40 research outputs found

    Many-objectives optimization: a machine learning approach for reducing the number of objectives

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    Solving real-world multi-objective optimization problems using Multi-Objective Optimization Algorithms becomes difficult when the number of objectives is high since the types of algorithms generally used to solve these problems are based on the concept of non-dominance, which ceases to work as the number of objectives grows. This problem is known as the curse of dimensionality. Simultaneously, the existence of many objectives, a characteristic of practical optimization problems, makes choosing a solution to the problem very difficult. Different approaches are being used in the literature to reduce the number of objectives required for optimization. This work aims to propose a machine learning methodology, designated by FS-OPA, to tackle this problem. The proposed methodology was assessed using DTLZ benchmarks problems suggested in the literature and compared with similar algorithms, showing a good performance. In the end, the methodology was applied to a difficult real problem in polymer processing, showing its effectiveness. The algorithm proposed has some advantages when compared with a similar algorithm in the literature based on machine learning (NL-MVU-PCA), namely, the possibility for establishing variable–variable and objective–variable relations (not only objective–objective), and the elimination of the need to define/chose a kernel neither to optimize algorithm parameters. The collaboration with the DM(s) allows for the obtainment of explainable solutions.This research was funded by POR Norte under the PhD Grant PRT/BD/152192/2021. The authors also acknowledge the funding by FEDER funds through the COMPETE 2020 Programme and National Funds through FCT (Portuguese Foundation for Science and Technology) under the projects UIDB/05256/2020, and UIDP/05256/2020, the Center for Mathematical Sciences Applied to Industry (CeMEAI) and the support from the São Paulo Research Foundation (FAPESP grant No 2013/07375-0, the Center for Artificial Intelligence (C4AI-USP), the support from the São Paulo Research Foundation (FAPESP grant No 2019/07665-4) and the IBM Corporation

    Dynamic multi-objective optimization: a two archive strategy

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    Existing studies on dynamic multi-objective optimization mainly focus on dynamic problems with time-dependent objective functions. Few works have put efforts on dynamic problems with a changing number of objectives, or dynamic problems with time-dependent constraints. When problems have time-dependent objective functions, the shape or position of the Pareto-optimal front/set may change over time. However, when dealing with problems with a changing objective number or time-dependent constraints, the challenges are different. Changing number of objectives leads to the expansion or contraction of the dimensions of the Pareto-optimal front/set manifold, while time-dependent constraints may change the shape of feasible regions over time. The existing dynamic handling techniques can hardly handle the changing number of objectives. The state-of-arts in constraints handling techniques are incapable of tackling problems with time-dependent constraints. In this thesis, we present our attempts toward tackling 1) the dynamic multiobjective optimizing problems with a changing number of objectives and 2) multi-objective optimizing problems with time-dependent constraints. Two-archive Evolutionary Algorithms are proposed. Comprehensive experiments are conducted on various benchmark problems for both types of dynamics. Empirical results fully demonstrate the effectiveness of our proposed algorithms

    Quality-driven Multi-objective Optimization of Software Architecture Design: Method, Tool, and Application

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    Software architecting is a non-trivial and demanding task for software engineers to perform. The architecture is a key enabler for software systems. Besides being crucial for user functionality, the software architecture has deep impact on software qualities such as performance, safety, and cost. In this dissertation, an automated approach for software architecture design is proposed that supports analysis and optimization of multiple quality attributes:First of all, we demonstrate an optimization approach for automated software architecture design. It reports the results of applying our architecture optimization framework to an automotive sub-system that was conducted based on a large-scale real world case study. Moreover, we introduce two novel degrees of freedom which demonstrate how the number of processing nodes and their interconnecting network can be codified to fit into a genetic algorithm. Our studies show that these extra degrees of freedom lead to better overall software architecture optimization. Finally, we propose a new search-based approach for generating a set of optimal software architectural solutions for use in software product lines. Our new approach analyses the commonality of the found optimal solutions and proposes a set of solutions which are suitable for the range of products defined by various feature combinations.Algorithms and the Foundations of Software technolog

    A Polyhedral Study of Mixed 0-1 Set

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    We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

    Fahrplanbasiertes Energiemanagement in Smart Grids

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    Die Zunahme dezentraler, volatiler Stromerzeugung im Rahmen der Energiewende führt schon heute zu Engpässen in Stromnetzen. Eine Lösung dieser Probleme verspricht die informationstechnische Vernetzung und Koordination der Erzeuger und Verbraucher in Smart Grids. Diese Arbeit präsentiert einen Energiemanagement-Ansatz, der basierend auf Leistungsprognosen und Flexibilitäten der Akteure spezifische, aggregierte Leistungsprofile approximiert. Hierbei werden Netzrestriktionen berücksichtigt

    Fahrplanbasiertes Energiemanagement in Smart Grids

    Get PDF
    Die Zunahme dezentraler, volatiler Stromerzeugung im Rahmen der Energiewende führt schon heute zu Engpässen in Stromnetzen. Eine Lösung dieser Probleme verspricht die informationstechnische Vernetzung und Koordination der Erzeuger und Verbraucher in Smart Grids. Diese Arbeit präsentiert einen Energiemanagement-Ansatz, der basierend auf Leistungsprognosen und Flexibilitäten der Akteure spezifische, aggregierte Leistungsprofile approximiert. Hierbei werden Netzrestriktionen berücksichtigt

    A novel computer aided engineering method for comparative evaluation of nonlinear structures in the conceptual design phase

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    Selection of the preferred design concept during design represents a major challenge to design engineers as the required level of information and rigour to achieve an objective evaluation at early stage of design is typically not available. This is particularly evident during evaluation of design concepts of complex load-bearing mechanical structures. The engineering design concepts during concept design phase typically lack detail and more specific performance indicators to enable accurate evaluation. Hence in such cases, a prevailing evaluation approach is based primarily on qualitative scores inferred through personal intuition and historical experience of the design team or individual experts. The principal motivation behind this research is to improve the ability and confidence to select a superior design concept early in the design process. The conventional approach is sensitive to individual expertise and availability of experienced designers. Therefore, in order to make more informed decisions especially in case of complex engineering designs, the concept evaluation methods require more detailed and accurate information. This research is concerned with the development of a novel method for comparative evaluation of engineering design concepts that exhibit nonlinear structural behaviour under load. The approach is based on two key concepts: i) an expansion of the conventional substructuring technique into the nonlinear domain to enable FEA to be more applicable, effective and computationally affordable in early stages of the conceptual design phase; and ii) a restructuring of the traditional process by incorporating the optimisation search to provide orderly rule-guided evolution of design concepts in order to produce objective development metrics which alleviates the dependence on personal intuition and historical experience of the engineering designers. A series of experiments and validation case studies conducted in this research provide conclusive evidence that demonstrates the applicability and the significance of the developed method in terms of reduced time for evaluation and amount of recurrent knowledge generated compared to the more traditional approaches based on the application of FEA in the conceptual design phase. Furthermore, a Confidence Index as a performance measure is developed in this research to describe the quality of the obtained solutions. The derived Confidence Index is a novel contribution to the fields of metaheuristic measurements and engineering concept validation methodology
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