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检索条件"主题词=multi-objective evolutionary algorithms"

共 320 条记录，以下是41-50 订阅

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A comparison between dynamic weighted aggregation and NSGA-II for multi-objective evolutionary algorithms

A comparison between dynamic weighted aggregation and NSGA-I...

引用

IASTED International Conference on Computational Intelligence

作者： Drzadzewski, G Wineberg, M Univ Guelph Guelph ON N1G 2W1 Canada

ISBN: (纸本)0889864799

The performance of the Dynamic Weight Aggregation system as applied to a Genetic Algorithm (DWAGA) and NSGA-II are evaluated and compared against each other. The algorithms are run on 11 test functions. The performance of the algorithms is evaluated by examining the spacing, diversity and coverage of the Pareto front, as well as each algorithm's execution time. It is discovered that, while the NSGA-II performs better on most of the test functions, the DWAGA can outperform the NSGA-II on some of the functions, including a concave one.

关键词： multi-objective evolutionary algorithms genetic algorithms

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Applying multi-objective evolutionary algorithms to QoS-Aware Web Service Composition

Applying Multi-Objective Evolutionary Algorithms to QoS-Awar...

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6th International Conference on Advanced Data Mining and Applications (ADMA)

作者： Li, Li Cheng, Peng Ou, Ling Zhang, Zili Southwest Univ Chongqing Peoples R China

ISBN: (纸本)9783642173127

Finding optimal solutions for QoS-aware Web service composition with conflicting objectives and various restrictions on quality matrices is a NP-hard problem. This paper proposes the use of multi-objective evolutionary algorithms (MOEAs for short) for QoS-aware service composition optimisation. More specifically, SPEA2 is introduced to achieve the goal. The algorithm is good at dealing with multi-objective combinational optimisation problems. Experimental results reveal that SPEA2 is able to approach the Pareto-optimal front with well spread distribution. The Pareto front approximations provide different trade-offs, from which the end-users may select the better one based on their preference.

关键词： multi-objective evolutionary algorithms Service composition QoS Pareto front

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Pareto Rank Learning in multi-objective evolutionary algorithms

Pareto Rank Learning in Multi-objective Evolutionary Algorit...

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IEEE Congress on evolutionary Computation (CEC)

作者： Seah, Chun-Wei Ong, Yew-Soon Tsang, Ivor W. Jiang, Siwei Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

ISBN: (纸本)9781467315098

In this paper, the interest is on cases where assessing the goodness of a solution for the problem is costly or hazardous to construct or extremely computationally intensive to compute. We label such category of problems as "expensive" in the present study. In the context of multi-objective evolutionary optimizations, the challenge amplifies, since multiple criteria assessments, each defined by an "expensive" objective is necessary and it is desirable to obtain the Pareto-optimal solution set under a limited resource budget. To address this issue, we propose a Pareto Rank Learning scheme that predicts the Pareto front rank of the offspring in MOEAs, in place of the "expensive" objectives when assessing the population of solutions. Experimental study on 19 standard multi-objective benchmark test problems concludes that Pareto rank learning enhanced MOEA led to significant speedup over the state-of-the-art NSGA-II, MOEA/D and SPEA2.

关键词： multi-objective evolutionary algorithms Expensive Problems Pareto Rank Learning

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Comparison of Single and multi-objective evolutionary algorithms for Robust Link-State Routing 8

Comparison of Single and Multi-objective Evolutionary Algori...

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8th International Conference on evolutionary multi-Criterion Optimization (EMO)

作者： Pereira, Vitor Sousa, Pedro Cortez, Paulo Rio, Miguel Rocha, Miguel Univ Minho Ctr Algoritmi Dept Informat Braga Portugal Univ Minho Ctr Algoritmi Dept Informat Syst Braga Portugal UCL Dept Elect & Elect Engn London England Univ Minho Ctr Biol Engn Dept Informat Braga Portugal

ISBN: (纸本)9783319158921;9783319158914

Traffic Engineering (TE) approaches are increasingly important in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of evolutionary algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to minimize network congestion. In both tasks, the optimization considers scenarios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came naturally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.

关键词： multi-objective evolutionary algorithms Traffic engineering NSGA SPEA Intra-domain routing OSPF

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Fault Tree Inference Using multi-objective evolutionary algorithms and Confusion Matrix-Based Metrics 29th

Fault Tree Inference Using Multi-objective Evolutionary Algo...

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29th International Conference on Formal Methods for Industrial Critical Systems (FMICS)

作者： Jimenez-Roa, Lisandro A. Rusnac, Nicolae Volk, Matthias Stoelinga, Marielle Univ Twente Formal Methods & Tools Enschede Netherlands Delft Univ Technol Delft Netherlands Eindhoven Univ Technol Formal Syst Anal Eindhoven Netherlands Radboud Univ Nijmegen Dept Software Sci Nijmegen Netherlands

ISBN: (纸本)9783031681493;9783031681509

In the domain of reliability engineering and risk assessment, the development of fault tree (FT) models is pivotal for decision-making in complex systems. Traditional FT model development, relying on manual efforts and expert collaboration, is both time-consuming and error-prone. The era of Industry 4.0 introduces capabilities for automatically deriving FTs from inspection and monitoring data. This paper presents FT-MOEA-CM, an extension of the FT-MOEA algorithm for inferring FT models from failure data using multi-objective optimization. FT-MOEA-CM enhances its predecessor by integrating confusion matrix-derived metrics and incorporating parallelization and caching mechanisms. Our evaluation on six FTs from diverse application areas showcases that FT-MOEA-CM exhibits (1) enhanced robustness, (2) faster convergence and (3) better scalability than FT-MOEA, suggesting its potential in efficiently inferring larger FT models.

关键词： Fault tree analysis multi-objective evolutionary algorithms confusion matrix model learning

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An Effective Ensemble-Based Method for Creating On-the-Fly Surrogate Fitness Functions for multi-objective evolutionary algorithms

An Effective Ensemble-Based Method for Creating On-the-Fly S...

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15th International Symposium on Symbolic and Numeric algorithms for Scientific Computing (SYNASC)

作者： Zavoianu, Alexandru-Ciprian Lughofer, Edwin Bramerdorfer, Gerd Amrhein, Wolfgang Klement, Erich Peter Johannes Kepler Univ Linz Fuzzy Log Lab Linz Hagenberg Dept Knowledge Based Math Syst Linz Austria Johannes Kepler Univ Linz Inst Elect Drives & Power Elect Linz Austria Linz Ctr Mechatron GmbH LCM Linz Austria

ISBN: (纸本)9781479930357

The task of designing electrical drives is a multi-objective optimization problem (MOOP) that remains very slow even when using state-of-the-art approaches like particle swarm optimization and evolutionary algorithms because the fitness function used to assess the quality of a proposed design is based on time-intensive finite element (FE) simulations. One straightforward solution is to replace the original FE-based fitness function with a much faster-to-evaluate surrogate. In our particular case each optimization scenario poses rather unique challenges (i.e., goals and constraints) and the surrogate models need to be constructed on-the-fly, automatically, during the run of the evolutionary algorithm. In the present research, using three industrial MOOPs, we investigated several approaches for creating such surrogate models and discovered that a strategy that uses ensembles of multi-layer perceptron neural networks and Pareto-trimmed training sets is able to produce very high-quality surrogate models in a relatively short time interval.

关键词： multi-objective evolutionary algorithms surrogate fitness evaluation artificial neural networks ensemble regression models

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Combining multi-objective evolutionary algorithms with Deep Generative Models Towards Focused Molecular Design 24th

Combining Multi-objective Evolutionary Algorithms with Deep ...

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24th International Conference on the Applications of evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Sousa, Tiago Correia, Joao Pereira, Vitor Rocha, Miguel Univ Minho Ctr Biol Engn Campus Gualtar Braga Portugal

ISBN: (纸本)9783030726980;9783030726997

Recent advances in applying deep generative learning to molecular design have led to a large number of novel approaches to the targeted generation of molecules towards specific features and applications. In this work, we expand on the latent space navigation approach, where molecules are optimized by operating in their latent representation inside a deep auto-encoder, by introducing multi-objective evolutionary algorithms (MOEAs), and benchmarking the proposed framework on several objectives from recent literature. Using several case studies from literature, we show that our proposed method is capable of controlling abstract chemical properties, is competitive with other state-of-the-art methods and can perform relevant tasks such as optimizing a predefined molecule while maintaining a similarity threshold. Also, MOEAs allow to generate molecules with a good level of diversity, which is a desired feature.

关键词： Molecular design Deep generative models multi-objective evolutionary algorithms

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Fast Antenna Design Using multi-objective evolutionary algorithms and Artificial Neural Networks 12

Fast Antenna Design Using Multi-Objective Evolutionary Algor...

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12th International Symposium on Antennas, Propagation and Electromagnetic Theory (ISAPE)

作者： Qin, Wenwen Dong, Jian Wang, Meng Li, Yingjuan Wang, Shan Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

ISBN: (纸本)9781538673027

Aiming at reducing the large computation cost of traditional EM-driven antenna design methods, surrogate models based on back propagation neural networks (BPNN) are studied. In order to solve the problem of easily falling into local optimum in BPNN, a PSO-BPNN surrogate model is developed by improving initial structural parameters of neural networks and applied to fast multi-objective optimization design of multi-parameter antenna structures. Design results show that the proposed PSO-BPNN surrogate model can be integrating into multi-objective evolutionary algorithms for dealing with complex antenna designs with high-dimensional parameter space.

关键词： antenna design multi-objective evolutionary algorithms neural networks back propagation particle swarm optimization

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Supplier Selection and Order Allocation Under Disruption: multi-objective evolutionary algorithms

Supplier Selection and Order Allocation Under Disruption: Mu...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Gargari, F. Javadi Pourjavad, E. Alzahra Univ Tech & Engn Dept Tehran Iran Polytech Montreal Dept Math & Ind Engn Montreal PQ Canada

ISBN: (纸本)9781538672204

Disruption is one of the critical issues that affect the performance and costs of supply chain management. The appropriate adjusting of supply chain disruptions is considered as a competitive privilege for companies. Hence, this paper aims to improve an optimization approach to select suppliers and allocate the proper quota of order to each one considering supplier disruption. A multi-objective Mixed Integer Linear Programming (MOMILP) is proposed model with five objective functions, minimize costs of the transaction and supplying, the percentage of delayed products, and the percentage of returned products, as well as maximize capabilities of orders tracking by customers. Strength Pareto evolutionary Algorithm-II (SPEA-II) and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) are developed to settle this problem. The efficiency of the solution algorithms is investigated based on four criteria for eight computational experiments. The results indicate the SPEA-II algorithm provides better solutions in comparison with the NSGA-II algorithm.

关键词： Disruption multi-objective evolutionary algorithms Operation research Quota allocation Supply chain management Supplier selection

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Interpreting multi-objective evolutionary algorithms via Sokoban Level Generation 6

Interpreting Multi-objective Evolutionary Algorithms via Sok...

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6th Annual IEEE Conference on Games (CoG)

作者： Zhang, Qingquan Li, Yuchen Lin, Yuhang Wang, Handing Liu, Jialin Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Key Lab Brain Inspired Intelligent Comp Shenzhen Peoples R China Xidian Univ Sch Artificial Intelligence Xian Peoples R China

ISBN: (纸本)9798350350685;9798350350678

This paper presents an interactive platform to interpret multi-objective evolutionary algorithms. Sokoban level generation is selected as a showcase for its widespread use in procedural content generation. By balancing the emptiness and spatial diversity of Sokoban levels, we illustrate the improved two-archive algorithm, Two_Arch2, a well-known multi-objective evolutionary algorithm. Our web-based platform integrates Two_Arch2 into an interface that visually and interactively demonstrates the evolutionary process in real-time. Designed to bridge theoretical optimisation strategies with practical game generation applications, the interface is also accessible to both researchers and beginners to multi-objective evolutionary algorithms or procedural content generation on a website. Through dynamic visualisations and interactive gameplay demonstrations, this web-based platform also has potential as an educational tool.

关键词： Procedural Content Generation multi-objective Optimisation multi-objective evolutionary algorithms Two_Arch2

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