A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization
Chugh, T., Jin, Y., Miettinen, K., Hakanen, J., & Sindhya, K. (2018). A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization. IEEE Transactions on Evolutionary Computation, 22(1), 129-142. https://doi.org/10.1109/TEVC.2016.2622301
Published in
IEEE Transactions on Evolutionary ComputationDate
2018Copyright
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed EA for many-objective optimization that relies on a set of adaptive reference vectors for selection. The proposed surrogate-assisted EA (SAEA) uses Kriging to approximate each objective function to reduce the computational cost. In managing the Kriging models, the algorithm focuses on the balance of diversity and convergence by making use of the uncertainty information in the approximated objective values given by the Kriging models, the distribution of the reference vectors as well as the location of the individuals. In addition, we design a strategy for choosing data for training the Kriging model to limit the computation time without impairing the approximation accuracy. Empirical results on comparing the new algorithm with the state-of-the-art SAEAs on a number of benchmark problems demonstrate the competitiveness of the proposed algorithm.
...
Publisher
Institute of Electrical and Electronics EngineersISSN Search the Publication Forum
1089-778XKeywords
Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/26550136
Metadata
Show full item recordCollections
Related items
Showing items with similar title or keywords.
-
Surrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies
Chugh, Tinkle; Allmendinger, Richard; Ojalehto, Vesa; Miettinen, Kaisa (Association for Computing Machinery (ACM), 2018)We consider multiobjective optimization problems where objective functions have different (or heterogeneous) evaluation times or latencies. This is of great relevance for (computationally) expensive multiobjective optimization ... -
Multiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithm
Chugh, Tinkle; Kratky, Tomas; Miettinen, Kaisa; Jin, Yaochu; Makkonen, Pekka (ACM, 2019)We formulate and solve a real-world shape design optimization problem of an air intake ventilation system in a tractor cabin by using a preference-based surrogate-assisted evolutionary multiobjective optimization algorithm. ... -
A surrogate-assisted a priori multiobjective evolutionary algorithm for constrained multiobjective optimization problems
Aghaei pour, Pouya; Hakanen, Jussi; Miettinen, Kaisa (Springer, 2024)We consider multiobjective optimization problems with at least one computationally expensive constraint function and propose a novel surrogate-assisted evolutionary algorithm that can incorporate preference information ... -
A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms
Chugh, Tinkle; Sindhya, Karthik; Hakanen, Jussi; Miettinen, Kaisa (Springer, 2019)Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, ... -
A Data-Driven Surrogate-Assisted Evolutionary Algorithm Applied to a Many-Objective Blast Furnace Optimization Problem
Chugh, Tinkle; Chakraborti, Nirupam; Sindhya, Karthik; Jin, Yaochu (Taylor & Francis Inc., 2017)A new data-driven reference vector-guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace. A total of eight objectives ...