Filters








391 Hits in 4.5 sec

Multi-Objective Optimization Using Surrogates [chapter]

Ivan Voutchkov, Andy Keane
2010 Computational Intelligence in Optimization  
surrogate assisted multiobjective search.  ...  This allows, for example, efficient fusion of surrogates and finite element models into a multiobjective optimization cycle.  ...  However it must be underlined that a correct selection is problem dependant and must be selected with care and understanding.  ... 
doi:10.1007/978-3-642-12775-5_7 fatcat:ncmklpyuhzezfdevmhj2balyre

Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case

Richard Allmendinger, Michael T. M. Emmerich, Jussi Hakanen, Yaochu Jin, Enrico Rigoni
2017 Journal of Multi-Criteria Decision Analysis  
Finally, we provide insights from an industrial point of view into how surrogate-assisted multicriteria optimization techniques can be developed and applied within a collaborative business environment  ...  solutions to a problem.  ...  Acknowledgements This paper is a product of discussions initiated in the Dagstuhl Seminar 15031: Understanding Complexity in Multiobjective Optimization. The authors acknowledge Prof. Carlos A.  ... 
doi:10.1002/mcda.1605 fatcat:5ew65lket5azppoo4uw546wdna

Surrogate-assisted evolutionary computation: Recent advances and future challenges

Yaochu Jin
2011 Swarm and Evolutionary Computation  
This paper provides a concise overview of the history and recent developments in surrogate-assisted evolutionary computation and suggests a few future trends in this research area.  ...  Research on surrogate-assisted evolutionary computation began over a decade ago and has received considerably increasing interest in recent years.  ...  Fig. 3 . 3 Two individual-based model management strategies. (a) Best strategy, and (b) Pre-selection strategy.  ... 
doi:10.1016/j.swevo.2011.05.001 fatcat:z3f6vey24fawbmz6gkpcjbnq5q

A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization [article]

Songbai Liu
2022 arXiv   pre-print
However, these progressively improved MOEAs have not necessarily been equipped with sophisticatedly scalable and learnable problem-solving strategies that are able to cope with new and grand challenges  ...  for environmental selection, learnable evolutionary generators for reproduction, and learnable evolutionary transfer for sharing or reusing optimization experience between different problem domains).  ...  Learnable Multiobjective Evolutionary Algorithms Since the evolutionary generator and discriminator of a classic MOEA are developed based on fixed genetic operators (e.g., crossover, mutation, and selection  ... 
arXiv:2206.11526v1 fatcat:mlgcvsi4sfadflm3xxfkbfjk6q

Evolutionary Approaches to Expensive Optimisation

Maumita Bhattacharya
2013 International Journal of Advanced Research in Artificial Intelligence (IJARAI)  
Use of approximate model or surrogates provides a much cheaper option. However, naturally this cheaper option comes with its own price!  ...  This paper discusses some of the key issues involved with use of approximation in evolutionary algorithm, possible best practices and solutions.  ...  INTRODUCTION Evolutionary algorithms (EAs) have long been accepted as powerful search algorithms, with numerous applications in various science and engineering problem domains.  ... 
doi:10.14569/ijarai.2013.020308 fatcat:gfblyrwl3ndpbgx4pdyn7obfgi

Multiobjective Stochastic Optimization of Dividing-wall Distillation Columns Using a Surrogate Model Based on Neural Networks
english

C. Gutiérrez-Antonio
2016 Chemical and Biochemical Engineering Quarterly  
In this work, we propose the optimization of dividing-wall columns, with a multiobjective genetic algorithm, through the use of neural networks as surrogate models.  ...  Surrogate models have been used for modelling and optimization of conventional chemical processes; among them, neural networks have a great potential to capture complex problems such as those found in  ...  In 2007, Zhou et al. 12 developed a strategy that combines global and local surrogate models to accelerate evolutionary optimization.  ... 
doi:10.15255/cabeq.2014.2132 fatcat:ji56trwg5bbzroh2vgxfsoepsy

A tutorial on multiobjective optimization: fundamentals and evolutionary methods

Michael T. M. Emmerich, André H. Deutz
2018 Natural Computing  
The idea of using a population of search agents that collectively approximate the Pareto front resonates well with processes in natural evolution, immune systems, and swarm intelligence.  ...  Keywords Multiobjective optimization Á Multiobjective evolutionary algorithms Á Decomposition-based MOEAs Á Indicator-based MOEAs Á Pareto-based MOEAs Á Performance assessment & Michael T. M.  ...  reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.  ... 
doi:10.1007/s11047-018-9685-y pmid:30174562 pmcid:PMC6105305 fatcat:7ienhqr3jzheth6a3y7ycvtt4q

Surrogate-assisted multiobjective optimization based on decomposition

Nicolas Berveglieri, Bilel Derbel, Arnaud Liefooghe, Hernán Aguirre, Kiyoshi Tanaka
2019 Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO '19  
A number of surrogate-assisted evolutionary algorithms are being developed for tackling expensive multiobjective optimization problems.  ...  More importantly, we provide a comprehensive comparative study of a representative selection of state-of-the-art methods, together with simple baseline algorithms.  ...  A local surrogate for the target search direction is then built accordingly, and the optimization of the inill (EI) criteria is performed by means of a scalar EA.  ... 
doi:10.1145/3321707.3321836 dblp:conf/gecco/BerveglieriDLAT19 fatcat:tzzxbxgv5bg5bcxqsvilzkabsu

On the hybridization of SMS-EMOA and local search for continuous multiobjective optimization

Patrick Koch, Oliver Kramer, Günter Rudolph, Nicola Beume
2009 Proceedings of the 11th Annual conference on Genetic and evolutionary computation - GECCO '09  
The motivation for the hybridization is a notion of combining the best of two worlds: evolutionary black box optimization and local search.  ...  We introduce a relay and a concurrent hybridization of the successful multiobjective optimizer SMS-EMOA and local optimization methods like Hooke & Jeeves and the Newton method.  ...  Jörg Fliege of the University of Southampton for providing the implementation of the multiobjective Newton and Steepest Descent method and Prof. Dr.  ... 
doi:10.1145/1569901.1569985 dblp:conf/gecco/KochKRB09 fatcat:x3mrs3vpnbfxjogbao4ozp4p4y

Evolutionary multiobjective optimization: open research areas and some challenges lying ahead

Carlos A. Coello Coello, Silvia González Brambila, Josué Figueroa Gamboa, Ma Guadalupe Castillo Tapia, Raquel Hernández Gómez
2019 Complex & Intelligent Systems  
Evolutionary multiobjective optimization has been a research area since the mid-1980s, and has experienced a very significant activity in the last 20 years.  ...  This paper provides a short description of some of them, with a particular focus on open research areas, rather than on specific research topics or problems.  ...  reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.  ... 
doi:10.1007/s40747-019-0113-4 fatcat:m5llfto6gzh4doap6gr3fmfwje

Special issue on emerging trends in soft computing: memetic algorithms

Yew-Soon Ong, Meng-Hiot Lim, Ferrante Neri, Hisao Ishibuchi
2008 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
and selecting these papers.  ...  We would like to give special thanks to Professor Vincenzo Loia and his production team for their continuous support that made this special issue a great success.  ...  The second paper, "A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems" by Wang et al. described a Memetic framework that adapts the hill-climbing local search method  ... 
doi:10.1007/s00500-008-0353-5 fatcat:m5hag4d5jvg7tpiuehr34mjg6m

A systems approach to evolutionary multiobjective structural optimization and beyond

Yaochu Jin, Bernhard Sendhoff
2009 IEEE Computational Intelligence Magazine  
systems such as self-organization, self-repair and scalability play a central role.  ...  Finally, we suggest a few promising future research topics in evolutionary structural design that consist in the necessary steps towards a life-like design approach, where design principles found in biological  ...  pre-selection according to the surrogate.  ... 
doi:10.1109/mci.2009.933094 fatcat:3lkyzxi5p5gddclxmw64ajecg4

Preference-Based Multiobjective Particle Swarm Optimization for Airfoil Design [chapter]

Robert Carrese, Xiaodong Li
2015 Springer Handbook of Computational Intelligence  
A significant challenge to the application of evolutionary multiobjective optimization (EMO) for transonic airfoil design is the often excessive number of computational fluid dynamic (CFD) simulations  ...  In this study, a multiobjective particle swarm optimization (MOPSO) framework is introduced, which incorporates designer preferences to provide further guidance in the search.  ...  The multiobjective search effort is coordinated via a MOPSO algorithm.  ... 
doi:10.1007/978-3-662-43505-2_67 fatcat:3cz54dknl5el5fwtnmkrn6cbje

Towards Efficient Multiobjective Optimization: Multiobjective statistical criterions

Ivo Couckuyt, Dirk Deschrijver, Tom Dhaene
2012 2012 IEEE Congress on Evolutionary Computation  
The EMO algorithm is applied on multiple standard benchmark problems and compared against the wellknown NSGA-II and SPEA2 multiobjective optimization methods with promising results.  ...  Most of the work in multiobjective optimization is focused on MultiObjective Evolutionary Algorithms (MOEAs).  ...  Dirk Deschrijver is a post-doctoral research fellow of FWO-Vlaanderen.  ... 
doi:10.1109/cec.2012.6256586 dblp:conf/cec/CouckuytDD12 fatcat:esme7i3gi5f6nhq3dyas7fos6a

A Multi-Facet Survey on Memetic Computation

Xianshun Chen, Yew-Soon Ong, Meng-Hiot Lim, Kay Chen Tan
2011 IEEE Transactions on Evolutionary Computation  
computation, multiagent system, multiobjective memetic algorithms, surrogate-assisted memetic algorithms.  ...  It covers a plethora of potentially rich meme-inspired computing methodologies, frameworks and operational algorithms including simple hybrids, adaptive hybrids and memetic automaton.  ...  of surrogates is confined within the trust-region local search.  ... 
doi:10.1109/tevc.2011.2132725 fatcat:dy4vpyft6rhdxb4dkt7iyaqvna
« Previous Showing results 1 — 15 out of 391 results