Filters








362 Hits in 7.0 sec

A multi-fidelity surrogate-model-assisted evolutionary algorithm for computationally expensive optimization problems

Bo Liu, Slawomir Koziel, Qingfu Zhang
2016 Journal of Computational Science  
Acknowledgement The authors thank CST AG for making CST Microwave Studio available. The authors would like to thank Dr. Renato Cordeiro de Amorim, Glyndwr 555 University, UK, for valuable discussions.  ...  For example, a 45 coarse model is used for a surrogate model-assisted evolutionary algorithm to explore the space and accurate but expensive ne model evaluations are only used for local search starting  ...  The surrogate model-aware 240 evolutionary search (SMAS) framework [23] with blind GP surrogate modeling is selected as the SBO for global optimization and the ORBIT algorithm with RBF surrogate modeling  ... 
doi:10.1016/j.jocs.2015.11.004 fatcat:kpv2agkq4fcftbgoevluzrmnkm

pysamoo: Surrogate-Assisted Multi-Objective Optimization in Python [article]

Julian Blank, Kalyanmoy Deb
2022 arXiv   pre-print
The framework extends the functionalities of pymoo, a popular and comprehensive toolbox for multi-objective optimization, and incorporates surrogates to support expensive function evaluations.  ...  Significant effort has been made to solve computationally expensive optimization problems in the past two decades, and various optimization methods incorporating surrogates into optimization have been  ...  Index Terms-Surrogate-Assisted Optimization, Model-based Optimization, Simulation Optimization, Evolutionary Computing, Genetic Algorithms. I.  ... 
arXiv:2204.05855v1 fatcat:niscndputncffdh2xiwomviiui

Surrogate-assisted evolutionary computation: Recent advances and future challenges

Yaochu Jin
2011 Swarm and Evolutionary Computation  
Surrogate-assisted, or meta-model based evolutionary computation uses efficient computational models, often known as surrogates or meta-models, for approximating the fitness function in evolutionary algorithms  ...  Very interestingly, surrogate-assisted evolutionary computation has found successful applications not only in solving computationally expensive single-or multi-objective optimization problems, but also  ...  Surrogate-assisted dynamic optimization If an expensive optimization is time-varying, evolutionary algorithms for solving dynamic optimization problems must be adopted to track the moving optima or moving  ... 
doi:10.1016/j.swevo.2011.05.001 fatcat:z3f6vey24fawbmz6gkpcjbnq5q

Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems [chapter]

Yew Soon Ong, P. B. Nair, A. J. Keane, K. W. Wong
2005 Studies in Fuzziness and Soft Computing  
In this paper, we present frameworks that employ surrogate models for solving computationally expensive optimization problems on a limited computational budget.  ...  Over the last decade, Evolutionary Algorithms (EAs) have emerged as a powerful paradigm for global optimization of multimodal functions.  ...  Conclusions The study of surrogate-assisted optimization algorithms for tackling computationally expensive high-fidelity engineering design problems is a research area that has attracted much attention  ... 
doi:10.1007/978-3-540-44511-1_15 fatcat:akme3pq77bc7fjdkra3zzlq7iy

A surrogate-assisted memetic co-evolutionary algorithm for expensive constrained optimization problems

C. K. Goh, D. Lim, L. Ma, Y. S. Ong, P. S. Dutta
2011 2011 IEEE Congress of Evolutionary Computation (CEC)  
This paper presents a surrogate-assisted memetic co-evolutionary framework to tackle both facets of practical problems, i.e. the optimization problems having computationally expensive objectives and constraints  ...  Stochastic optimization of computationally expensive problems is a relatively new field of research in evolutionary computation (EC).  ...  In evolutionary computation, one such research direction is on the use of surrogate models that are less computationally intensive [12] , i.e., the Surrogate-Assisted Evolutionary Algorithms (SAEAs).  ... 
doi:10.1109/cec.2011.5949693 dblp:conf/cec/GohLMOD11 fatcat:2pguuvwklrcdbepi5x6ci6k2ym

Constrained multi-objective antenna design optimization using surrogates

Prashant Singh, Marco Rossi, Ivo Couckuyt, Dirk Deschrijver, Hendrik Rogier, Tom Dhaene
2017 International journal of numerical modelling  
A novel surrogate-based constrained multi-objective optimization algorithm for simulation-driven optimization is proposed.  ...  This leads to substantial savings in time and drastically reduces the time-to-market for expensive antenna design optimization problems.  ...  Surrogate-assisted algorithms have gained popularity in recent years for the problem of optimizing antennas.  ... 
doi:10.1002/jnm.2248 fatcat:ltm5u4dxpncltm2teppbqeogmm

A proximity-based surrogate-assisted method for simulation-based design optimization of a cylinder head water jacket

Ali Ahrari, Julian Blank, Kalyanmoy Deb, Xianren Li
2020 figshare.com  
This study develops the Proximity-based Surrogate-Assisted Evolutionary Algorithm (PSA-EA) that aims at handling both single-objective and multi-objective computationally expensive problems.  ...  The method employs an ensemble of metamodels and a parallel infill criterion. PSA-EA is evaluated and compared to a recently developed surrogate-assisted evolutionary algorithm on ten test problems.  ...  This measures the intuition and mimics the decisions of an evolutionary algorithm during optimization.  ... 
doi:10.6084/m9.figshare.12932829.v1 fatcat:u5i4n6lxdvahrmakob5cwmrxli

A multi-fidelity RBF surrogate-based optimization framework for computationally expensive multi-modal problems with application to capacity planning of manufacturing systems

Jin Yi, Yichi Shen, Christine A. Shoemaker
2020 Structural And Multidisciplinary Optimization  
This paper presents a multi-fidelity RBF (radial basis function) surrogate-based optimization framework (MRSO) for computationally expensive multi-modal optimization problems when multi-fidelity (high-fidelity  ...  The performance of MRSO is compared with 6 other surrogate-based optimization methods (4 are using a single-fidelity surrogate and the rest 2 are using multi-fidelity surrogates).  ...  The computational work for this article was partially performed at the National Supercomputing Centre, Singapore. Compliance with ethical standards  ... 
doi:10.1007/s00158-020-02575-7 fatcat:bisttgm2tngj3myqttkbwijv7q

Semi-supervised learning assisted particle swarm optimization of computationally expensive problems

Chaoli Sun, Yaochu Jin, Ying Tan
2018 Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO '18  
Empirical studies on five 30-dimensional benchmark problems show that the proposed algorithm is able to find high-quality solutions for computationally expensive problems on a limited computational budget  ...  This paper aims to make use of semi-supervised learning techniques that are able to enhance the training of surrogate models using the unlabelled data together with the labelled data in a surrogate-assisted  ...  In this work, the RBF network is employed as a global surrogate model to assist the modified PSO to search for optimal solutions of computationally expensive problems.  ... 
doi:10.1145/3205455.3205596 dblp:conf/gecco/SunJT18 fatcat:darmszapang2bgvvdtqaie4pu4

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  
solutions to a problem.  ...  Which model to use? The first question to answer in surrogate-assisted multicriteria optimization is to decide which type of models should be used as the surrogate.  ...  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

Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems

Handing Wang, Yaochu Jin, John Doherty
2017 IEEE Transactions on Cybernetics  
Experimental results comparing the proposed algorithm with a few state-of-the-art surrogate-assisted evolutionary algorithms on both benchmark problems up to 30 decision variables as well as an airfoil  ...  In the proposed algorithm, a global model management strategy inspired from committeebased active learning is developed, which searches for the best and most uncertain solutions according to a surrogate  ...  CONCLUSION This paper proposes an ensemble-based model management strategy combining uncertainty and performance based criteria for surrogate-assisted evolutionary optimization of computationally expensive  ... 
doi:10.1109/tcyb.2017.2710978 pmid:28650832 fatcat:qoajp55kgrdfba7u5frw6tgauy

Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization

Zongzhao Zhou, Yew Soon Ong, Prasanth B. Nair, Andy J. Keane, Kai Yew Lum
2007 IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)  
In this paper, we present a novel surrogate-assisted evolutionary optimization framework for solving computationally expensive problems.  ...  Furthermore, it is shown that the new algorithm gives significant savings in computational cost when compared to the traditional evolutionary algorithm and other surrogate assisted optimization frameworks  ...  The present work is motivated by the lack of suitable multi-layer surrogate-assisted evolutionary optimization framework for solving computationally expensive problems.  ... 
doi:10.1109/tsmcc.2005.855506 fatcat:rk52bas67jcmbieytpukdhhjzy

Efficient Use of Partially Converged Simulations in Evolutionary Optimization

Juergen Branke, Md. Asafuddoula, Kalyan Shankar Bhattacharjee, Tapabrata Ray
2017 IEEE Transactions on Evolutionary Computation  
Abstract-For many real-world optimization problems, evaluating a solution involves running a computationally expensive simulation model.  ...  Evolutionary Optimization Using Surrogate Models The use of approximate fitness models within an evolutionary algorithm (EA) for computationally expensive fitness functions has been investigated in numerous  ...  His primary focus of research lies on handling computationally expensive optimization which includes surrogate assisted optimization, multi-fidelity based optimization and multi/many-objective optimization  ... 
doi:10.1109/tevc.2016.2569018 fatcat:y7n4a2zrsjfvbemmqpajpmmqgm

Methodology for "Surrogate-Assisted" Multi-Objective Optimisation (MOO) for Computationally Expensive Process Flowsheet Analysis

I. Sharma, A. Hoadley, S.M. Mahajani, A. Ganesh
2015 Chemical Engineering Transactions  
For such problems, "surrogate" or "meta" models are often used to approximate the exact, but computationally expensive models. This results in a significant saving in terms of computation time.  ...  This becomes an issue, especially for problems involving computationally expensive functional evaluations.  ...  Acknowledgement The authors would like to thank Orica Ltd for funding the project through the IITB-Monash research academy.  ... 
doi:10.3303/cet1545059 doaj:95a31a2d88ad4fa6be38c8072ff66ee0 fatcat:qrjjeh3lbfb6xa3og7cld4sdji

Evolutionary Approaches to Expensive Optimisation

Maumita Bhattacharya
2013 International Journal of Advanced Research in Artificial Intelligence (IJARAI)  
Surrogate assisted evolutionary algorithms (EA) are rapidly gaining popularity where applications of EA in complex real world problem domains are concerned.  ...  Use of approximate model or surrogates provides a much cheaper option. However, naturally this cheaper option comes with its own price!  ...  -Research is lacking in the area of surrogate assisted evolutionary algorithm (or other metaheuristics) for combinatorial optimization problems that are computationally intensive.  ... 
doi:10.14569/ijarai.2013.020308 fatcat:gfblyrwl3ndpbgx4pdyn7obfgi
« Previous Showing results 1 — 15 out of 362 results