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检索条件"主题词=Multi-Objective Evolutionary Algorithm"

共 545 条记录，以下是451-460 订阅

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multi-objective Evolution of Machine Learning Workflows

Multi-Objective Evolution of Machine Learning Workflows

引用

IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Kren, Tomas Pilat, Martin Neruda, Roman Charles Univ Prague Fac Math & Phys Prague Czech Republic Czech Acad Sci Inst Comp Sci Prague Czech Republic

ISBN: (纸本)9781538627266

In this paper we describe a multi-objective genetic programming algorithm which can be used to create complete machine learning workflows. The algorithm is an extension of a single-objective one. In a series of test on four datasets, we show that the additional objectives can be used to search for smaller or faster models. The algorithm is also in some cases much faster than the single-objective one while obtaining results of similar quality.

关键词： genetic programming multi-objective evolutionary algorithm machine learning

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Cost-effective Network Planning for Rural Communities 2

Cost-effective Network Planning for Rural Communities

引用

International Conference on Computing, Electronics and Communications Engineering (iCCECE)

作者： Islam, Nazrul Phillips, Chris Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

ISBN: (纸本)9781728121383

Broadband Internet access is central to the regeneration of remote communities and reducing the digital divide between rural and urban regions. This paper focuses on rural communities with limited financial resources, environmental issues including long reach from conurbations, and mountainous or otherwise adverse terrain, typically with limited access to a wired power supply. As such, regular access technologies based on cable or fibre optics are not financially viable. To overcome this challenge, we consider the deployment of a Free-Space Optical (FSO) based relay network as primary technology, using diversity to provide resilience to atmospheric effects. More precisely, this paper describes a novel network planning tool based on a multi-objective evolutionary algorithm (MOEA), that determines the suitable location of FSO relay nodes, taking into account end-to-end link speed and the degree of path diversity. This MOEA approach can account for Line-of-Sight occlusions and allows various compromises to be selected from a Pareto front to suit individual needs. We provide suitable results to show the satisfactory operation of the tool and outline avenues for future development.

关键词： Free-Space Optical Network Planning multi-objective evolutionary algorithm

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multi-objective Population-Based Incremental Learning for Community Structure Detection

Multi-objective Population-Based Incremental Learning for Co...

引用

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

作者： Ma, Wenping Wu, Yue Yun, Jie Xidian Univ Int Res Ctr Intelligent Percept & ComputatMinist Joint Int Res Lab Intelligent Percept & Computat Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Provinc Peoples R China

ISBN: (纸本)9781509006229

The community structure detection of complex networks has become a hot topic in the past several years. In this paper, a new discrete framework of population-based incremental learning for complex networks problem is proposed. Based on the proposed discrete framework, a novel multi-objective population-based incremental learning algorithm is proposed to solve community structure detection problem. The proposed algorithm combines population-based incremental learning with the multi-objective evolutionary algorithm based on decomposition, this makes the evolution get directionality and converge fast. In order to discourage premature convergence, a random perturbation operator is adopted. The proposed algorithm has two contradictory objective functions termed as negative ratio association and ratio cut, respectively. The community structure detection results are a set of tradeoff solutions by simultaneous optimizing these two contradictory objective functions. Each of these solutions corresponds to a network community structure at one hierarchical level. Experiments on both real-world and synthetic networks prove the effectiveness of the proposed algorithm.

关键词： community structure detection multi-objective evolutionary algorithm population based incremental learning random perturbation operator

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Carbon-Aware Mine Planning with a Novel multi-objective Framework 14th

Carbon-Aware Mine Planning with a Novel Multi-objective Fram...

引用

14th International Conferences on Computational Logistics (ICCL)

作者： Azhar, Nurul Asyikeen Binte Gunawan, Aldy Cheng, Shih-Fen Leonardi, Erwin Singapore Management Univ Sch Comp & Informat Syst 80 Stamford Rd Singapore 178902 Singapore Rio Tinto Ltd Tower 312 Marina BlvdMarina Bay Financial Ctr Singapore 018982 Singapore

ISBN: (纸本)9783031436116;9783031436123

The logistical complication of long-term mine planning involves deciding the sequential extraction of materials from the mine pit and their subsequent processing steps based on geological, geometrical, and resource constraints. The net present value (NPV) of profit over the mine's lifespan usually forms the sole objective for this problem, which is considered as the NP-hard precedence-constrained production scheduling problem (PCPSP) as well. However, increased pressure for more sustainable and carbon-aware industries also calls for environmental indicators to be considered. In this paper, we enhance the generic PCPSP formulation into a multi-objective optimization (MOO) problem whereby carbon cost forms an additional objective. We apply the Non-Dominated Sorting Genetic algorithm II (NSGA-II) to this formulation and experiment with variants to the solution generation. Our tailored application of the NSGA-II using a set of real-world inspired datasets can form an approximated Pareto front for planners to observe stipulated annual carbon emission targets. It also displays that tailored variants of the NSGA-II can produce diverse solutions that are close to the true Pareto front.

关键词： Precedence-constraint production scheduling Resource capacity optimization multi-objective evolutionary algorithm Sustainability

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Internet Traffic Classification using MOEA and Online Refinement in Voting on Ensemble Methods

Internet Traffic Classification using MOEA and Online Refine...

引用

21st Iranian Conference on Electrical Engineering (ICEE)

作者： Aliakbarian, Mohammad Sadegh Fanian, Ali Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran

ISBN: (纸本)9781467356343

Internet Traffic Classification is one of the most important issues in network management. Until now, many methods have been proposed to this end, but studies show that machine learning based algorithms have a good performance in comparison to other methods. Selecting the best feature subset causes better accuracy and efficiency in machine learning algorithms. In this paper, in order to obtain higher classification accuracy, effective features are selected using a multi-objective evolutionary algorithm. In this evolutionary algorithm, some objectives such as minimizing feature number, maximizing classification accuracy, maximizing true positive rate (maximizing TPR), and minimizing false positive rate (minimizing FPR) are satisfied simultaneously and with no conflicts with each other. In the proposed method, selected features subset is given to a new ensemble algorithm with online refinement during training and testing phases. Final result of each new ensemble algorithm is obtained by the vote of the majority with respect to the accuracy of any voter. Results show the high efficiency and performance of proposed method in comparison with other methods. So that the WWW traffic classification accuracy ascend to 99.93%. The results for 8 other traffics such as P2P indicate high accuracy of the proposed method.

关键词： Traffic Classification multi-objective evolutionary algorithm Ensemble method

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multi-objective and multi-Criteria Optimization of Microgrids for Nearly Zero-Energy Buildings 2

Multi-Objective and Multi-Criteria Optimization of Microgrid...

引用

2nd International Conference on Smart Energy Systems and Technologies (SEST)

作者： Galisai, Simone Ghiani, Emilio Pilo, Fabrizio Univ Cagliari Dept Elect & Elect Engn Cagliari Italy

ISBN: (纸本)9781728111568

Nearly zero-energy buildings (NZEBs) are high energy performance buildings in which part of the amount of energy that these buildings require comes mostly from renewable sources. In order to obtain the target of nearly zero energy consumption, the electrical loads and the energy sources related to the NZEB building can be organized as a Microgrid, which needs to be optimally sized in his components. The optimal sizing of a Microgrid for NZEBs can be formulated as multi-objective problem. In fact, for the NZEB owner exists an economic target aimed at maximizing profits from micro generation, a different one aimed solely to minimize the energy bought from the market and an environmental target aimed at minimizing the global CO2 emissions. These objectives can enter into conflict and create the need for combined optimization. In this paper, this optimization problem is investigated with an integrated framework addressing the multi-objective optimization and multi-criteria evaluation issues simultaneously Minimize the investment cost, maximize the fraction of energy self-consumed with renewable energy sources and reduce the CO2 emissions will be considered as three objectives for multi-objective optimization. The proposed methodology is applied to a microgrid for a NZEB public building. The simulation results show the effectiveness of the proposed methodology.

关键词： Microgrid NZEBs Renewable sources multi-objective evolutionary algorithm Real-time pricing

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A computational intelligence approach to system-of-systems architecting incorporating multi-objective optimization 1

A computational intelligence approach to system-of-systems a...

引用

Conference on Systems Engineering Research

作者： Curry, David M. Dagli, Cihan H. Missouri Univ Sci & Technol Dept Engn Management & Syst Engn Rolla MO 65409 USA

A computational intelligence approach to system-of-systems architecting is developed using multi-objective optimization. Such an approach yields a set of optimal solutions (the Pareto set) which has both advantages and disadvantages. The primary benefit is that a set of solutions provides a picture of the optimal solution space that a single solution cannot. The primary difficulty is making use of a potentially infinite set of solutions. Therefore, a significant part of this approach is the development of a method to model the solution set with a finite number of points allowing the architect to intelligently choose a subset of optimal solutions based on criteria outside of the given objectives. The approach developed incorporates a meta-architecture, multi-objective genetic algorithm, and a corner search to identify points useful for modeling the solution space. This approach is then applied to a network centric warfare problem seeking the optimum selection of twenty systems. Finally, using the same problem, it is compared to a hybrid approach using single-objective optimization with a fuzzy logic assessor to demonstrate the advantage of multi-objective optimization. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： System-of-Systems SoS Architecting Genetic algorithm GA multi-objective Optimization Problem MOP multi-objective evolutionary algorithm MOEA Meta-Architecture

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Combine LHS with MOEA to Optimize Complex Pareto Set MOPs

引用

3rd International Conference on Intelligence Computation and Applications

作者： Zheng, Jinhua Luo, Biao Li, Miqing Li, Jing Xiangtan Univ Res Ctr Evolutionary Computat & Intelligent Syst Xiangtan 411105 Peoples R China

ISBN: (纸本)9783540921363

The Pareto set (PS) of real multi-objective optimization problems (MOPs) are often unknown and complex, so, it is significant for multi-objective evolutionary algorithms (MOEAs) to solve complex PS MOPs (CPS_MOPs namely). In this paper, we combined Latin hypercube sampling (LHS) with MOEA, proposed a LHS based MOEA (LHS-MOEA). We Suggested two kinds of LHS-MOEA, in which LHS local search and evolutionary operator are combined to handle CPS_MOPs. Through sonic experiments, the results demonstrate that LHS-MOEA performs Much better than the traditional prevalent MOEA - NSGA-II in solvin CPS_MOPs.

关键词： multi-objective evolutionary algorithm Latin Hypercube Sampling Complex Pareto Set Local Search evolutionary Operator

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A multi-objective framework for location recommendation based on user preference 13

A multi-objective framework for location recommendation base...

引用

13th International Conference on Computational Intelligence and Security (CIS)

作者： Wang, Shanfeng Gong, Maoguo Qin, Can Yang, Junwei Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9781538648223

Location recommendation has attracted increasing attention in recent years. This paper proposes a novel multi-objective framework for location recommendation based on user preference. Under this framework, user preference can be separated into common preference and individual preference. Then two contradictory objective functions are designed to describe these two kinds of preferences. It is difficult to optimize these two objective functions simultaneously. In this paper, a novel multi-objective evolutionary algorithm is proposed to optimize these two objective functions. The proposed algorithm can make a good balance between these two objective functions. Experiments on two real application recommendation scenarios: Foursquare dataset and Gowalla dataset show that the proposed algorithm is effective to recommend locations.

关键词： Location recommendation multi-objective evolutionary algorithm User preference

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Study on multi-objective Optimization of Oil Production Process 11

Study on Multi-objective Optimization of Oil Production Proc...

引用

11th World Congress on Intelligent Control and Automation

作者： Liu, Tan Gao, Xianwen Wang, Lina Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781479958252

By analyzing the mechanism of oil production process and the relationship between each subsystem, a multi-objective optimization model is proposed, which is with maximizing overall oil production, minimizing overall water production and comprehensive energy consumption for per ton oil as the goals, and then a multi-objective evolutionary algorithm NSGA-II is used for solving the multi-objective optimization model in this paper. Finally, the actual production process in a block of an oil production operation area is taken as background for simulation study, the results showed that the effectiveness and rationality of the model and it laid the foundation for the energy saving of oil production process.

关键词： oil production process multi-objective optimization multi-objective evolutionary algorithm NSGA-II

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