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Energy disaggregation considering least square error and temporal sparsity: A multi-objective evolutionary approach

SWARM AND EVOLUTIONARY COMPUTATION 2021年 64卷 100909-100909页

作者： Ghorbanpour, Samira Pamulapati, Trinadh Mallipeddi, Rammohan Lee, Minho Kyungpook Natl Univ Sch Elect & Elect Engn Daegu South Korea Kyungpook Natl Univ Sch Elect Engn Dept Artificial Intelligence Daegu South Korea

In energy disaggregation (ED), an accurate estimation of each appliance power consumption over time is crucial for practical applications. Most optimization-based ED approaches are single-objective and the disaggregated appliance power consumption profiles provided by them do not match the actual operational characteristics of the devices. In this paper, we propose a multi-objective ED (MOED) framework considering two conflicting objectives 1) minimizing the least square error between the measured and estimated aggregated signals;and 2) minimizing the temporal sparsity of appliance switching events. The multi-objective consideration results in a set of trade-off solutions where each solution represents a prospective disaggregation corresponding to the operation of different devices present. From the trade-off set, a disaggregated solution where the operational characteristics of appliances are close to practical operation is identified by evaluating statistical similarity. For each prospective solution, statistical similarity is evaluated with respect to a set of reference signals corresponding to the practical device operation provided by the device manufacturer. In addition, to effectively solve the MOED problem, an initialization based on Integer Programming (IP), problem-specific mutation and crossover operators are proposed. The performance of the proposed MOED framework is tested on real-world dataset from University of Victoria (UVic) and compared with other optimization approaches. On various indicators MOED shows better performance than conventional and improved Integer Programming based single-objective ED algorithms. In addition, the disaggregated signals provided by MOED are close to the practical operational characteristics of the respective appliances compared to disaggregated signals provided by the state-of-the-art ED approaches.

关键词： Energy disaggregation Non-intrusive appliance load monitoring Sparsity multi-objective optimization

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Implementing Search-Based Software Engineering approaches with Nautilus 21

Implementing Search-Based Software Engineering Approaches wi...

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35th Brazilian Symposium on Software Engineering, SBES 2021, held in conjunction with the Brazilian Conference on Software: Theory and Practice, CBSoft 2021

作者： Ferreira, Thiago Do Nascimento Vergilio, Silvia Regina Kessentini, Marouane University of Michigan DearbornMI United States DInf Federal University of Paraná Curitiba Brazil

ISBN: (纸本)9781450390613

Search-Based Software Engineering (SBSE) approaches adopt search-based techniques to solve Software Engineering (SE) optimization problems. Among these techniques, evolutionary algorithms are the most popular and successfully used, such as multi-objective evolutionary algorithms. However, some challenges still need to be addressed. Firstly, SE problems are complex and commonly impacted by many conflicting factors. In this context, the use of many-objective algorithms is necessary. Secondly, the users very often do not recognise the found solutions as feasible because these solutions are usually not generated considering the users' needs and preferences. Thus, to deal properly with this situation, preference-based algorithms should be applied. Moreover, there are some practical issues regarding the choice of operators, evaluation of algorithms and visualization of solutions. Existing frameworks do not provide support to address these challenges. To overcome these limitations, we present Nautilus, an open-source Java web-platform tool that works with plugins to ease the addition of new problem instances, implementation of search operators and different multi and many-objective optimization algorithms, guided (or not) by human participation. This paper describes Nautilus-NRP, an extension implemented to address the Next Release Problem (NRP). NRP refers to the selection of requirements to be implemented in the next release of a software and is used to illustrate Nautilus' main functionalities and how it can be extended to solve a SE problem. Link for the video: https://***/2dbwslTrvhg. © 2021 ACM.

关键词： Open source software

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Dynamic evolutionary multiobjective optimization for open-order coil allocation in the steel industry

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APPLIED SOFT COMPUTING 2023年 146卷

作者： Zou, Fei Guo, Qingxin Yen, Gary G. Shenyang Univ Technol Sch Artificial Intelligence Shenyang 110870 Peoples R China Northeastern Univ Natl Frontiers Sci Ctr Ind Intelligence & Syst Opt Shenyang 110819 Peoples R China Northeastern Univ Key Lab Data Analyt & Optimizat Smart Ind Minist Educ Shenyang Peoples R China Liaoning Key Lab Mfg Syst & Logist Optimizat Shenyang Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

In terms of improving coil resource utilization and customer satisfaction, most steel companies are still far behind in these quality objectives and need significant improvements. Scientific approaches to production and operations planning are critical to improve its situation. Motivated by this phenomenon, this study investigates a challenging operations decision problem on allocating open-order coils to customer orders in the steel industry. In this article, a dynamic multiobjective optimization model is formulated to optimize the total mismatching costs, surplus inventory and coil utilization considering various practical dynamisms, such as production changes and unscheduled arrivals of new customer orders. To address this problem, we propose a multiobjective evolutionary algorithm based on knee driven change response strategy and Pareto local search mechanism, so called KPLSEA. This proposed approach, which combines the information of decision-making stages with the evolutionary search, significantly reduces the computational cost and promotes the evolutionary search to converge quickly. Extensive experiments are performed on real-world production benchmark instances. Computational comparisons with other state-of-art algorithms validate that the proposed algorithm could generate effective and practical solutions in dynamic environments as well as provide the decision makers with a satisfied near-optimal solution.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Dynamic multi-objective open-order coil allocation Steel industry Knee solution Pareto local search

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multi-objective optimization of the 3D container stowage planning problem in a barge convoy system

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COMPUTERS & OPERATIONS RESEARCH 2022年 144卷 1页

作者： El Yaagoubi, Amina Charhbili, Mohamed Boukachour, Jaouad Alaoui, Ahmed El Hilali Normandie Univ UNIHAVRE F-76600 Le Havre France Euro Mediterranean Fez Fes Morocco

Mainly motivated by the massification of flows in inland shipping, where several barges can be assembled in convoys to transport a large number of containers to and from the hinterland, we introduce an original extension of the Container Stowage Planning Problem (CSPP) (which we denote the 3D - barge Convoy Container Stowage Planning Problem (3D -CCSPP)). The requirements for stowing all containers in a barge convoy entail additional constraints related to both the containers and the convoy. In this paper, the 3D -CCSPP is dealt with, on the basis of its relation to the 3D Bin-Packing Problem (3D-BPP), in which the filled objects represent the containers, and the bins indicate the convoy barges. We consider the multi-objective aspect of the problem with three realistic functions to be optimized: shifting movements, the convoy stability and the number of real-used barges. This is a new and effective aspect considering the state of the art of CSPP in inland shipping. We develop a multi-objective mathematical formulation of the 3D-CCSPP and propose a novel adaptation of the multi-objective evolutionary algorithm NSGA-II (Non-Dominated Sorting Genetic Algorithm-II) based on a set of heuristics introduced by the BPP resolution methods. Specific encoding method, generation of the initial population and genetic operators are designed based on the mathematical description of the problem. Extensive experiments are then carried out on a varied set of instances. The computational results demonstrate the effectiveness of the proposed approaches, they are evaluated using CPLEX solver and their performance is verified by a set of measurements which are adapted to the problem in real-world applications in relation to each objective function. Additional studies are carried out in sensitivity analysis to provide decision makers with a better knowledge of the problem. They can then choose, from the set of compromise solutions of the Pareto front, the most promising one for practical

关键词： Shifting movements Barge convoy system Containers loading/unloading Stability Bin-packing multi-objective optimization NSGA-II

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Scalable approaches to Selecting Key Entities in Large Networked Infrastructure Systems

Scalable Approaches to Selecting Key Entities in Large Netwo...

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9th IEEE International Conference on Big Data (IEEE BigData)

作者： Sathanur, Arun, V Khan, Arif Pacific Northwest Natl Lab Phys & Computat Sci Directorate Seattle WA 98109 USA

ISBN: (纸本)9781665439022

This work aims at bringing advances in discrete optimization algorithms to solving practical engineering problems at scale. Often times, in many engineering design problems, there is a need to select a small set of influential or representative elements from a large ground set of entities in an optimal fashion. Submodular optimization provides for a formal way to solve such problems. Common examples with infrastructure systems involve sensor placement and identification of key entities with certain objectives. However, scaling these approaches to large infrastructure systems can be challenging because of the high computational complexity of the overall framework that include the optimization algorithms as well as high-complexity compute-oracles that provide the necessary objective function values. In this work, we explore a well-studied and widely-applicable paradigm, namely leader-selection in a multi-agent networked setting in the context of scalable methodologies. We demonstrate novel frameworks that utilize variations of accelerated submodular optimization algorithms along with linear-algebraic methods that can help accelerate the oracle computations. We further explore this combination in conjunction with graph partitioning paradigms to take advantage of the accelerated algorithms in a distributed setting. Finally we demonstrate the key findings on a practical problem in an operational setting. For this, we leverage an example road network with approximately 18k nodes and 27k edges in a traffic control application, where we seek a limited number of k = 200 key intersections. This problem can be solved in a serial setting in just under 5 hours providing more than 2 orders of magnitude speed-up over methods that do not consider acceleration techniques.

关键词： Complex networks

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Dimensionality reduction of the criterion space in some optimization problems 2

Dimensionality reduction of the criterion space in some opti...

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2nd International Scientific Symposium "Intelligent Solutions", IntSol 2021

作者： Kondruk, Natalia Malyar, Mykola Uzhhorod National University Narodna Square 3 Uzhhorod88000 Ukraine

A characteristic feature of many practical decision-making tasks is their multicriteria, which leads to the complexity of information processing when finding a solution. Failure to consider many criteria can lead to inconsistency of the constructed model with the real problem and impossibility to find its solution. Therefore, improving the effectiveness of methods for solving such problems is an urgent research problem. This work is a continuation of research on the use of the clustering method based on fuzzy binary relations in solving different classes of applied problems. It is devoted to the urgent problem of increasing the efficiency of methods for solving multi-objective problems of linear programming by applying the approach of clustering the criterion space, identifying strongly dependent efficiency criteria, further reduction of the criterion space of the problem into a smaller centroid space. In this paper we analyzes the current state of research of multi-objective optimization problem. The clustering method based on fuzzy binary relations has been adapted to detect groups of vectors by angular similarity measure. At the same time, a new kind of membership function was proposed. The approach of criterion space reduction of the problem by the space of centroids is described. At the same time centroids no longer need normalization. Based on this approach, the weighted sum scalarization method in the selection of the decision maker who decides the weights of the efficiency criteria is modified. Some approaches for determining the weights of centroids representing the criteria in the respective clusters are proposed. The scheme of the complex approach to the decision of multi-objective linear optimization problem with use of the approach of criterion space reduction is presented. © 2021 Copyright for this paper by its authors.

关键词： Membership functions

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Solving forest harvesting problem with artificial intelligence approaches: An example in Taiwan

Solving forest harvesting problem with artificial intelligen...

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8th International multi-Conference on Engineering and Technology Innovation (IMETI)

作者： Hsieh, Yi-Chih You, Peng-Sheng Chuang, Hao-Chun Natl Formosa Univ Dept Ind Management Huwei Township Yunlin Taiwan Natl ChiaYi Univ Dept Business Adm Chiayi 600 Taiwan

In this paper, we study the forest harvesting problem (FHP). A forest is assumed to be divided into several identical square units, and each unit has its harvesting value based on its type. Harvesting a unit will affect the growth and values of its neighboring units. In this FHP, the best harvesting plan of a unit must be identified to maximize three various objectives simultaneously. The FHP is a multiobjective mathematical and an NP-hard problem. We apply three artificial intelligence algorithms, namely, immune algorithm, genetic algorithm, and particle swarm optimization, for maximizing the weighted objective to solve the FHP. We also solve the following two sets of test problems: (i) a set of randomly generated FHP problems and (ii) a practical problem in Taiwan. Numerical results show the performance of the three algorithms for the test problems. Finally, we compare and discuss the effects of various weights for the three objectives.

关键词： Forest harvesting problem optimization immune algorithm genetic algorithm particle swarm optimization

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A state-of-the-art review on the dynamic design of nonlinear energy sinks

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ENGINEERING STRUCTURES 2024年 313卷

作者： Geng, Xiao-Feng Ding, Hu Ji, Jin-Chen Wei, Ke-Xiang Jing, Xing-Jian Chen, Li-Qun Hunan Inst Engn Dept Mech Engn Xiangtan 411104 Hunan Peoples R China Shanghai Univ Shanghai Inst Appl Math & Mech Sch Mech & Engn Sci Shanghai Key Lab Mech Energy Engn Shanghai 200072 Peoples R China Univ Technol Sydney Sch Mech & Mechatron Engn Sydney NSW 2007 Australia City Univ Hong Kong Dept Mech Engn Hong Kong Peoples R China

Nonlinear energy sink (NES) is a type of vibration absorbers that does not have linear stiffness. Through establishing strongly nonlinear coupling between a primary system and a NES, the targeted energy transfer can be achieved from the primary system to the NES, thereby realizing vibration absorption in a wide frequency range. A significant amount of research work has been conducted on developing the NES for unidirectional vibration energy transfer over the last decade. More research is expected to develop further NESs to address various engineering vibration problems. Meanwhile, the question of whether NES can be practically applied to engineering is always being asked. The main objective of this paper is to review the research progress on dynamic design of NESs to promote the application of NESs to reduce engineering structure vibration. To do so, this paper first summarizes the characteristics of NESs, including vibration absorption mechanism, the threshold of targeted energy transfer, and strong nonlinearity characteristics. Then, dynamic designs of the NESs proposed in the literation are reviewed in terms of nonlinear stiffness design, mass design and damping design. Special attention is placed on the nonlinear stiffness design for NESs, including design principle, multi-stability design, track design, and magnetic design. The gaps between these design approaches and applications are explained. NES cells and their distributed vibration control strategy are also introduced. The research progress on the NES optimization design is also briefly discussed. Following the extensive review on NES dynamic design research, future promising research topics are recommended with an attempt to advancing the engineering application of NES. It is expected that this paper would help readers to understand the progress of NES dynamic design research and the future NES development for more practical applications.

关键词： Nonlinear energy sink Vibration absorption Passive vibration control Nonlinear design optimization design

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Breeding Particle Swarm optimization for Railways Rolling Stock Preventive Maintenance Scheduling

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American Journal of Operations Research 2021年第5期11卷 242-251页

作者： Tarek Aboueldah Hanan Farag Research and Development: Ministry of Transport Cairo Egypt Faculty of Graduate Studies for statistical Research Department: Management & Operations Research Cairo University Cairo Egypt

The goal of railway rolling stock maintenance and replacement approaches is to reduce overall cost while increasing reliability which is multi objective optimization problem and a proper predictive maintenance scheduling table should be adequately designed. We propose Breeding Particle Swarm Optimization (BPSO) model based on the concepts of Breeding Swarm and Genetic Algorithm (GA) operators to design this table. The practical experiment shows that our model reduces cost while increasing reliability compared to other models previously utilized.

关键词： Railways Rolling Stock Predictive Maintenance Scheduling Table multi objective optimization Problem Breeding Particle Swarm optimization

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A Mathematical Model and Two Fuzzy approaches Based on Credibility and Expected Interval for Project Cost-Quality-Risk Trade-Off Problem in Time-Constrained Conditions

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ALGORITHMS 2022年第7期15卷 226-226页

作者： Haghighi, Mohammad Hossein Mousavi, Seyed Meysam Shahed Univ Dept Ind Engn Tehran *** Iran

To successfully finalize projects and attain their determined purposes, it is indispensable to control all success criteria of a project. The time-cost trade-off (TCT) is known as a prevalent and efficient approach applied when the planned finish date of a project is not admitted by stakeholders, and consequently, the project duration must be decreased. This paper proposes a new mathematical model under fuzzy uncertainty to deal with the project cost-risk-quality trade-off problem (CRQT) under time constraints. Because of the unique nature of projects and their uncertain circumstances, applying crisp values for some project parameters does not seem appropriate. Hence, this paper employs fuzzy sets to resolve these weaknesses. In this study, two approaches are presented to handle proposed fuzzy multi-objective mathematical model. First, fuzzy credibility theory and then goal attainment method are used. Secondly, the model is solved by a fuzzy method based on expected interval and value and augmented e-constraint method. A project from the literature review is adopted and solved by the presented methodology. The results demonstrate the accuracy and efficiency of the two proposed approaches for the introduced practical problem.

关键词： time-cost trade-off (TCT) fuzzy credibility theory multi-objective optimization augmented e-constraint method goal attainment method

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