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A numerical framework for the multi-objective optimal design of check dam systems in erosion-prone areas

ENVIRONMENTAL MODELLING & SOFTWARE 2019年 119卷 21-31页

作者： Pal, Debasish Galelli, Stefano Singapore Univ Technol & Design Pillar Engn Syst & Design Singapore 487372 Singapore

Check dams are one of the most popular measures for soil erosion control. Despite the availability of various process-based modelling tools, their design is often carried out in an uncoordinated manner, potentially leading to check dam systems with limited life expectancy or unreliable sediment retention capacity. In this study, we consider the problem of determining the optimal location and size (i.e., initial storage capacity) of a given number of check dams, and tackle this problem by contributing a numerical framework that builds on geo-processing, two process-based models (WaTEM/SEDEM and StoDyM), and multi-objective evolutionary computation. The application of our framework to two catchments in the Chinese Loess Plateau, characterized by similar erosion processes but different extensions (4.26 and 13.97 km(2)), reveals a strong trade-off between three criteria of system's performance, namely life expectancy, sediment retention capacity, and storage dynamics. Results also show that there are opportunities for improving the performance of existing check dam systems through a coordinated, optimization-based, planning exercise.

关键词： Soil erosion Check dams Optimization multi-objective evolutionary algorithms WaTEM/SEDEM Loess plateau

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SMTIBEA: a hybrid multi-objective optimization algorithm for configuring large constrained software product lines

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SOFTWARE AND SYSTEMS MODELING 2019年第2期18卷 1447-1466页

作者： Guo, Jianmei Liang, Jia Hui Shi, Kai Yang, Dingyu Zhang, Jingsong Czarnecki, Krzysztof Ganesh, Vijay Yu, Huiqun East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai Peoples R China Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada Shanghai Dianji Univ Sch Elect Informat Shanghai Peoples R China Chinese Acad Sci Inst Biochem & Cell Biol Inst Biol Sci Shanghai Peoples R China

A key challenge to software product line engineering is to explore a huge space of various products and to find optimal or near-optimal solutions that satisfy all predefined constraints and balance multiple often competing objectives. To address this challenge, we propose a hybrid multi-objective optimization algorithm called SMTIBEA that combines the indicator-based evolutionary algorithm (IBEA) with the satisfiability modulo theories (SMT) solving. We evaluated the proposed algorithm on five large, constrained, real-world SPLs. Compared to the state-of-the-art, our approach significantly extends the expressiveness of constraints and simultaneously achieves a comparable performance. Furthermore, we investigate the performance influence of the SMT solving on two evolutionary operators of the IBEA.

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

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Compact modeling of the effects of illumination on the contact region of organic phototransistors

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ORGANIC ELECTRONICS 2019年 70卷 113-121页

作者： Romero, A. Jimenez, C. Gonzalez, J. Lopez-Varo, P. Deen, M. J. Jimenez-Tejada, J. A. Univ Granada CITIC UGR Dept Arquitectura & Tecnol Computadores E-18071 Granada Spain Univ Granada Dept Electron & Tecnol Computadores E-18071 Granada Spain Inst Photovoltaique Ile de France 30 RD 128 F-91120 Palaiseav France McMaster Univ Dept Elect & Comp Engn 1280 Main St West Hamilton ON L8S 4K1 Canada

A good modeling of degrading effects in an electronic device, such as the contact region of organic phototransistors (OPTs), can be favorably used to better describe and optimize the performance of the whole device. Furthermore, a proper design of the contacts can enhance the exciton dissociation and the extraction of photogenerated charge in the device. In this work, a compact model for OPTs is developed. This model is valid for all the operation regimes of the transistors. It includes a model for the contact region of the device that incorporates the effects of illumination. The compact model and the contact region model are validated with published experimental data from several OPTs under different illumination conditions. The tool used to validate the model is an evolutionary parameter extraction procedure developed in a previous work. The results show that both photoconductive and photovoltaic effects impact the intrinsic region of the transistor, as well as the electrical behavior of the contact region. The parameters used in the contact region model are linked to these photovoltaic and photoconductive effects.

关键词： Phototransistors Thin-film transistor Device parameters Modeling Contact effects Many-objective optimization multi-objective evolutionary algorithms Photovoltaic effect Photoconductive effect

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Hyper-Heuristics based on Reinforcement Learning, Balanced Heuristic Selection and Group Decision Acceptance

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APPLIED SOFT COMPUTING 2020年第PartA期97卷 106760-106760页

作者： de Santiago Junior, Valdivino Alexandre Ozcan, Ender de Carvalho, Vinicius Renan Inst Nacl Pesquisas Espaciais INPE Coordenacao Pesquisa Aplicada & Desenvolvimento T Av Astronautas 1758 BR-12227010 Sao Jose Dos Campos SP Brazil Univ Nottingham Sch Comp Sci Computat Optimisat & Learning COL Lab Wollaton Rd Nottingham NG8 1BB England Univ Sao Paulo Escola Politecn Poli Av Prof Luciano Gualberto 380 BR-380 Sao Paulo SP Brazil

In this paper, we introduce a multi-objective selection hyper-heuristic approach combining Reinforcement Learning, (meta)heuristic selection, and group decision-making as acceptance methods, referred to as Hyper-Heuristic based on Reinforcement LearnIng, Balanced Heuristic Selection and Group Decision AccEptance (HRISE), controlling a set of multi-objective evolutionary algorithms (MOEAs) as Low-Level (meta)Heuristics (LLHs). Along with the use of multiple MOEAs, we believe that having a robust LLH selection method as well as several move acceptance methods at our disposal would lead to an improved general-purpose method producing most adequate solutions to the problem instances across multiple domains. We present two learning hyper-heuristics based on the HRISE framework for multi-objective optimisation, each embedding a group decision-making acceptance method under a different rule: majority rule (HRISE_M) and responsibility rule (HRISE_R). A third hyper-heuristic is also defined where both a random LLH selection and a random move acceptance strategy are used. We also propose two variants of the late acceptance method and a new quality indicator supporting the initialisation of selection hyper-heuristics using low computational budget. An extensive set of experiments were performed using 39 multi-objective problem instances from various domains where 24 are from four different benchmark function classes, and the remaining 15 instances are from four different real-world problems. The cross-domain search performance of the proposed learning hyperheuristics indeed turned out to be the best, particularly HRISE_R, when compared to three other selection hyper-heuristics, including a recently proposed one, and all low-level MOEAs each run in isolation. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Hyper-heuristic Reinforcement learning Balanced heuristic selection Group decision-making multi-objective evolutionary algorithms multi-objective optimisation

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A genetic algorithm-based framework for mining quantitative association rules without specifying minimum support and minimum confidence

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SCIENTIA IRANICA 2020年第3期27卷 1316-1332页

作者： Moslehi, F. Haeri, A. Iran Univ Sci & Technol Sch Ind Engn Tehran Iran

Discovering association rules is a useful and common technique for data mining, in which relations and co-dependencies of datasets are shown. One of the most important challenges of data mining is to discover the rules of continuous numerical datasets. Furthermore, another restriction imposed by algorithms in this area is the need to determine the minimum threshold for the support and confidence criteria. In this paper, a multi-objective algorithm for mining quantitative association rules is proposed. The procedure is based on the genetic algorithm, and there is no need to determine the extent of the threshold for the support. and confidence criteria. By proposing a multi-criteria method, useful and attractive rules and the most suitable numerical intervals are discovered, without the need to discretize numerical values and determine the minimum support threshold and minimum confidence threshold. Different criteria are considered to determine appropriate rules. In this algorithm, selected rules are extracted based on confidence, interestingness, and cosine(2). The results obtained from real-world datasets demonstrate the effectiveness of the proposed approach. The algorithm is used to examine three datasets, and the results show the superior performance of the proposed algorithm compared to similar algorithms. (C) 2020 Sharif University of Technology. All rights reserved.

关键词： Machine learning Data mining Quantitative association rules multi-objective evolutionary algorithms Genetic algorithm

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Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks prediction

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2020年 104卷 101818-101818页

作者： Jimenez, Fernando Palma, Jose Sanchez, Gracia Marin, David Francisco Palacios, M. D. Lucia Lopez, M. D. Univ Murcia Fac Comp Sci Artificial Intelligence & Knowledge Engn Grp Murcia Spain Univ Hosp Getafe Madrid Spain

Antimicrobial resistance has become one of the most important health problems and global action plans have been proposed globally. Prevention plays a key role in these actions plan and, in this context, we propose the use of Artificial Intelligence, specifically Time Series Forecasting techniques, for predicting future outbreaks of Methicillin-resistant Staphylococcus aureus (MRSA). Infection incidence forecasting is approached as a Feature Selection based Time Series Forecasting problem using multivariate time series composed of incidence of Staphylococcus aureus Methicillin-sensible and MRSA infections, influenza incidence and total days of therapy of both of Levofloxacin and Oseltamivir antimicrobials. Data were collected from the University Hospital of Getafe (Spain) from January 2009 to January 2018, using months as time granularity. The main contributions of the work are the following: the applications of wrapper feature selection methods where the search strategy is based on multi-objective evolutionary algorithms (MOEA) along with evaluators based on the most powerful state-of-the-art regression algorithms. The performance of the feature selection methods has been measured using the root mean square error (RMSE) and mean absolute error (MAE) performance metrics. A novel multi-criteria decision-making process is proposed in order to select the most satisfactory forecasting model, using the metrics previously mentioned, as well as the slopes of model prediction lines in the 1, 2 and 3 steps-ahead predictions. The multi-criteria decision-making process is applied to the best models resulting from a ranking of databases and regression algorithms obtained through multiple statistical tests. Finally, to the best of our knowledge, this is the first time that a feature selection based multivariate time series methodology is proposed for antibiotic resistance forecasting. Final results show that the best model according to the proposed multi-criteria decision making

关键词： Feature selection multi-objective evolutionary algorithms multivariate time series Antibiotic resistance forecasting multiple criteria decision making

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Integrated and dynamic energy modelling of a regional system: A cost-optimized approach in the deep decarbonisation of the Province of Trento (Italy)

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ENERGY 2020年 209卷 118378-118378页

作者： Viesi, Diego Crema, Luigi Mahbub, Md Shahriar Verones, Sara Brunelli, Roberto Baggio, Paolo Fauri, Maurizio Prada, Alessandro Bello, Andrea Nodari, Benedetta Silvestri, Silvia Tomasi, Luca Fdn Bruno Kessler FBK Via Sommar 18 I-38123 Povo TN Italy Ahsanullah Univ Sci & Technol AUST Love Rd 141&142 Dhaka 1208 Bangladesh Agenzia Risorse Idriche & lEnergia APRIE Prov Autonoma Trento Piazza Fiera 3 I-38122 Trento TN Italy Univ Trento UNITN Via Mesiano 77 I-38123 Trento TN Italy Fdn Edmund Mach FEM Via E Mach 1 I-38010 San Michele All Adige TN Italy

Since the Kyoto Protocol (1997), the European Union has fought against climate change adopting European, national and regional policies to decarbonise the economy. Moreover, the Paris Agreement (2015) calls 2050 solutions between -80% and -100% of greenhouse gas emissions compared with 1990. Regions have an important role in curbing CO2 emissions, and tailor-made strategies considering local energy demands, savings potentials and renewables must be elaborated factoring in the social and economic context. An "optimized smart energy system" approach is proposed, considering: (I) integration of electricity, thermal and transport sectors, (II) hourly variability of productions and demands, (III) coupling the EnergyPLAN software, to develop integrated and dynamic scenarios, with a multi-objective evolutionary algorithm, to identify solutions optimized both in terms of CO2 emissions and costs, including decision variables for all the three energy sectors simultaneously. The methodology is tested at the regional scale for the Province of Trento (Italy) analyzing a total of 30,000 scenarios. Compared to the Baseline 2016, it is identified: (I) the strategic role of sector coupling among large hydroelectric production and electrification of thermal and transport demands (heat pumps, electric mobility), (II) slight increases in total annual cost, +14% for a -90% of CO2 emissions in 2050. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Integrated energy system modelling multi-objective evolutionary algorithms Sector coupling Renewable energy Low-carbon economy

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multimodal multi-objective Optimization Using A Density-based One-by-One Update Strategy

Multimodal Multi-objective Optimization Using A Density-base...

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Shi, Ruizhi Lin, Wu Lin, Qiuzhen Zhu, Zexuan Chen, Jianyong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781728121536

For real-world optimization problems, a uniformly and widely distributed Pareto optimal set (PS) in the decision space can provide more choices for decision makers. However, most of multi-objective evolutionary algorithms (MOEAs) only consider convergence and diversity in the objective space, which rarely pay attention to diversity in the decision space. Especially for multimodal multi-objective optimization problems (MMOPs), there may exist multiple distinct PSs corresponding to the same Pareto front (PF). Thus, we propose a novel multimodal multi-objective evolutionary algorithm using a density-based one-by-one update strategy in this paper, which considers diversity in both the objective and decision spaces. In the proposed algorithm, once an offspring is generated during evolution, the most crowded subregion with the largest niche count in the objective space has to be identified again, helpful to maintain diversity in the objective space. Furthermore, the harmonic average distance approach is used to estimate the global density of solutions in the decision space, trying to maintain the population's diversity in the decision space. Our proposed algorithm is compared with several state-of-the-art algorithms on MMOPs. The experimental results demonstrate that our algorithm is capable of preserving promising solutions with even distribution in both of decision space and objective space and also shows the superiority on solving the adopted MMOPs.

关键词： multi-objective evolutionary algorithms multimodal multi-objective optimization global density harmonic average distance

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A multi-objective evolutionary algorithm based on Pareto prediction for automatic test task scheduling problems

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APPLIED SOFT COMPUTING 2018年 66卷 394-412页

作者： Lu, Hui Zhou, Rongrong Fei, Zongming Shi, Jinhua Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Univ Kentucky Dept Comp Sci Lexington KY 40506 USA

The test task scheduling problem (TTSP) is a combinatorial optimization problem still under investigation. A multi-objective evolutionary algorithm based on Pareto prediction (PP-MOEA) is proposed fully considering the characteristics of TTSP. In a multi-objective TTSP, multiple solutions in the decision space correspond to a point in the objective space and the number of true Pareto front in the objective space is relatively small. Moreover, there are less solutions distributing in either high or low objective values while most solutions distribute in average. Based on these characteristics, a pure elitism strategy and a novel scale-down non-dominated sorting (SDNS) method are combined to improve the efficiency and convergence of the algorithm. Additionally, an extended encoding range approach and a Pareto prediction strategy are proposed to help exploring new solutions and increase the diversity of population. The Pareto prediction strategy is implemented by following the historical evolutionary information of the current Pareto set and then predicting the next generation of several individuals. The makespan (maximal test completion time) and the mean workload of the instruments are considered in this research. Based on the general framework of MOEAs, the proposed algorithm is more effective and efficient in tackling multi-objective TTSPs. PP-MOEA has the better performance of convergence and diversity compared with other algorithms based on the statistical analysis of experiments results. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithms Scheduling theory Encoding mechanism Pareto prediction strategy Scale-down non-dominated sorting method

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multi-objective evolutionary algorithm for tuning the Type-2 inference engine on classification task

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SOFT COMPUTING 2018年第15期22卷 5021-5031页

作者： Hinojosa, Edward C. Camargo, Heloisa A. Natl Univ San Agustin Arequipa Peru Univ Fed Sao Carlos Sao Carlos SP Brazil

Type-2 fuzzy systems have been investigated as an alternative formalism to deal with uncertainty when the classic Type-1 fuzzy systems do not offer the suitable flexibility for the representation of the information being modeled. The higher flexibility in representation comes with a higher complexity in the system modeling, mainly in the design of the Type-2 fuzzy sets and in the definition of the inference engine parameters. In this paper, we focus on the Type-2 fuzzy systems design, proposing a multi-objective evolutionary approach for tuning the Type-2 inference engine of a fuzzy rule-based classification system by means of automatically choosing the t-norm used in the inference process. The selection of the t-norm used plays an important hole, since different operators could lead to different results. In a preliminary version of this work, we have proposed an approach to design and optimize Type-2 fuzzy systems that includes the tuning of Type-2 fuzzy sets and the selection of rules. The additional tuning process proposed in this paper is an extension of the previous method in the sense that the same evolutionary procedure performs simultaneously the tuning of the inference mechanism and the tasks performed before. The evolutionary process is executed by means of a multi-objective genetic algorithm with three objectives that aim to balance the accuracy and interpretability of the system generated: the accuracy, the number of rules and the number of conditions in the rules. The proposed method has been compared with a state-of-the-art method proposed in the literature, presenting good results.

关键词： Type-2 fuzzy inference system Fuzzy rule-based classification systems Tuning multi-objective evolutionary algorithms

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