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On the design of a framework integrating an optimization engine with streaming technologies

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 107卷 538-550页

作者： Barba-Gonzalez, Cristobal Nebro, Antonio J. Benitez-Hidalgo, Antonio Garcia-Nieto, Jose Aldana-Montes, Jose F. Univ Malaga Dept Lenguajes & Ciencias Computat Ada Byron Res Bldg E-29071 Malaga Spain

A number of streaming technologies have appeared in the last years as a result of the rising of Big Data applications. Nowadays, deciding which technology to adopt is not an easy task due not only to the number of available data streaming processing projects, but also because they are continuously evolving. In this paper, we focus on how these issues have affected jMetalSP, a framework for dynamic multi-objective optimization that incorporates streaming features. jMetalSP allows the development of three tier optimization workflows where the central component is an optimizer that is continuously solving a dynamic multi-objective optimization problem. This problem can change as a consequence of the analysis of data streams carried out by components that use the Apache Spark streaming engine. A third kind of components receive and process the Pareto front approximations being yielded by the optimization algorithm. However, all jMetalSP elements are tightly coupled and linked to Spark, making it difficult to use a different streaming system. To overcome this issue, we have redesigned the jMetalSP architecture to make it flexible enough to avoid the dependence of any particular streaming system. This way, popular Apache projects such as Spark Structured Streaming, Kafka Streams, or Flink can be used without requiring to change the rest of components of the application. Furthermore, Kafka can be used for inter-process communication, what enables the execution of components in different nodes of a cluster, independently of their implementation languages thanks to the serialization of data streams with Apache Avro. We show how the embraced solution provides a high degree of flexibility that enhances the usability of jMetalSP. To this end, a representative case study based on a transport problem is conducted that focuses on data representation and performance evaluation of the Spark, Flink, and Kafka systems. (C) 2020 Elsevier B.V. All rights reserved.

关键词： dynamic multi-objective optimization Streaming data processing Big data Software framework

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multi-objective optimization of HVAC Operation for Balancing Energy Use and Occupant Comfort in Educational Buildings

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ENERGIES 2021年第10期14卷 2847页

作者： Franco, Alessandro Bartoli, Carlo Conti, Paolo Miserocchi, Lorenzo Testi, Daniele Univ Pisa Dept Energy Syst Terr & Construct Engn DESTEC I-56122 Pisa Italy

The paper provides a methodology for the optimal control of heating, ventilation, and air conditioning (HVAC) systems used in public buildings, with the purpose of obtaining high comfort and safety standards along with energy efficiency. The combination of the two concurrent objectives of minimizing energy use and guaranteeing high standards of occupant comfort is obtained by means of multi-objective optimization, in which a comfort model is combined along with a dynamic energy model of the building. The use of dynamic setpoints for the HVAC and the inclusion of comfort indicators represent a step forward, compared to the current design and operation procedures suggested by technical standards. The utilization of the proposed methodology is tested with reference to a case study, represented by an academic building used by the University of Pisa for educational purposes, whose extensive and variable occupancy can help to emphasize the importance of comfort in the operation of HVAC systems in different climatic conditions and with different occupancy profiles. We show how this optimization brings interesting results in terms of energy-saving (up to 30%), obtaining an increased comfort level (of more than 25%) compared to the operating conditions suggested by technical standards.

关键词： energy efficiency improvement occupant comfort HVAC operation dynamic multi-objective optimization shared smart buildings sustainability goals

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An ensemble learning based multi-objective evolutionary algorithm for the dynamic vehicle routing problem with time windows

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 154卷 107131-107131页

作者： Wang, Feng Liao, Fanshu Li, Yixuan Yan, Xuesong Chen, Xu Wuhan Univ Sch Comp Sci 299 Bayi Rd Wuchang Wuhan 430072 Peoples R China China Univ Geosci Wuhan Sch Comp Sci Wuhan 430074 Hubei Peoples R China

The Vehicle Routing Problem (VRP) is a typical combinatorial optimization problem and has been studied for many years. However, there are few researches on the dynamic Vehicle Routing Problem with Time Window (DVRPTW), which is an extension of VRP and more challenging with changing environmental factors, such as stochastic customer requests. Once changes happen, the routes should be adjusted for the new environments. In this paper, we construct a multi-objective optimization model for the DVRPTW and propose a new algorithm named as EL-DMOEA, where an ensemble learning method is investigated to improve the performance of the algorithm. In EL-DMOEA, to enhance the population's diversity and accelerate the convergence, three different strategies, i.e., population-based prediction strategy, immigrant strategy and random strategy, are employed in the training process of three kinds of basic models respectively. The experimental results on the test benchmarks reveal that the proposed algorithm is effective to make promising routing plans.

关键词： dynamic vehicle routing problem dynamic multi-objective optimization Ensemble learning

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dynamic multi-objective evolutionary algorithm with objective space prediction strategy

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APPLIED SOFT COMPUTING 2021年 107卷 107258-107258页

作者： Guerrero-Pena, Elaine Araujo, Aluizio F. R. Univ Fed Pernambuco Recife PE Brazil

To solve dynamic multi-objective optimization problems, a dynamic multi-objective evolutionary algorithm (DMOEA) must be able to deal with the dynamics of the environment, and such modifications can lead to new optimal solutions over time. Various algorithms have been proposed that modify the way a change is handled. Among them, prediction-based methods are promising for solving this kind of problem. They provide guided direction for population evolution through a prediction mechanism that assists the DMOEA to respond quickly to new changes. Based on these strategies, we propose a dynamic non-dominated sorting differential evolution improvement with prediction in the objective space (DOSP-NSDE). The proposal uses the objective space prediction (OSP) strategy for both the static evolutionary process (between changes) and the change reaction mechanism to predict the new optimal front location. Experiments were performed on a real-world problem and four sets of test problems: FDA, dMOP, UDF, and DF. Comparison of DOSP-NSDE with several algorithms in the literature, considering three metrics, is presented, showing that the proposal is competitive with most problems. (C) 2021 Elsevier B.V. All rights reserved.

关键词： dynamic multi-objective optimization Evolutionary computation Prediction-based methods Forecast Probabilistic modeling

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dynamic multi-objective optimization based on membrane computing for control of time-varying unstable plants

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INFORMATION SCIENCES 2011年第11期181卷 2370-2391页

作者： Huang, Liang Suh, Il Hong Abraham, Ajith Hanyang Univ Intelligence & Commun Robots Lab Dept Comp Sci & Engn Coll Engn Seoul 133791 South Korea SNIRE Machine Intelligence Res Labs MIR Labs Auburn WA 98071 USA

dynamic multi-objective optimization is a current hot topic. This paper discusses several issues that has not been reported in the static multi-objective optimization literature such as the loss of non-dominated solutions, the emergence of the false non-dominated solutions and the necessity for an online decision-making mechanism. Then, a dynamic multi-objective optimization algorithm is developed, which is inspired by membrane computing. A novel membrane control strategy is proposed in this article and is applied to the optimal control of a time-varying unstable plant. Experimental results clearly illustrate that the control strategy based on the dynamic multi-objective optimization algorithm is highly effective with a short rise time and a small overshoot. (C) 2011 Elsevier Inc. All rights reserved.

关键词： dynamic multi-objective optimization Time-varying system Membrane computing (P systems) Membrane control strategy

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Surrogate Assisted Evolutionary Algorithm Based on Transfer Learning for dynamic Expensive multi-objective Optimisation Problems

Surrogate Assisted Evolutionary Algorithm Based on Transfer ...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Fan, Xuezhou Li, Ke Tan, Kay Chen City Univ Hong Kong Dept Comp Sci Tat Chee Ave Hong Kong Peoples R China Univ Exeter Dept Comp Sci Exeter EX4 4QF Devon England

ISBN: (纸本)9781728169293

dynamic multi-objective optimisation has attracted increasing attention in the evolutionary multi-objective optimisation community in recent years. Comparing to its static counterpart, which has been studied for more than half a century, the involvement of dynamic and uncertain features, including but not limited to the changing Pareto-optimal set, Pareto-optimal front and problem formulation, pose significant more challenges to evolutionary algorithms. This will become even more complicated when the underlying problem involves computationally expensive objective functions which are not rare in many real world application scenarios. In this paper, we pave an initial step towards the study of dynamic multi-objective optimisation with computationally expensive objective functions. More specifically, we use a surrogate assisted evolutionary algorithm, MOEA/D-EGO in particular, as the baseline in order to carry out evolutionary optimisation with a limited amount of function evaluations. Furthermore, instead of restart the MOEMD-EGO from scratch after each change, we use transfer learning to map the previously archived training data to the current landscape in order to jump start the surrogate model building process. By doing so, we can expect a better adaptation to the new environment. Proof-of-concept experiments fully demonstrate the effectiveness of our proposed method.

关键词： dynamic multi-objective optimization evolutionary algorithm surrogate modeling Gaussian process transfer learning

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Design of optimal wine distillation recipes using multi-criteria decision-making techniques

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COMPUTERS & CHEMICAL ENGINEERING 2021年 145卷 107194-107194页

作者： Luna, Ricardo Lopez, Francisco Perez-Correa, Jose R. Ctr Res & Innovat CRI Vina Concha y Toro Pencahue 3550000 Chile Univ Rovira & Virgili Dept Engn Quim Av Paisos Catalans 26 Tarragona 43007 Spain Pontificia Univ Catolica Chile Dept Chem & Bioproc Engn Vicuna Mackenna 4860 Santiago Chile

In this work, dynamic multi-objective optimization and multi-criteria decision making (MCDM) techniques were applied to design recipes for optimal alembic wine distillations. The optimization problem considered eight objectives associated with sensorial quality, food safety, productivity, and efficiency. Through correlation analysis of the optimal solutions, it was possible to reduce the original objectives into four conflicting objectives. Seven MCDM methods were tested to select the best recipes considering five scenarios. In the equal priority scenario, each multi-criterion decision-making algorithm selected different solutions. In scenarios with higher priority for a single objective, most multi-criteria decision-making algorithms selected similar or identical solutions. A well balanced performance was achieved when a high priority was assigned to ethanol recovery. This works methodology can be easily adapted to define recipes for the optimal operation of any fruit batch distillation process. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： dynamic multi-objective optimization multi-criteria decision making Weighted cost function Batch distillation Distillation recipes Spirits

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A dynamic multiple populations particle swarm optimization algorithm based on decomposition and prediction

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APPLIED SOFT COMPUTING 2018年 73卷 434-459页

作者： Liu, Ruochen Li, Jianxia Fan, Jing Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Int Res Ctr Intelligent Percept & Computat Minist EducInt Collaborat Joint Lab Intelligent Xian 710071 Shaanxi Peoples R China

dynamic multi-objective optimization problems (DMOPs) involve multiple optimization objectives which are in conflict with one another and change over time or environment. A novel dynamic multiple population particle swarm optimization algorithm based on decomposition and prediction (denoted as DP-DMPPSO) is proposed to solve DMOPs. Each objective is optimized by one population and each population shares their information with other populations. The populations evolve independently using a modified particle swarm optimization (PSO). An external archive is adopted to store the non-dominated solutions selected from all populations in the evolutionary process and the archive will be output as the final solution. A mechanism for updating the archive based on the objective space decomposition (DOS) is proposed. In addition, a population prediction mechanism is employed to accelerate the convergence to the true Pareto front. DP-DMPPSO is tested on a set of benchmark problems and compared with several state-of-the-art algorithms. The results show DP-DMPPSO is highly competitive for solving dynamic multi-objective optimization problems. (C) 2018 Elsevier B.V. All rights reserved.

关键词： dynamic multi-objective optimization multiple populations Decomposition of objective space Population prediction mechanism Particle swarm optimization

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dynamic multi-objective Estimation of Distribution Algorithm based on Domain Adaptation and Nonparametric Estimation

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INFORMATION SCIENCES 2018年 435卷 203-223页

作者： Jiang, Min Qiu, Liming Huang, Zhongqiang Yen, Gary G. Xiamen Univ Dept Cognit Sci & Technol Xiamen Peoples R China Xiamen Univ Fujian Key Lab Brain Like Comp & Applicat Xiamen Peoples R China Oklahoma State Univ Sch Elect & Comp Engn 202 Engn South Stillwater OK 74078 USA

Although dynamic optimization and multi-objective optimization have made considerable progress individually, solving dynamic multi-objective optimization problems remains a monumental challenge since their multiple, conflicting objectives could change over time. In this paper, we propose a Domain Adaptation and Nonparametric Estimation-based Estimation of Distribution Algorithm, called DANE-EDA, to solve dynamic multi-objective optimization problems. Notable features of the proposed algorithm include the importance sampling, nonparametric density estimation, probabilistic prediction, and a domain adaptation technique seamlessly unified under an innovative framework. The design takes full advantage of the powerful Monte-Carlo method and transfer learning technique. This kind of combination will help the proposed algorithm to maintain a delicate exploration exploitation trade-off from temporal and spatial perspectives. At the same time, it will help the proposed algorithm to overcome the shortcomings caused by transfer learning, specifically, the loss of the diversity. After proving convergence and analyzing the computational complexity of the DANE-EDA, we compare the proposed method with nine EDAs or dynamic multi-objective optimization algorithms on twelve different test instances. The experimental results affirm the effectiveness of the proposed method in addressing dynamic multi-objective optimization problems. (C) 2018 Elsevier Inc. All rights reserved.

关键词： dynamic multi-objective optimization Domain adaption Nonparametric statistics Importance sampling Transfer learning

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InDM2: Interactive dynamic multi-objective Decision Making Using Evolutionary Algorithms

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SWARM AND EVOLUTIONARY COMPUTATION 2018年 40卷 184-195页

作者： Nebro, Antonio J. Ruiz, Ana B. Barba-Gonzalez, Cristobal Garcia-Nieto, Jose Luque, Mariano Aldana-Montes, Jose F. Univ Malaga Dept Lenguajes & Ciencias Comp Ada Byron Res Bldg E-29071 Malaga Spain Univ Malaga Dept Appl Econ Math Campus El Ejido E-29071 Malaga Spain

dynamic optimization problems involving two or more conflicting objectives appear in many real-world scenarios, and more cases are expected to appear in the near future with the increasing interest in the analysis of streaming data sources in the context of Big Data applications. However, approaches combining dynamic multi objective optimization with preference articulation are still scarce. In this paper, we propose a new dynamic multi-objective optimization algorithm called InDM2 that allows the preferences of the decision maker (DM) to be incorporated into the search process. When solving a dynamic multi-objective optimization problem with InDM2, the DM can not only express her/his preferences by means of one or more reference points (which define the desired region of interest), but these points can be also modified interactively. InDM2 is enhanced with methods to graphically display the different approximations of the region of interest obtained during the optimization process. In this way, the DM is able to inspect and change, in optimization time, the desired region of interest according to the information displayed. We describe the main features of InDM2 and detail how It is implemented. Its performance is illustrated using both synthetic and real-world dynamic multi-objective optimization problems.

关键词： dynamic multi-objective optimization multiple criteria decision making Preferences Evolutionary algorithms jMetalSP

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