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Discrete evolutionary multi-objective optimization for energy-efficient blocking flow shop scheduling with setup time

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APPLIED SOFT COMPUTING 2020年 93卷 106343-106343页

作者： Han, Yuyan Li, Junqing Sang, Hongyan Liu, Yiping Gao, Kaizhou Pan, Quanke Liaocheng Univ Sch Comp Sci Liaocheng 252059 Shandong Peoples R China Shandong Normal Univ Sch Comp Sci Jinan 252000 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau 999078 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

Sustainable scheduling problems have been attracted great attention from researchers. For the flow shop scheduling problems, researches mainly focus on reducing economic costs, and the energy consumption has not yet been well studied up to date especially in the blocking flow shop scheduling problem. Thus, we construct a multi-objective optimization model of the blocking flow shop scheduling problem with makespan and energy consumption criteria. Then a discrete evolutionary multi-objective optimization (DEMO) algorithm is proposed. The three contributions of DEMO are as follows. First, a variable single-objective heuristic is proposed to initialize the population. Second, the self-adaptive exploitation evolution and self-adaptive exploration evolution operators are proposed respectively to obtain high quality solutions. Third, a penalty-based boundary interstation based on the local search, called by PBI-based-local search, is designed to further improve the exploitation capability of the algorithm. Simulation results show that DEMO outperforms the three state-of-the-art algorithms with respect to hypervolume, coverage rate and distance metrics. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Blocking flow shop Energy consumption multi-objective evolutionary optimization Self-adaptive

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Synthesis of Wide-Angle Scanning Arrays Through Array Power Control

IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION

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IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION 2025年第2期6卷 468-477页

作者： Rosatti, Pietro Oliveri, Giacomo Massa, Andrea Univ Trento ELEDIA Res Ctr Dept Civil Environm & Mech Engn DICAM I-38123 Trento Italy Univ Trento ELEDIA Res Unit CNIT I-38123 Trento Italy UESTC ELEDIA Res Ctr Sch Elect Engn Chengdu 611731 Peoples R China Tsinghua Univ ELEDIA Res Ctr Beijing 100084 Peoples R China Tel Aviv Univ Sch Elect Engn IL-69978 Tel Aviv Israel

A new methodology for the synthesis of wide-angle scanning arrays is proposed. It is based on the formulation of the array design problem as a multi-objective one where, for each scan angle, both the radiated power density in the scan direction and the total reflected power are accounted for. A set of numerical results from full-wave simulated examples - dealing with different radiators, arrangements, frequencies, and number of elements - is reported to show the features of the proposed approach as well as to assess its potentialities. A widening of the field-of-view percentage of up to 22% with respect to standard scanning methods is demonstrated.

关键词： Antenna arrays Power system measurements Density measurement Phased arrays Couplings Telecommunications Power control optimization Next generation networking Indexes Wide-angle scanning excitation design array synthesis active reflection coefficient stabilization multi-objective evolutionary optimization

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Two-Level evolutionary multi-objective optimization of a District Heating System with Distributed Cogeneration

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ENERGIES 2019年第1期12卷 114-114页

作者： Casisi, Melchiorre Costanzo, Stefano Pinamonti, Piero Reini, Mauro Univ Udine Polytech Dept Engn & Architecture I-33100 Udine Italy ESTECO SpA Area Sci PkPadriciano 99 I-34149 Trieste Italy Univ Trieste Dept Engn & Architecture I-34100 Trieste Italy

The paper deals with the modeling and optimization of an integrated multi-component energy system. On-off operation and presence-absence of components must be described by means of binary decision variables, besides equality and inequality constraints;furthermore, the synthesis and the operation of the energy system should be optimized at the same time. In this paper a hierarchical optimization strategy is used, adopting a genetic algorithm in the higher optimization level, to choose the main binary decision variables, whilst a MILP algorithm is used in the lower level, to choose the optimal operation of the system and to supply the merit function to the genetic algorithm. The method is then applied to a distributed generation system, which has to be designed for a set of users located in the center of a small town in the North-East of Italy. The results show the advantage of distributed cogeneration, when the optimal synthesis and operation of the whole system are adopted, and significant reduction in the computing time by using the proposed two-level optimization procedure.

关键词： district heating multi-objective evolutionary optimization distributed cogeneration optimal operation

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Scenario-based multi-period program optimization for capability-based planning using evolutionary algorithms

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APPLIED SOFT COMPUTING 2017年 56卷 717-729页

作者： Shafi, Kamran Elsayed, Saber Sarker, Ruhul Ryan, Michael Univ New South Wales Canberra SEIT Canberra ACT 2600 Australia Zagazig Univ Zagazig Egypt

Capability based planning (CBP) is a strategy focused planning framework that facilitates organizations to systematically develop capacity to achieve their business objectives in highly uncertain, dynamic and competitive environments. Capability programming is an integral part of CBP which requires selecting a portfolio of capability projects for execution, referred as a capability program, such that the overall strategic risk facing the planning organization across a number of projected future operating scenarios is minimized while maintaining the most economical choice. It is a challenging optimization problem that requires handling a number of dynamic constraints and objectives that vary throughout the entire planning horizon. An optimizing simulation approach is presented in this paper that combines an evolutionary multi-objective optimization algorithm with a reinforcement learning technique to generate capability programs which optimize strategic risks and program costs across multiple planning scenarios as well as over a rolling planning horizon. The role of the optimization algorithm in this approach is to search for the non-dominated capability programs at each decision point by minimizing the strategic risks associated with individual capability projects across a number of planning scenarios as well as the total cost of the program. The reinforcement learning algorithm, on the other hand, searches horizontally within the set of non-dominated programs to minimize capability risks and costs over the entire planning horizon. The methodology is evaluated on a test problem generated based on the data distributions in an Australian Defence Capability Plan and the performance is compared with two myopic heuristic methods. (c) 2016 Elsevier B.V. All rights reserved.

关键词： Capability-based planning Program optimization Project portfolio optimization multi-objective evolutionary optimization

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evolutionary multiobjective optimization for Portfolios in Emerging Markets: Contrasting Higher Moments and Median Models 19th

Evolutionary Multiobjective Optimization for Portfolios in E...

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19th European Conference on the Applications of evolutionary Computation (EvoApplications)

作者： Ibrahim, Mai A. El-Beltagy, Mohammed Khorshid, Motaz Cairo Univ Fac Comp & Informat Operat Res & Decis Support Dept Giza Egypt

ISBN: (纸本)9783319312040

multi-objective evolutionary algorithms are well suited to Portfolio optimization and hence have been applied in complex situations were traditional mathematical programming falls short. Often they were used in portfolios scenario of classical Mean-Variance which are not applicable to the Emerging Markets. Emerging Markets are characterized by return distributions that have shown to exhibit significance departure from normality and are characterized by skewness and fat tails. Therefore higher moments models and median models have been suggested in the literature for asset allocation in this case. Three higher moment models namely the Mean-Variance-Skewness, Mean-Variance-Skewness-Kurtosis, Mean-Variance-Skewness-Kurtosis for return and liquidity and three median models namely the Median-Value at Risk, Median-Conditional Value at Risk and Median-Mean Absolute Deviation are formulated as a multi-objective problem and solved using a multi-objective evolutionary algorithm namely the non-dominated sorting genetic algorithm II. The six models are compared and tested on real financial data of the Egyptian Index EGX. The median models were found in general to outperform the higher moments models. The performance of the median models was found to be better as the out-sample time increases.

关键词： Median models Higher moment models multi-objective evolutionary optimization Non-dominated sorting genetic algorithm II Egyptian stock exchange

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Reducing systemic risk in multiplex networks using evolutionary optimization 17

Reducing systemic risk in multiplex networks using evolution...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Michalak, Krzysztof Wroclaw Univ Econ Dept Informat Technol Inst Business Informat Komandorska 118-120 PL-53345 Poland Poland

ISBN: (纸本)9781450349390

This paper concerns the topic of cascading failures in a system containing economic entities. Cascading failures occur when some of the entities fail and, because of the relationships on the market, cause many more entities to fail. In this paper a multiobjective optimization problem is formulated for an economic model involving companies composed of a core-business and a subsidiary-business unit. A multiobjective optimization algorithm is used to solve the optimization problem, thereby making the system more robust against failures. Subsequently, the properties of the optimized system are studied. Solving the proposed optimization problem diminishes the severity of cascading failures occurring in the system and increases the threshold coupling strength between business units for which the cascading failure occurs.

关键词： Graph-based optimization multi-objective evolutionary optimization Cascading failures

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Energy Consumption Prediction in Residential Buildings-An Accurate and Interpretable Machine Learning Approach Combining Fuzzy Systems with evolutionary optimization

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ENERGIES 2024年第13期17卷 3242页

作者： Gorzalczany, Marian B. Rudzinski, Filip Kielce Univ Technol Dept Elect & Comp Engn Al 1000-lecia P-P-7 PL-25314 Kielce Poland

This paper addresses the problem of accurate and interpretable prediction of energy consumption in residential buildings. The solution that we propose in this work employs the knowledge discovery machine learning approach combining fuzzy systems with evolutionary optimization. The contribution of this work is twofold, including both methodology and experimental investigations. As far as methodological contribution is concerned, in this paper, we present an original designing procedure of fuzzy rule-based prediction systems (FRBPSs) for accurate and transparent energy consumption prediction in residential buildings. The proposed FRBPSs are characterized by a genetically optimized accuracy-interpretability trade-off. The trade-off optimization is carried out by means of multi-objective evolutionary optimization algorithms-in particular, by our generalization of the well-known strength Pareto evolutionary algorithm 2 (SPEA2). The proposed FRBPSs' designing procedure is our original extension and generalization (for regression problems operating on continuous outputs) of an approach to designing fuzzy rule-based classifiers (FRBCs) we developed earlier and published in 2020 in this journal. FRBCs operate on discrete outputs, i.e., class labels. The experimental contribution of this work includes designing the collection of FRBPSs for residential building energy consumption prediction using the data set published in 2024 and available from Kaggle Database Repository. Moreover, the comparison with 20 available alternative approaches is carried out, demonstrating that our approach significantly outperforms alternative methods in terms of interpretability and transparency of the energy consumption predictions made while remaining comparable or slightly superior in terms of the accuracy of those predictions.

关键词： energy consumption prediction accurate and interpretable prediction systems machine learning fuzzy rule-based prediction systems multi-objective evolutionary optimization

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Intelligent controllers for bi-objective dynamic scheduling on a single machine with sequence-dependent setups

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APPLIED SOFT COMPUTING 2013年第12期13卷 4704-4717页

作者： Xanthopoulos, A. S. Koulouriotis, D. E. Tourassis, V. D. Emiris, D. M. Democritus Univ Thrace Dept Prod & Management Engn GR-67100 Xanthi Greece Univ Piraeus Dept Ind Management & Technol Piraeus 18534 Greece

This article addresses the problem of dynamic job scheduling on a single machine with Poisson arrivals, stochastic processing times and due dates, in the presence of sequence-dependent setups. The objectives of minimizing mean earliness and mean tardiness are considered. Two approaches for dynamic scheduling are proposed, a Reinforcement Learning-based and one based on Fuzzy Logic and multi-objective evolutionary optimization. The performance of the two scheduling approaches is tested against the performance of 15 dispatching rules in four simulation scenarios with different workload and due date pressure conditions. The scheduling methods are compared in terms of Pareto optimal-oriented metrics, as well as in terms of minimizing mean earliness and mean tardiness independently. The experimental results demonstrate the merits of the proposed methods. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Scheduling Dispatching rules Reinforcement Learning Fuzzy Logic multi-objective evolutionary optimization

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An execution, monitoring and replanning approach for optimal energy management in microgrids

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ENERGY 2011年第5期36卷 3429-3436页

作者： Sanseverino, Eleonora Riva Di Silvestre, Maria Luisa Ippolito, Mariano Giuseppe De Paola, Alessandra Lo Re, Giuseppe Univ Palermo DIEET I-90128 Palermo Italy Univ Palermo DINFO I-90128 Palermo Italy

This work develops a new approach for optimal energy management of electrical distribution 'smartgrids'. Optimality aims at improving sustainability through the minimization of carbon emissions and at reducing production costs and maximizing quality. Input data are the forecasted loads and productions from renewable generation units, output data are a set of control actions for the actuators. The considered electrical distribution system includes storage units that must be considered over a 24 h time interval, to consider an entire charge and discharge cycle. The objectives for the optimal management of distributed (renewables and not) generation are technical, economical and environmental. It is thus required to solve a multi-objective optimization problem over a 24 h time interval considering the uncertainty associated to weather conditions and loads profiles. The novelty of the proposed approach resides in considering the optimal scheduling of generation units an automatic planning process in a dynamic, non-deterministic and not fully observable environment, as it is, getting closer to actual conditions. The system proposed here is a planning and execution scheduler which allows the central controller to monitor the execution of a scheduling plan, interrupt the monitoring to input new information and repair the plan under execution every time interval. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Distributed energy resources management multi-objective evolutionary optimization Microgrids

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A Loop Pairing Method for Non-Linear multivariable Control Systems Under a multi-objective optimization Approach

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IEEE ACCESS 2020年 8卷 41262-41281页

作者： Huilcapi, Victor Blasco, Xavier Herrero, Juan Manuel Reynoso-Meza, Gilberto Univ Politecn Salesiana Fac Ingn Guayaquil 09014752 Ecuador Univ Politecn Valencia Inst Univ Automat & Informat Ind E-46022 Valencia Spain Pontificia Univ Catolica Parana PUCPR PPGEPS BR-80215901 Curitiba Parana Brazil

In the multivariable control literature there are few techniques that face the problem of selecting suitable loop pairings in non-linear multivariable systems. Most techniques analyze the linearized system at a specific operating point. This paper proposes a new methodology to optimally and simultaneously select the loop pairings and the tuning of the parameters of the decentralized control by applying a multi-objective optimization approach directly on the non-linear system. The main contribution of this work is that the proposed methodology enables a detailed multi-dimensional analysis of the performances and trade-offs in the available loop pairings to control a multivariable non-linear system. The methodology is applied in this paper to three examples that analyze how the different types of loop pairings conflict. In one of the examples, the proposed methodology was applied first in the linearized system and later in the non-linear system. The results were contradictory and show how the application of loop pairing techniques for linear systems can be inaccurate when they are applied on a non-linear system previously linearized at an operating point. The following examples show that the operating point of a non-linear system, the design objectives of each multi-objective problem, as well as the designer's preferences have important roles in the selection of an optimal loop pairing.

关键词： MIMO communication optimization Tuning Decentralized control Process control Tools multivariable control system non-linear systems loop pairing decentralized control structures multi-objective evolutionary optimization Pareto front

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