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

共 320 条记录，以下是281-290 订阅

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Hybrid multi-objective Greenhouse Crop Optimization using the Parallel Island Model

Hybrid Multi-Objective Greenhouse Crop Optimization using th...

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7th International Conference on Engineering Computational Technology

作者： Marquez, A. L. Gil, C. Manzano-Agugliaro, F. Montoya, F. G. Montoya, M. G. Banos, R. Univ Almeria Dept Comp Architecture & Elect La Canada De San Urbano Almeria Spain Univ Almeria Dept Rural Engn La Canada De San Urbano Almeria Spain

ISBN: (纸本)9781905088416

multi-objective evolutionary algorithms are commonly used to solve complex problems. It is possible to improve the quality of the solutions they can produce by means of employing advanced parallelization techniques. This paper solves a land management problem by using the Island Parallel Model with the purpose of expanding the number of objectives to optimize by using several isolated populations exploring two dimensional search spaces and migrating the best subjects to nearby populations that are exploring a search space with a different objective. The goal is to fuel the generation of solutions that are optimal in both search spaces to generate a three objective pareto front that is the solution of a larger problem.

关键词： multi-objective evolutionary algorithms island parallel model threads greenhouse crops resource optimization

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Collaborative multi-objective Optimization for Distributed Design of Complex Products 18

Collaborative Multi-Objective Optimization for Distributed D...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Duro, Joao A. Yan, Yiming Purshouse, Robin C. Fleming, Peter J. Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England

ISBN: (纸本)9781450356183

multidisciplinary design optimization problems with competing objectives that involve several interacting components can be called complex systems. Nowadays, it is common to partition the optimization problem of a complex system into smaller subsystems, each with a subproblem, in part because it is too difficult to deal with the problem all-at-once. Such an approach is suitable for large organisations where each subsystem can have its own (specialised) design team. However, this requires a design process that facilitates collaboration, and decision making, in an environment where teams may exchange limited information about their own designs, and also where the design teams work at different rates, have different time schedules, and are normally not co-located. A multi-objective optimization methodology to address these features is described. Subsystems exchange information about their own optimal solutions on a peer-to-peer basis, and the methodology enables convergence to a set of optimal solutions that satisfy the overall system. This is demonstrated on an example problem where the methodology is shown to perform as well as the ideal, but "unrealistic" approach, that treats the optimization problem all-at-once.

关键词： Collaborative multidisciplinary optimization Complex systems multi-objective evolutionary algorithms multiple-criteria decision-making

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NSGA-II: Implementation and Performance Metrics Extraction for CPU and GPU 16

NSGA-II: Implementation and Performance Metrics Extraction f...

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16th International Symposium on Symbolic and Numeric algorithms for Scientific Computing (SYNASC)

作者： Padurariu, Florina Roxana Marinescu, Cristina Politehn Univ Timisoara Timisoara Romania West Univ Timisoara Timisoara Romania

ISBN: (纸本)9781479984480

multi-objective Optimization evolutionary algorithms are widely employed for solving different real-world optimization problems. Usually their runs involve a considerable amount of time because of the need to evaluate many functions. This particularity makes them good candidates of parallelization. In this work we investigate the benefits of the GPU implementation of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) versus its CPU implementation in terms of the execution time.

关键词： multi-objective evolutionary algorithms GPU CPU empirical software engineering

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SUSTAINABLE CLOSED-LOOP SUPPLY CHAIN NETWORK DESIGN

SUSTAINABLE CLOSED-LOOP SUPPLY CHAIN NETWORK DESIGN

引用

作者： Ehsan Pourjavad University of Regina

学位级别：博士

Sustainable Closed Loop Supply Chain (CLSC) management is increasingly adopted by companies, due to increasing concerns for the environmental, legislative compliance, decreasing availability of raw materials, and customer demands for environmentally friendly products. Sustainable CLSC network design provides a platform which ensures an effective and efficient supply chain management. In this thesis, the sustainable CLSC network design problem was formulated deterministically via Mixed Integer Linear Programming (MILP), and non- deterministically via Fuzzy multi-objective Mixed Integer Linear Programming (FMOMILP) model, by considering sustainability and uncertainty. Fuzzy programming approaches were utilized to solve the problem. Two multi-objective evolutionary algorithms were employed to find the optimal solutions for large cases. Computational experiments were conducted, as well as studying actual industrial cases, to illustrate the applicability and significance of the proposed approaches and solution methods. Results showed that the Fuzzy Programming approach presents a systematic framework that enables management to obtain a satisfactory solution by adjusting the search direction. The results also demonstrated that the adopted Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is a satisfactory technique to solve large scale sustainable CLSC network design problems.

关键词： Closed Loop Supply Chain Sustainability Uncertainty Fuzzy multi-objective evolutionary algorithms

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Unsupervised text feature selection using NSGA II with Hill Climbing local search 27

Unsupervised text feature selection using NSGA II with Hill ...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Cornei, Laura Croitoru, Eugen Luchian, Henri Faculty of Computer Science Alexandru Ioan Cuza University 16 General Berthelot St. Iasi700483 Romania

This paper introduces a novel unsupervised text feature selection technique that combines the multi-objective evolutionary algorithm NSGA II with a local Hill Climbing based search. The objective functions in NSGA II are an adapted version of the Mean Absolute Difference criterion and the Holes projection pursuit index, both of which incorporate information regarding the cardinality of the selected feature subset. The local Hill Climbing procedure aims to further improve the solutions provided by NSGA II through elimination/replacement of redundant features. The redundancy level of a feature F in a chromosome is measured via the lexical and semantic similarities between F and the other features represented in the chromosome. Experiments conducted on three real-world textual subdatasets indicate that our proposed approach achieves very good results in comparison to four state-of-the-art evolutionary unsupervised feature selection techniques and to one classical feature extraction method with proven effectiveness (Principal Component Analysis). © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Hill Climbing multi-objective evolutionary algorithms NLP NSGA-II semantic similarity unsupervised text feature selection

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Adaptive Pareto differential evolution and its parallelization

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5th International Conference on Parallel Processing and Applied Mathematics

作者： Zaharie, D Petcu, D W Univ Timisoara Dept Comp Sci Timisoara 300223 Romania

ISBN: (纸本)3540219463

An adaptive Pareto differential evolution algorithm for multi-objective optimization is proposed. Its effectiveness on approximating the Pareto front is compared with that of SPEA[9] and of SPDE[2]. A parallel implementation, based on an island model with a random connection topology, is also analyzed. The parallelization efficiency derives from the simple migration strategy. Numerical tests were performed on a cluster of workstations.

关键词： multi-objective evolutionary algorithms Pareto front parameter adaptation island model parallel computing PC cluster

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Innovization for Route Planning Applied to an Uber Movement Speeds Dataset for Berlin 18th

Innovization for Route Planning Applied to an Uber Movement ...

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18th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Roeper, Eva Weise, Jens Steup, Christoph Mostaghim, Sanaz Otto von Guericke Univ Fac Comp Sci Magdeburg Germany Agentur Innovat Cybersicherheit GmbH Halle Germany Fraunhofer Inst Transportat & Infrastruct Syst IV Dresden Germany

ISBN: (纸本)9783031700842;9783031700859

multi-objective route planning is a prominent but computationally expensive optimisation problem of everyday life. Reusing knowledge from similar route planning problems could enhance the performance and the sustainability of routing algorithms. The goal of this paper is to adapt the concept of innovization to route planning and in this way extract knowledge from Pareto-optimal solutions. As part of the adaptation, we design a multi-objective evolutionary algorithm for routing and introduce a novel local search for routing problems called Perimeter Mutation Local Search. We evaluate our proposed approach on multi-objective time-dependent routing problems to see what knowledge can be gained and whether this knowledge can improve a multi-objective evolutionary algorithm. Our results show that we can extract knowledge using the introduced innovization for route planning. This knowledge is used to improve a multiobjective evolutionary algorithm by reducing computational effort. With only about 40 % of previously necessary function evaluations, we manage to produce similar optimisation results. This is particularly beneficial for mobile applications with limited available computational resources.

关键词： Innovization Time-Dependent Route Planning multi-objective evolutionary algorithms

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Distributed NSGA-II Sharing Extreme Non-dominated Solutions for Improving Accuracy and Achieving Speed-up

Distributed NSGA-II Sharing Extreme Non-dominated Solutions ...

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Sato, Yuji Sato, Mikiko Miyakawa, Minami Hosei Univ Fac Comp & Informat Sci Tokyo Japan Tokai Univ Sch Informat & Telecommun Engn Tokyo Japan Sinshu Univ Fac Engn Dept Elect & Comp Engn Nagano Japan

ISBN: (纸本)9781728121536

A recent trend of multi-objective evolutionary algorithms is to approximate the Pareto optimal front with high accuracy by increasing the size of the population. On the other hand, the cconventional method NSGA-II algorithm widely used in multi-objective optimization ranks solutions using non-dominant sorting that increase in computational complexity proportional to the population size squared. In engineering applications, the increased execution time can be a problem. Also, even if standard techniques for parallel processing of genetic manipulation are applied to multi-objective evolutionary computation, it is difficult to speed up while maintaining the accuracy of the solution search. This paper proposes the distributed NSGA-II which performs a hierarchical non-dominated sorting in a many-core environment, and migration that shares the extreme solutions of the latest Pareto optimal solutions among all cores. In the evaluation, using the test functions provided by the NSGA-II source code and the constrained knapsack problem with two-objective and three-objective, we describe that our proposed method contributes to the improvement of solution diversity search and for enhancing solution accuracy and speeding up.

关键词： NSGA-II non-dominated sorting parallel and distributed processing many-core environment multi-objective evolutionary algorithms

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Parallel and Distributed MOEA/D with Exclusively Evaluated Mating and Migration

Parallel and Distributed MOEA/D with Exclusively Evaluated M...

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IEEE Congress on evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Sato, Yuji Sato, Mikiko Midtlyng, Mads Miyakawa, Minami Hosei Univ Fac Comp & Informat Sci Tokyo Japan Tokai Univ Sch Informat & Telecommun Engn Tokyo Japan Hosei Univ Grad Sch Comp & Informat Sci Tokyo Japan Sinshu Univ Dept Elect Informat Syst Engn Nagano Japan

ISBN: (纸本)9781728169293

This paper proposes a method for many-core-based large-scale parallel and distributed computation of MOEA/D, a decomposition-based evolutionary multi-objective optimization algorithm. Standard parallel MOEA/D on many-core environments provides fast execution time, but uniformity and diversity of the Pareto front may be lost. To avoid this problem, we propose a method of defining a virtual overlapping zone between partitions and selecting individuals for mating and migration by evaluating individual populations in this area using weight vectors of adjacent partitions. Using a two-objective constrained knapsack problem for evaluation, we compare the proposed method with standard single-core execution, no-migration parallel MOEA/D, and parallel MOEA/D with standard migration, and show that the proposed method is effective in improving diversity in solution searching while shortening execution time and increasing the accuracy of solution searching.

关键词： MOEA/D parallel and distributed processing many-core environment multi-objective evolutionary algorithms

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multi-objective Robust Static Mapping of Independent Tasks on Grids

Multi-objective Robust Static Mapping of Independent Tasks o...

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2010 IEEE World Congress on Computational Intelligence

作者： Dorronsoro, Bernabe Bouvry, Pascal Canero, J. Alberto Maciejewski, Anthony A. Siegel, Howard Jay Univ Luxembourg Fac Sci & Commun Luxembourg Luxembourg Colorado State Univ Dept ECE Ft Collins CO USA Colorado State Univ Dept CS Ft Collins CO 80523 USA

ISBN: (纸本)9781424481262

We study the problem of efficiently allocating incoming independent tasks onto the resources of a Grid system. Typically, it is assumed that the estimated time to compute each task on every machine is known. We are making the same assumption in this work, but we allow the existence of inaccuracies in these values. Our schedule will be robust versus such inaccuracies, ensuring that even when the estimated time to compute all the tasks is increased by a given percentage, the makespan of the schedule (i.e., the time when the last machine finishes its tasks) will not grow behind that percentage. We propose a new multi-objective definition of the problem, optimizing at the same time the makespan of the schedule and its robustness. Four well-known multi-objective evolutionary algorithms are used to find competitive results to the new problem. Finally, a new population initialization method for scheduling problems is proposed, leading to more efficient and accurate algorithms.

关键词： evolutionary computation grid computing grid system independent tasks multi-objective evolutionary algorithms multi-objective robust static mapping task analysis

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