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检索条件"主题词=multi-objective evolutionary algorithm"

共 545 条记录，以下是411-420 订阅

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Design of Next Generation Smart Surface Transportation System 3

Design of Next Generation Smart Surface Transportation Syste...

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6th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS) / 3rd International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS)

作者： Chang, Francis Garg, Hari K. Natl Univ Singapore Dept Elect & Comp Engn 4 Engn Dr 3 Singapore 117576 Singapore

ISBN: (纸本)9789897582424

Transportation systems of the future need to be adaptive, adoptive, and responsive in order to meet the diverse challenges and ever-evolving demands. Conventional method of adding more resources on the road does not enhance its utility, rather it creates traffic congestion. Optimization of the usage of existing resources has been found to be one of the most effective solution to manage traffic congestion. The method we propose consists in increasing the occupancy rate of each vehicle and utilize other untapped resources in existing infrastructure. The resource optimization problem studied in this paper is NP hard, due to the vehicle routing and resource matching problem. In this paper, we are focused on developing a multi-objective evolutionary algorithm to optimize the use of taxi service not just as a carrier for people but also as a transport system for parcel delivery. Preliminary experiment with real-world data shows that our approach is able to quickly produce satisfactory solutions and the algorithm is able to provide an average of 17.7% improvement over conventional methods.

关键词： multi-objective evolutionary algorithm Genetic algorithm Intelligent Transport System

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DMOPSO: Dual multi-objective Particle Swarm Optimization

DMOPSO: Dual Multi-Objective Particle Swarm Optimization

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IEEE Congress on evolutionary Computation (CEC)

作者： Lee, Ki-Baek Kim, Jong-Hwan Korea Adv Inst Sci & Technol Dept Elect Engn Daejeon 305701 South Korea

ISBN: (纸本)9781479914883

Since multi-objective optimization algorithms (MOEAs) have to find exponentially increasing number of nondominated solutions with the increasing number of objectives, it is necessary to discriminate more meaningful ones from the other nondominated solutions by additionally incorporating user preference into the algorithms. This paper proposes dual multi-objective particle swarm optimization (DMOSPO) by introducing secondary objectives of maximizing both user preference and diversity to the nondominated solutions obtained for primary objectives. The proposed DMOSPO can induce the balanced exploration of the particles in terms of user preference and diversity through the dual-stage of nondominated sorting such that it can generate preferable and diverse nondominated solutions. To demonstrate the effectiveness of the proposed DMOPSO, empirical comparisons with other state-of-the-art algorithms are carried out for benchmark functions. Experimental results show that DMOPSO is competitive with the other compared algorithms and properly reflects the user's preference in the optimization process while maintaining the diversity and solution quality.

关键词： multi-objective evolutionary algorithm multi-objective Particle Swarm Optimization Dual-stage dominance check Crowding distance User preference

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Combining Constraint Solving with Different MOEAs for Configuring Large Software Product Lines: A Case Study 42

Combining Constraint Solving with Different MOEAs for Config...

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42nd Annual IEEE-Computer-Society Computers, Software and Applications (COMPSAC) Conference - Staying Smarter in a Smartening World

作者： Yu, Huiqun Shi, Kai Guo, Jianmei Fan, GuiSheng Yang, Xingguang Chen, Liqiong East China Univ Sci & Technol Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Key Lab Comp Software Evaluating & Testi Shanghai Peoples R China Alibaba Grp Hangzhou Peoples R China Shanghai Inst Technol Dept Comp Sci & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9781538626672

multi-objective evolutionary algorithm (MOEA) with the constraint solving has been successfully applied to address the configuration optimization problem in software product line (SPL), for example, the state-of-the-art SATIBEA algorithm. However, each different MOEA with special search operator demonstrates the different strength and weakness in terms of optimality and convergence speed. The SATIBEA just combines the SAT (Boolean satisfiability problem) constraint solving with the Indicator-Based evolutionary algorithm (IBEA) for evaluating the algorithm performance. In this paper, we propose six hybrid algorithms which combine the SAT solving with different MOEAs. Case study is based on five large-scale, rich-constrained and real-world SPLs. Empirical results demonstrate that SATMOCell algorithm obtains a competitive optimization performance to the state-of-the-art that outperforms the SATIBEA in terms of quality Hypervolume metric for 2 out of 5 SPLs within the same time budget. Moreover, the convergence speed of SATMOCell and SATssNSGA2 is comparable after 10min terminal times. Particularly, the Hypervolume value of SATssNSGA2 reports the average improvement of 1.33% after 20min terminal times.

关键词： Constraint solving Search-based software engineering Software product lines multi-objective evolutionary algorithm

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An interactive method for multi-satellite imaging mission planning in complex environments 36

An interactive method for multi-satellite imaging mission pl...

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36th Chinese Control and Decision Conference (CCDC)

作者： Yang, Xueying Hu, Min Huang, Gang Space Engn Univ Dept Aerosp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Existing multi-satellite imaging mission planning methods mostly apply to scenarios with clear problem boundaries, explicit decision rules, and limited problem sizes. It is difficult to deal with the problem that the real environment is more complex and the action effect is more uncertain. This paper proposes a Human-machine interactive mechanism based on knowledge transfer strategy multi-objective evolutionary algorithm (HMIM-KTSMOEA) for multi-satellite Imaging Mission Planning in Complex Environments (MSIMPCE). Firstly, a Human-machine interactive mechanism is proposed to deal with the dynamic changes of the environment effectively. Secondly, a multi-objective evolutionary algorithm based on a knowledge transfer strategy is proposed to improve the efficiency of MSIMPCE, which has complex constraints and large solving space. Simulation results show that HMIM-KTSMOEA has obvious advantages in solving the MSIMPCE problem in terms of task execution efficiency and response time.

关键词： multi-satellite imaging mission planning active method multi-objective evolutionary algorithm

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Parallel Differential Evolution approach for Cloud workflow placements under simultaneous optimization of multiple objectives

Parallel Differential Evolution approach for Cloud workflow ...

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IEEE Congress on evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Balouek-Thomert, Daniel Bhattacharya, Arya K. Caron, Eddy Gadireddy, Karunakar Lefevre, Laurent NewGenerat SR Paris France Univ Lyon UCH Lyon 5668 INRIA ENS LyonUMR CNRSLIP LabINRIA Avalon Team Lyon France Mahindra Ecole Cent Sch Engn Hyderabad Andhra Pradesh India

ISBN: (纸本)9781509006229

The recent rapid expansion of Cloud computing facilities triggers an attendant challenge to facility providers and users for methods for optimal placement of workflows on distributed resources, under the often-contradictory impulses of minimizing makespan, energy consumption, and other metrics. evolutionary Optimization techniques that from theoretical principles are guaranteed to provide globally optimum solutions, are among the most powerful tools to achieve such optimal placements. multi-objective evolutionary algorithms by design work upon contradictory objectives, gradually evolving across generations towards a converged Pareto front representing optimal decision variables - in this case the mapping of tasks to resources on clusters. However the computation time taken by such algorithms for convergence makes them prohibitive for real time placements because of the adverse impact on makespan. This work describes parallelization, on the same cluster, of a multi-objective Differential Evolution method (NSDE-II) for optimization of workflow placement, and the attendant speedups that bring the implicit accuracy of the method into the realm of practical utility. Experimental validation is performed on a real-life testbed using diverse Cloud traces. The solutions under different scheduling policies demonstrate significant reduction in energy consumption with some improvement in makespan.

关键词： Parallel computing workflow placement cloud computing energy makespan middleware Differential Evolution multi-objective evolutionary algorithm Pareto front convergence

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Building Efficient Frontier by CVaR minimization for non-normal asset returns using Copula Theory

Building Efficient Frontier by CVaR minimization for non-nor...

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11th IEEE International Conference on Computational Science and Engineering

作者： Agrawal, Kapil KanGAL Indian Inst Technol Kanpur Kanpur Genet Algorithms Lab Dept Mech Engn Kanpur Uttar Pradesh India

ISBN: (纸本)9780769531939

In the realm of Computational Finance, the performance of the optimal portfolio largely depends upon its composition and its ability to accurately predict the market movements. Recent empirical studies have shown that the underlying assumption of normality of asset returns for risk modeling is seriously flawed, in view of their asymmetric and fat-tailed behavior. This problem is further aggravated when we delve into the functioning of the Financial market and realize that the market parameters have highly nonlinear kind of inter-dependence amongst themselves. Any investment portfolio that does not account for these factors and their mutual relationship, will tend to under-perform. This work is a novel attempt, which aims at developing a framework which solves all of these problems in an integrated fashion, without overlooking any of them or preassigning lesser importance to any of these issues. The contemporary techniques often neglect one of them, resulting in an incomplete and sometimes even a misleading picture of the market scenario. In this work, copula theory effectively captures the non-linear inter-dependence. The scenarios are generated from a non-elliptical multivariate distribution constructed by a Students t-copula assuming marginal distributions as Gaussian in the center and EVT distributed in the tail. For gauging the market risk we have used CVaR (Conditional Value-at-Risk) as the risk measure. The efficient frontier thus resulted by minimizing the CVaR and maximizing the returns, gives a clear insight into how does the composition of the optimal portfolio changes with respect to change in CVaR of the portfolio. Our aim is to prove that much more reliable conclusions will certainly be drawn if a more realistic representation of data can be done using the concept of Copulas.

关键词： Portfolio Optimization multi-objective evolutionary algorithm conditional expectation Copula multivariate distribution Value-at-Risk Conditional Value-at-Risk extreme value theory NSGA-II

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multicast Session Protection Planner Tool to plan and deploy protection infrastructure: a SPEA approach

Multicast Session Protection Planner <i>Tool to plan and dep...

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15th International Conference of the Catalan-Association-of-Artificial-Intelligence (CCIA)

作者： Maldonado-Lopez, Ferney A. Donoso, Yezid Univ Los Andes Bogota Colombia

ISBN: (纸本)9781614991397;9781614991380

Several multimedia networking services are based on multicast sessions. Due to high amount of traffic demanded by multicast applications, they need a system or mechanism to protect them from network failures and to keep the service when incidents are present. We propose the multicast Session Protection Planner (MSPP) as a tool to protect multicast services which uses an elitist multi-objective technique to determine a min-cost multicast tree. MSPP pursues minimizing the cost associated to establish redundant trees on a network with diverse decision variables, maximizing the network availability to satisfy a demand, and considering multiple failures. MSPP is tested over a generated initial topology which includes population demand over a core network. The simulation results shows that the proposed algorithm satisfies recovery resilience requirements of fault-tolerance varying topology. Results demonstrate that MSPP is an useful tool which finds new low-cost high-availability redundant trees, even if the number of failures increases.

关键词： multi-objective evolutionary algorithm protection network survivability networks Resilience

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An NSGA-III-Based multi-objective Intelligent Autoscaler for Executing Engineering Applications in Cloud Infrastructures 19th

An NSGA-III-Based Multi-objective Intelligent Autoscaler for...

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19th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Yannibelli, Virginia Pacini, Elina Monge, David Mateos, Cristian Rodriguez, Guillermo ISISTAN UNICEN CONICET Tandil Buenos Aires Argentina UNCUYO ITIC Mendoza Argentina Consejo Nacl Invest Cient & Tecn Mendoza Argentina UNCuyo Fac Ingn Mendoza Argentina

ISBN: (纸本)9783030608842;9783030608835

Parameter Sweep Experiments (PSEs) are commonplace to perform computer modelling and simulation at large in the context of industrial, engineering and scientific applications. PSEs require numerous computational resources since they involve the execution of many CPU-intensive tasks. Distributed computing environments such as Clouds might help to fulfill these demands, and consequently the need of Cloud autoscaling strategies for the efficient management of PSEs arise. The multi-objective Intelligent Autoscaler (MIA) is proposed to address this problem, which is based on the Non-dominated Sorting Genetic algorithm III (NSGA-III), while aiming to minimize makespan and cost. MIA is assessed utilizing the CloudSim simulator with three study cases coming from real-world PSEs and current characteristics of Amazon EC2. Experiments show that MIA significantly outperforms the only PSE autoscaler (MOEA autoscaler) previously reported in the literature, to solve different instances of the problem.

关键词： Parameter Sweep Experiments Cloud autoscaling multi-objective evolutionary algorithm CloudSim Amazon EC2

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A fuzzy multi-objective portfolio selection model with transaction cost and liquidity 11

A fuzzy multi-objective portfolio selection model with trans...

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11th International Conference on Computational Intelligence and Security (CIS)

作者： Yue, Wei Wang, Yuping Xidian Univ Sch Comp Sci & Technol Xian 710071 Shannxi Peoples R China

ISBN: (纸本)9781467386609

In this paper, a new proportion entropy function is proposed as an objective function to obtain a well-diversified portfolio. Secondly, a new fuzzy multi-objective portfolio selection model based on the proposed entropy function is presented. By using the model, we can find tradeoffs between risk, return and the diversification degree of portfolio. Thirdly, a new multi-objective evolutionary algorithm is designed to solve the proposed model. Finally, some numerical examples are presented to illustrate the effectiveness of the proposed model and the corresponding algorithm.

关键词： Portfolio selection Possibilistic moments Entropy multi-objective evolutionary algorithm

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Facial recognition using composite correlation filters designed with multiobjective combinatorial optimization 37

Facial recognition using composite correlation filters desig...

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Conference on Applications of Digital Image Processing XXXVII

作者： Cuevas, Andres Diaz-Ramirez, Victor H. Kober, Vitaly Trujillo, Leonardo Inst Politecn Nacl CITEDI Ave Parque 1310 Tijuana 22510 BC Mexico Chelyabinsk State Univ Dept Math Moscow 22860 Russia Inst Tecnol Tijuana Tijuana 22414 Mexico Inst Politecn Nacl CITEDI Tijuana 22510 Mexico

ISBN: (纸本)9781628412444

Facial recognition is a difficult task due to variations in pose and facial expressions, as well as presence of noise and clutter in captured face images. In this work, we address facial recognition by means of composite correlation filters designed with multi-objective combinatorial optimization. Given a large set of available face images having variations in pose, gesticulations, and global illumination, a proposed algorithm synthesizes composite correlation filters by optimization of several performance criteria. The resultant filters are able to reliably detect and correctly classify face images of different subjects even when they are corrupted with additive noise and nonhomogeneous illumination. Computer simulation results obtained with the proposed approach are presented and discussed in terms of efficiency in face detection and reliability of facial classification. These results are also compared with those obtained with existing composite filters.

关键词： Facial recognition composite correlation filters multi-objective evolutionary algorithm combinatorial optimization

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