461 research outputs found

    A multi-objective evolutionary approach to simulation-based optimisation of real-world problems.

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    This thesis presents a novel evolutionary optimisation algorithm that can improve the quality of solutions in simulation-based optimisation. Simulation-based optimisation is the process of finding optimal parameter settings without explicitly examining each possible configuration of settings. An optimisation algorithm generates potential configurations and sends these to the simulation, which acts as an evaluation function. The evaluation results are used to refine the optimisation such that it eventually returns a high-quality solution. The algorithm described in this thesis integrates multi-objective optimisation, parallelism, surrogate usage, and noise handling in a unique way for dealing with simulation-based optimisation problems incurred by these characteristics. In order to handle multiple, conflicting optimisation objectives, the algorithm uses a Pareto approach in which the set of best trade-off solutions is searched for and presented to the user. The algorithm supports a high degree of parallelism by adopting an asynchronous master-slave parallelisation model in combination with an incremental population refinement strategy. A surrogate evaluation function is adopted in the algorithm to quickly identify promising candidate solutions and filter out poor ones. A novel technique based on inheritance is used to compensate for the uncertainties associated with the approximative surrogate evaluations. Furthermore, a novel technique for multi-objective problems that effectively reduces noise by adopting a dynamic procedure in resampling solutions is used to tackle the problem of real-world unpredictability (noise). The proposed algorithm is evaluated on benchmark problems and two complex real-world problems of manufacturing optimisation. The first real-world problem concerns the optimisation of a production cell at Volvo Aero, while the second one concerns the optimisation of a camshaft machining line at Volvo Cars Engine. The results from the optimisations show that the algorithm finds better solutions for all the problems considered than existing, similar algorithms. The new techniques for dealing with surrogate imprecision and noise used in the algorithm are identified as key reasons for the good performance.University of Skövde Knowledge Foundation Swede

    Incremental non-dominated sorting with O(N) insertion for the two-dimensional case.

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    Abstract-We propose a new algorithm for incremental nondominated sorting of two-dimensional points. The data structure which stores non-dominating layers is based on a tree of Cartesian trees. If there are N points in M layers, the running time for of an insertion is O(M (1 + log(N/M )) + log M log(N/ log M )), which is O(N ) in the worst case. This algorithm can be a basic building block for efficient implementations of steady-state multiobjective algorithms such as NSGA-II

    Development of an Algorithm for Multicriteria Optimization of Deep Learning Neural Networks

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    Nowadays, machine learning methods are actively used to process big data. A promising direction is neural networks, in which structure optimization occurs on the principles of self-configuration. Genetic algorithms are applied to solve this nontrivial problem. Most multicriteria evolutionary algorithms use a procedure known as non-dominant sorting to rank decisions. However, the efficiency of procedures for adding points and updating rank values in non-dominated sorting (incremental non-dominated sorting) remains low. In this regard, this research improves the performance of these algorithms, including the condition of an asynchronous calculation of the fitness of individuals. The relevance of the research is determined by the fact that although many scholars and specialists have studied the self-tuning of neural networks, they have not yet proposed a comprehensive solution to this problem. In particular, algorithms for efficient non-dominated sorting under conditions of incremental and asynchronous updates when using evolutionary methods of multicriteria optimization have not been fully developed to date. To achieve this goal, a hybrid co-evolutionary algorithm was developed that significantly outperforms all algorithms included in it, including error-back propagation and genetic algorithms that operate separately. The novelty of the obtained results lies in the fact that the developed algorithms have minimal asymptotic complexity. The practical value of the developed algorithms is associated with the fact that they make it possible to solve applied problems of increased complexity in a practically acceptable time. Doi: 10.28991/HIJ-2023-04-01-011 Full Text: PD

    A stopping criterion for multi-objective optimization evolutionary algorithms

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    This Paper Puts Forward A Comprehensive Study Of The Design Of Global Stopping Criteria For Multi-Objective Optimization. In This Study We Propose A Global Stopping Criterion, Which Is Terms As Mgbm After The Authors Surnames. Mgbm Combines A Novel Progress Indicator, Called Mutual Domination Rate (Mdr) Indicator, With A Simplified Kalman Filter, Which Is Used For Evidence-Gathering Purposes. The Mdr Indicator, Which Is Also Introduced, Is A Special-Purpose Progress Indicator Designed For The Purpose Of Stopping A Multi-Objective Optimization. As Part Of The Paper We Describe The Criterion From A Theoretical Perspective And Examine Its Performance On A Number Of Test Problems. We Also Compare This Method With Similar Approaches To The Issue. The Results Of These Experiments Suggest That Mgbm Is A Valid And Accurate Approach. (C) 2016 Elsevier Inc. All Rights Reserved.This work was funded in part by CNPq BJT Project 407851/2012-7 and CNPq PVE Project 314017/2013-

    Simulation-Based Evolutionary Optimization of Air Traffic Management

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    In the context of aerospace engineering, the optimization of processes may often require to solve multi-objective optimization problems, including mixed variables, multi-modal and non-differentiable quantities, possibly involving highly-expensive objective function evaluations. In Air Traffic Management (ATM), the optimization of procedures and protocols becomes even more complicated, due to the involve-ment of human controllers, which act as final decision points in the control chain. In this article, we propose the use of computational intelligence techniques, such as Agent-Based Modelling and Simulation (ABMS)and Evolutionary Computing (EC), to design a simulation-based distributed architecture to optimize control plans and procedures in the context of ATM. We rely on Agent-Based fast-time simulations to carry out offline what-if analysis of multiple scenarios, also taking into account human-related decisions, during the strategic or pre-tactical phases. The scenarios are constructed using real-world traffic data traces, while multiple optimization variables governed by an EC algorithm allow to explore the search space to identify the best solutions. Our optimization approach relies on ad-hoc multi-objective performance metrics which allow to assess the goodness of the control of aircraft and air traffic regulations. We present experimental results which prove the viability of our approach, comparing them with real-world data traces, and proving their meaningfulness from an Air Traffic Control perspective

    Performance Counter Measurements of Data Structures: Implementations for Multi-Objective Optimisation

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    Solving multi-objective optimisation problems using evolutionary computation methods involve the implementation of algorithms and data structures for the storage of tempo- rary solutions. Computational efficiency of these systems becomes important as problems increase in complexity and the number of solutions maintained becomes large. Many data structures and algorithms have been proposed looking to decrease computa- tional times. The effectiveness of a data structure/algorithm can be characterised using wall-clock time. This is a widely used parameter in the literature, however it is strongly dependent on the underlying computer architecture and hence not a reliable measure of absolute performance. A commonly used approach to avoid architectural dependencies is to compare the performance of the data structure being evaluated to the equivalent implementation using a linked list. Modern processors offer built-in hardware performance counters, giving access to a wide set of parameters that can be used to explore performance. In this dissertation we study the efficiency of a non-dominated quad-tree data structure in combination with different evolutionary algorithms using hardware performance counters. We also compare the re- sults for the quad-tree data structure to a linked list as it is the standard practice, however we find non-scalable hardware dependencies might appear

    Design methodologies and architectures of hardware-based evolutionary algorithms for aerospace optimisation applications on FPGAS

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    This thesis is a study of new design methods for allowing evolutionary algorithms to be more effectively utilised in aerospace optimisation applications where computation needs are high and computation platform space may be restrictive. It examines the applicability of special hardware computational platforms known as field programmable gate arrays and shows that with the right implementation methods they can offer significant benefits. This research is a step forward towards the advancement of efficient and highly automated aircraft systems for meeting compact physical constraints in aerospace platforms and providing effective performance speedups over traditional methods

    Fuzzy-Analysis in a Generic Polymorphic Uncertainty Quantification Framework

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    In this thesis, a framework for generic uncertainty analysis is developed. The two basic uncertainty characteristics aleatoric and epistemic uncertainty are differentiated. Polymorphic uncertainty as the combination of these two characteristics is discussed. The main focus is on epistemic uncertainty, with fuzziness as an uncertainty model. Properties and classes of fuzzy quantities are discussed. Some information reduction measures to reduce a fuzzy quantity to a characteristic value, are briefly debated. Analysis approaches for aleatoric, epistemic and polymorphic uncertainty are discussed. For fuzzy analysis α-level-based and α-level-free methods are described. As a hybridization of both methods, non-flat α-level-optimization is proposed. For numerical uncertainty analysis, the framework PUQpy, which stands for “Polymorphic Uncertainty Quantification in Python” is introduced. The conception, structure, data structure, modules and design principles of PUQpy are documented. Sequential Weighted Sampling (SWS) is presented as an optimization algorithm for general purpose optimization, as well as for fuzzy analysis. Slice Sampling as a component of SWS is shown. Routines to update Pareto-fronts, which are required for optimization are benchmarked. Finally, PUQpy is used to analyze example problems as a proof of concept. In those problems analytical functions with uncertain parameters, characterized by fuzzy and polymorphic uncertainty, are examined

    Optimization of Thermo-mechanical Conditions in Friction Stir Welding

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