Li Gao, Yan Zhu, Hongbo Liu, Songtao Univ Shanghai Sci & Technol Sch Management Shanghai 200093 Peoples R China Huaiyin Inst Technol Fac Math & Phys Huaian 223003 Peoples R China Huaian Hydrol & Water Resource Invest Bur Jiangsu Huaian 223001 Peoples R China

As reform and deregulation in the power industry proceed, more than one utility company has emerged in the power grid. In addition, due to the random fluctuation of electricity consumption, the power grid becomes more complex. New appropriate management strategies are required to handle the future smart grid. The real-time pricing (RTP) mechanism is an incentive way to increase energy usage efficiency. In this paper, considering the effect of the random fluctuation of electricity consumption, we propose a distributed genetic RTP scheme for smart grid with multiple utility companies and users based on expectation bilevel programming. We discuss the properties of the model and transform it into a deterministic optimization problem. Using a distributed genetic algorithm (GA), we find the optimal strategy for electricity supply and consumption and obtain the RTP. With this approach, subproblems can be solved individually. In addition, at each iteration, the information exchanged in the integrated system only consists of prices, the optimal electricity consumption, etc., which can be provided by each utility company and user respectively. Therefore, the distributed GA not only helps to protect the privacy of utility companies and users but also lowers the complexity of the computation. Simulation results validate the proposed distributed genetic RTP can significantly reduce peak time loading and efficiently balance system energy distribution while maximizing benefits for both utility companies and users.

关键词： Smart grid Real-time pricing Multiple utility companies and multiple users Bilevel programming distributed genetic algorithm Random fluctuation

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QDGRP : A Hybrid QoS distributed genetic Routing Protocol for Wireless Sensor Networks

QDGRP : A Hybrid QoS Distributed Genetic Routing Protocol fo...

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International Conference on Multimedia Computing and Systems (ICMCS)

作者： Koulali, Mohammed-Amine Kobbane, Abdellatif El Koutbi, Mohammed Azizi, Mostafa Mohammed V Souissi Univ Lab Syst Informat Mobiles SIME ENSIAS BP 713 Rabat Morocco Moahmmed I Univ ENSAO Oujda Morocco Moahmmed I Univ Lab MATSI Oujda Morocco

ISBN: (纸本)9781467315203

Nature-inspired routing protocols have been subject to a great research activity. Their decentralized nature and low requirements make them highly suitable for Wireless Sensor Networks (WSN) where individual nodes operate without central control and without global overview of the network status to achieve global tasks. This paper proposes a hybrid QoS routing protocol for WSN based on a customized distributed genetic algorithm (DGA) that accounts for delay and energy constraints. The proposed protocol (QDGRP) is compared to AODV w.r.t several performance metrics : end-to-end delay, throughput, packet delivery ratio and energy efficiency. The realized simulations show that QDGRP outperforms AODV and manages to achieve very good performances for various deployment scenarios.

关键词： Wireless Sensor Networks QDGRP distributed genetic algorithm Hybrid Routing AODV Quality of Service

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An implementation of new selection strategies in a genetic algorithm - population recombination and elitist refinement

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ENGINEERING OPTIMIZATION 2011年第12期43卷 1367-1384页

作者： Kwak, Noh-Sung Lee, Jongsoo Yonsei Univ Sch Mech Engn Seoul 120749 South Korea

The present study aims to develop a new genetic algorithm utilizing population recombination and elitist refinement. Population recombination determines how a population and its elitist sub-population evolve. A whole population consists of three major sub-populations: the first is the current generation's elitist sub-population, the second is obtained from the pure crossover of the elitist sub-population with another existing sub-population, and the third is the result of a crossover between the elitist and random sub-populations. genetic operations such as reproduction and crossover are applied among sub-populations during the process of population recombination. The refinement of the elitist sub-population is then implemented in order to improve the converged solution that was obtained from the recombination. The refinement of elitist sub-populations facilitates the locations of a more enhanced design by altering the binary values in chromosomes. The proposed method is verified through a number of nonlinear and/or multi-modal functions and constrained optimization problems.

关键词： distributed genetic algorithm population recombination elitist sub-population elitist refinement tail evolution and fitting

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Speed-based toll design for cordon-based congestion pricing scheme

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2013年 31卷 83-98页

作者： Liu, Zhiyuan Meng, Qiang Wang, Shuaian Monash Univ Inst Transport Studies Dept Civil Engn Clayton Vic 3800 Australia Natl Univ Singapore Dept Civil & Environm Engn Singapore 117576 Singapore Univ Wollongong Sch Math & Appl Stat Wollongong NSW 2522 Australia

The cordon-based Electronic Road Pricing (ERP) system in Singapore adopts the average travel speed as an index for evaluating the traffic congestion within a cordon area, and the maintenance of the average travel speed within a satisfactory range is taken as the objective of the toll adjustment. To formulate this practical speed-based toll design problem, this paper proposes a mathematical programming with equilibrium constraint (MPEC) model with the objective of maintaining the traffic condition in the cordon area. In the model, the network users' route choice behavior is assumed to follow probit-based stochastic user equilibrium with elastic demand, asymmetric link travel time functions and continuous value-of-time. A distributed revised genetic algorithm is designed for solving the MPEC model. Finally, a network example based on the ERP system is adopted to numerically validate the proposed models and algorithms, and further indicates that the computation speed can be improved greatly by using a distributed computing system. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Cordon-based congestion pricing Stochastic user equilibrium Elastic demand Continuous value-of-time distributed genetic algorithm

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A distributed and integrated method for cooperative mission planning of multiple heterogeneous UAVs

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AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2018年第9期90卷 1403-1412页

作者： Wu, Weinan Cui, Naigang Harbin Inst Technol Dept Aerosp Engn Harbin Heilongjiang Peoples R China

Purpose - The purpose of this paper is to develop a distributed and integrated method to get a fast and feasible solution for cooperative mission planning of multiple heterogeneous unmanned aerial vehicles (UAVs). Design/methodology/approach - In this study, the planning process is conducted in a distributed framework;the cooperative mission planning problem is reformulated with some specific constraints in the real mission;a distributed genetic algorithm is the algorithm proposed for searching for the optimal solution;genes of the chromosome are modified to adapt to the heterogeneous characteristic of UAVs;a fixed-wing UAV's six degrees-of-freedom (DOF) model with a path following method is used to test the proposed mission planning method. Findings - This method not only has the ability to obtain good feasible solutions but also improves the operating rate vastly. Research limitations/implications - This study is only applied to the case where the communication among UAVs is linked during the mission. Practical implications - This study is expected to be practical for a real mission because of its fast operating rate and good feasible solution. Originality/value - This solution is tested on a fixed-wing UAV's 6-DOF model by a path following method, so it is believable from the perspective of an autonomous UAV guidance and control system.

关键词： Heterogeneous UAVs distributed genetic algorithm Integrated solution Mission planning Specific constraints

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Video sequence segmentation using genetic algorithms

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PATTERN RECOGNITION LETTERS 2002年第7期23卷 843-863页

作者： Kim, EY Park, SH Hwang, SW Kim, HJ Chosun Univ Dept Comp Engn Dong Gu Kwangju 501759 South Korea Kyungpook Natl Univ Dept Comp Engn Puk Gu Taegu South Korea

This paper proposes a new video sequence segmentation method based on the genetic algorithm (GA) that can improve computational efficiency. The computation is distributed into chromosomes that evolve using distributed genetic algorithms (DGAs). Each chromosome consists of a label and feature vector. The label is used as the region number for the pixel where the chromosome is located. Based on the temporal correlation between two consecutive frames in a video sequence, the segmentation of a frame is successively obtained using the segmentation result of the previous frame. In addition to eliminating redundant computation, only unstable chromosomes corresponding to moving object parts are evolved. Experimental results confirm the effectiveness of the proposed method. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： video sequence segmentation distributed genetic algorithm temporal correlation

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Multi-objective optimization of the design of two-stage flash evaporators: Part 2. Multi-objective optimization

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2010年第12期49卷 2459-2466页

作者： Sebastian, P. Quirante, T. Tiat, V. Ho Kon Ledoux, Y. Univ Bordeaux Lab TREFLE CNRS UMR 8508 F-33405 Talence France

Flash evaporation process is currently developing in the wine industry where it is used for flash-cooling or concentration. The design of flash evaporators is faced with specific constraints and must take into account multiple design objectives. In this paper, the development of a multi-objective optimization method is investigated for the joint optimization of design objectives such as process transportability, environmental efficiency, operative cost or cooling power. The optimization method is based on the aggregation of design objectives through desirability functions and indexes. Desirability functions are suitable for formulating design constraints more precisely than inequality relations and, moreover, the global design model results in an unconstrained optimization problem. However, aggregation methods do make it difficult to compute the global optimum of the design problem. This difficulty has been addressed by developing a distributed genetic algorithm which is not so sensitive to this type of numerical solving difficulty. Another difficulty arises from the weighting method for the aggregation of desirability functions since weight parameters have no physical meaning. This weighting problem is approached through a sensitivity analysis of the weight parameters and by observing their relative influence. (C) 2010 Elsevier Masson SAS. All rights reserved.

关键词： Low-pressure evaporation Flash evaporator Multi-objective optimization Desirability function distributed genetic algorithm

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Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts

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APPLIED INTELLIGENCE 2014年第1期41卷 145-166页

作者： Osaba, E. Diaz, F. Onieva, E. Univ Deusto Deusto Inst Technol DeustoTech Bilbao 48007 Spain

In this paper, a new multiple population based meta-heuristic to solve combinatorial optimization problems is introduced. This meta-heuristic is called Golden Ball (GB), and it is based on soccer concepts. To prove the quality of our technique, we compare its results with the results obtained by two different genetic algorithms (GA), and two distributed genetic algorithms (DGA) applied to two well-known routing problems, the Traveling Salesman Problem (TSP) and the Capacitated Vehicle Routing Problem (CVRP). These outcomes demonstrate that our new meta-heuristic performs better than the other techniques in comparison. We explain the reasons of this improvement.

关键词： Meta-heuristics Golden ball distributed genetic algorithm Routing problems Combinatorial optimization Intelligent transportation systems

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Multi Population genetic algorithm to estimate snow properties from GPR data

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JOURNAL OF APPLIED GEOPHYSICS 2016年 131卷 133-144页

作者： Godio, A. Politecn Torino DIATI I-10129 Turin Italy

Multi-population genetic algorithms (DGA or MGA) are based on the partition of the population into several semi-isolated subpopulations (demes). Each sub-population is associated to an independent GA and explores different promising regions of the search space. We evaluate the sensitivity of some parameters to solve a non-linear problem in georadar data analysis. Particularly, we adapt the DGAs to optimize the model parameters of a data set of variable-offset data, collected in variable offset modality with Ground Penetrating Radar, to estimate porosity, saturation and density of snowpack in a glacial environment. The data set comes from investigation on glaciers to estimate the thickness and density of the seasonal snow. The main strategies to select the best parameters of the optimization process are outlined. We analyze the sensitivity on the solution of the optimization problems of some parameters of DGA;we deal with the effects of population and sub-population, and mutation properties. We consider the reflection traveltimes in a layered medium including a relationship between the traveltimes, porosity and saturation of the snow. We solve the problem for the layer thickness and the porosity, saturation and structural exponent of the snow. Reliable results are obtained in the snow density estimating, while the evaluation of free water content into the snow still remains challenging. (C) 2016 Elsevier B.V. All rights reserved.

关键词： distributed genetic algorithm Sub-population Migration Variable off-set radar data Snow properties

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