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18th EPIA Conference on Artificial Intelligence (EPIA)

作者： Pereira, Pedro Jose Cortez, Paulo Mendes, Rui Univ Minho Dept Informat Syst ALGORITMI Ctr P-4804533 Guimaraes Portugal Univ Minho Ctr Biol Engn ALGORITMI Ctr Dept Informat P-4710057 Braga Portugal

ISBN: (纸本)9783319653402;9783319653396

In this paper, we address multi-step ahead time series Prediction Intervals (PI). We extend two Neural Network (NN) methods, Lower Upper Bound Estimation (LUBE) and multi-objective evolutionary algorithm (MOEA) LUBE (MLUBE), for multi-step PI. Furthermore, we propose two new MOEA methods based on a 2-phase gradient and MOEA based learning: M2LUBET1 and M2LUBET2. Also, we present a robust evaluation procedure to compare PI methods. Using four distinct seasonal time series, we compared all four PI methods. Overall, competitive results were achieved by the 2-phase learning methods, in terms of both predictive performance and computational effort.

关键词： multi-objective evolutionary algorithm multilayer perceptron multi-step ahead prediction Prediction intervals Time series

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Automatic Code Generation for Microcontroller-Based System Using multi-objective Linear Genetic Programming

Automatic Code Generation for Microcontroller-Based System U...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Ferrel Serruto, Wildor Alfaro Casas, Luis Univ Nacl San Agustin Arequipa Dept Acad Ingn Elect Arequipa Peru

ISBN: (纸本)9781538626528

Microcontroller-based systems often include peripheral devices such as matrix keyboard and character LCD module among others. We propose the application of the multi-objective linear genetic programming, for automatic generation of the assembly driver routines for these devices, to perform the operations: matrix keyboard scan, LCD module initialization and character display on LCD. For fitness evaluation, we assign a function to be maximized to each bit of the binary result or to the timing diagram of each used microcontroller Port pins. This decomposition of the problem used in a multi-objective evolutionary algorithm allows generating programs, in some cases, with smaller code size or shorter execution time than programs written by a human programmer.

关键词： multi-objective evolutionary algorithm Linear genetic programming Automatic code generation Microcontroller-based system

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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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multi-objective Maximum Diversity Problem 43

Multi-Objective Maximum Diversity Problem

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43rd Latin American Computer Conference (CLEI)

作者： Vera, Katherine Lopez-Pires, Fabio Baran, Benjamin Sandoya, Fernando Natl Univ Asuncion San Lorenzo Paraguay Itaipu Technol Pk Hernandarias Paraguay ESPOL Polytech Univ Dept Math Guayaquil Ecuador

ISBN: (纸本)9781538630570

The Maximum Diversity (MD) problem is the process of selecting a subset of elements where the diversity among selected elements is maximized. Several diversity measures were already studied in the literature, optimizing the problem considered in a pure mono-objective approach. This work presents for the first time multi-objective approaches for the MD problem, considering the simultaneous optimization of the following five diversity measures: (i) Max-Sum, (ii) Max-Min, (iii) Max-MinSum, (iv) Min-Diff and (v) Min-P-center. Two different optimization models are proposed: (i) multi-objective Maximum Diversity (MMD) model, where the number of elements to be selected is defined a-priori, and (ii) multi-objective Maximum Average Diversity (MMAD) model, where the number of elements to be selected is also a decision variable. To solve the formulated problems, a multi-objective evolutionary algorithm (MOEA) is presented. Experimental results demonstrate that the proposed MOEA found good quality solutions, i.e. between 89.20% and 99.92% of the optimal Pareto front when considering the hyper-volume for comparison purposes.

关键词： multi-objective Maximum Diversity multi-objective Maximum Average Diversity multi-objective evolutionary algorithm

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multi-objective evolutionary Optimization and Monte Carlo Simulation for Placement of Low Impact Development in the Catchment Scale

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2017年第9期143卷

作者： Giacomoni, M. H. Joseph, John Univ Texas San Antonio Dept Civil & Environm Engn 1 UTSA Circle San Antonio TX 78249 USA

Restoring the hydrologic flow regime of urban areas by promoting infiltration, retention, and evapotranspiration on the site is one of the goals of low-impact development (LID). These goals can be achieved through the implementation of stormwater control measures (SCMs) such as green roofs and permeable pavements. The effectiveness of SCMs can be influenced not only by their design but also by their location. The present study applies multi-objective evolutionary optimization and Monte Carlo simulation approaches to help identify nearoptimal locations of green roofs and permeable pavements in the catchment scale. The Nondominated Sorting Genetic algorithm II was connected to the stormwater management model (SWMM) to identify the location of SCMs and characterize the tradeoffs between flow regime alteration and implementation costs. The impact of implementing SCMs is measured by peak flow, runoff volume, and the hydrologic footprint residence (HFR). The HFR is a new stormwater metric that represents dynamics of inundated areas and residence time of flood waves throughout downstream segments. The approach was tested in an illustrative case study of an 11.7-ha urban catchment divided into five subcatchments. The results indicate that locating SCMs in downstream subcatchments can reduce peak flow more effectively, whereas SCMs placed in upstream subcatchments better reduce the HFR. The proposed methodology can help stormwater managers to better assess the combined performance of LID-SCMs in different hydrologic scales and generate guidelines for prioritizing the implementation or retrofitting of urban areas with green infrastructure. (C) 2017 American Society of Civil Engineers.

关键词： Low-impact development Green roof Permeable pavement Hydrologic footprint residence multi-objective evolutionary algorithm Monte Carlo simulation

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multi-objective Evolution of Machine Learning Workflows

Multi-Objective Evolution of Machine Learning Workflows

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

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

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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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multi-objective Optimization of Hybrid Renewable Energy System with Load Forecasting 1

Multi-objective Optimization of Hybrid Renewable Energy Syst...

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1st IEEE International Conference on Energy Internet (ICEI)

作者： Ming, Mengjun Wang, Rui Zha, Yabing Zhang, Tao Natl Univ Def Technol Coll Informat Syst & Management Changsha Hunan Peoples R China

ISBN: (纸本)9781509057597

Load forecasting is crucial to schedule energy in advance with meeting the load demand of users. This study proposes a hybrid model in which the multiple linear regression (MLR) and the autoregressive integrated moving average (ARI-MA) method are used to perform load forecasting. The proposed method enhances the forecasting accuracy by emphasizing more on the previous forecasting results that have smaller deviations. In addition, Kalman filter is used to estimate parameters of the hybrid model. By taking the forecasted load as input, the paper further considers multi-objective optimal design of a hybrid renewable energy system (HRES). The HRES consists of photovoltaic (PV) panels, wind turbines, batteries and diesel generators. The design is to minimize the HRES cost, pollutant emission, as well as to satisfy the load demand as much as possible. The problem is solved by a multi-objective evolutionary algorithm (MOEA) by which a set of approximated Pareto optimal (trade-off) solutions are found. By further integrating the preference of a decision maker, the best configuration of the HRES is identified.

关键词： load forecasting the multiple linear regression ARIMA hybrid renewable energy system multi-objective evolutionary algorithm

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Using evolutionary algorithms to determine the residual stress profile across welds of age-hardenable aluminum alloys

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APPLIED SOFT COMPUTING 2016年 40卷 429-438页

作者： Ignacio Hidalgo, J. Fernandez, Ricardo Manuel Colmenar, J. Cioffi, Florencia Risco-Martin, Jose L. Gonzalez-Doncel, Gaspar Univ Complutense Madrid Adapt & Bioinspired Syst Grp Calle Prof Jose Garcia Santesmases 9 E-28040 Madrid Spain CSIC CENIM Ctr Nacl Invest Met Dept Met Phys Av Gregorio del Amo 8 Madrid 28040 Spain Univ Rey Juan Carlos Calle Tulipan Mostoles 28933 Spain

This paper presents an evolutionary based method to obtain the un-stressed lattice spacing, do, required to calculate the residual stress profile across a weld of an age-hardenable aluminum alloy, AA2024. Due to the age-hardening nature of this alloy, the do value depends on the heat treatment. In the case of welds, the heat treatment imposed by the welding operation differs significantly depending on the distance to the center of the joint. This implies that a variation of do across the weld is expected, a circumstances which limits the possibilities of conventional analytical methods to determine the required do profile. The interest of the paper is, therefore, two-fold: First, to demonstrate that the application of an evolutionary algorithm solves a problem not addressed in the literature such as the determination of the required data to calculate the residual stress state across a weld. Second, to show the robustness of the approximation used, which allows obtaining solutions for different constraints of the problem. Our results confirm the capacity of evolutionary computation to reach realistic solutions under three different scenarios of the initial conditions and the available experimental data. (C) 2015 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm Real world application Residual stress determination

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A diagnostic assessment of evolutionary algorithms for multi-objective surface water reservoir control

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ADVANCES IN WATER RESOURCES 2016年 92卷 172-185页

作者： Salazar, Jazmin Zatarain Reed, Patrick M. Herman, Jonathan D. Giuliani, Matteo Castelletti, Andrea Cornell Univ Sch Civil & Environm Engn Ithaca NY 14853 USA Univ Calif Davis Dept Civil & Environm Engn Davis CA 95616 USA Politecn Milan Dept Elect Informat & Bioengn I-20133 Milan Italy

Globally, the pressures of expanding populations, climate change, and increased energy demands are motivating significant investments in re-operationalizing existing reservoirs or designing operating policies for new ones. These challenges require an understanding of the tradeoffs that emerge across the complex suite of multi-sector demands in river basin systems. This study benchmarks our current capabilities to use evolutionary multi-objective Direct Policy Search (EMODPS), a decision analytic framework in which reservoirs' candidate operating policies are represented using parameterized global approximators (e. g., radial basis functions) then those parameterized functions are optimized using multi-objective evolutionary algorithms to discover the Pareto approximate operating policies. We contribute a comprehensive diagnostic assessment of modern MOEAs' abilities to support EMODPS using the Conowingo reservoir in the Lower Susquehanna River Basin, Pennsylvania, USA. Our diagnostic results highlight that EMODPS can be very challenging for some modern MOEAs and that epsilon dominance, time-continuation, and auto-adaptive search are helpful for attaining high levels of performance. The epsilon-MOEA, the auto-adaptive Borg MOEA, and epsilon-NSGAII all yielded superior results for the six-objective Lower Susquehanna bench-marking test case. The top algorithms show low sensitivity to different MOEA parameterization choices and high algorithmic reliability in attaining consistent results for different random MOEA trials. Overall, EMODPS poses a promising method for discovering key reservoir management tradeoffs;however algorithmic choice remains a key concern for problems of increasing complexity. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： multi-purpose reservoir control Direct policy search multi-objective evolutionary algorithm Benchmark

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