Surrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies
Chugh, T., Allmendinger, R., Ojalehto, V., & Miettinen, K. (2018). Surrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies. In H. Aguirre (Ed.), GECCO '18 : Proceedings of the Genetic and Evolutionary Computation Conference (pp. 609-616). Association for Computing Machinery (ACM). https://doi.org/10.1145/3205455.3205514
Toimittajat
Päivämäärä
2018Tekijänoikeudet
© 2018 Association for Computing Machinery
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 as there is no reason to assume that all objective functions should take an equal amount of time to be evaluated (particularly when objectives are evaluated separately). To cope with such problems, we propose a variation of the Kriging-assisted reference vector guided evolutionary algorithm (K-RVEA) called heterogeneous K-RVEA (short HK-RVEA). This algorithm is a merger of two main concepts designed to account for different latencies: A single-objective evolutionary algorithm for selecting training data to train surrogates and K-RVEA's approach for updating the surrogates. HK-RVEA is validated on a set of biobjective benchmark problems varying in terms of latencies and correlations between the objectives. The results are also compared to those obtained by previously proposed strategies for such problems, which were embedded in a non-surrogate-assisted evolutionary algorithm. Our experimental study shows that, under certain conditions, such as short latencies between the two objectives, HK-RVEA can outperform the existing strategies as well as an optimizer operating in an environment without latencies.
...
Julkaisija
Association for Computing Machinery (ACM)Emojulkaisun ISBN
978-1-4503-5618-3Konferenssi
Genetic and Evolutionary Computation ConferenceKuuluu julkaisuun
GECCO '18 : Proceedings of the Genetic and Evolutionary Computation ConferenceAsiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/28184398
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Rahoittaja(t)
TEKES; Suomen AkatemiaRahoitusohjelmat(t)
Muut, TEKES; Akatemiahanke, SALisätietoja rahoituksesta
This work was partly supported by Tekes, the Finnish funding agency for innovation under the FiDiPro project DeCoMo (Chugh) and the Academy of Finland, grant 287496 (Ojalehto).Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization
Chugh, Tinkle; Jin, Yaochu; Miettinen, Kaisa; Hakanen, Jussi; Sindhya, Karthik (Institute of Electrical and Electronics Engineers, 2018)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 ... -
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 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 ... -
Probabilistic Selection Approaches in Decomposition-based Evolutionary Algorithms for Offline Data-Driven Multiobjective Optimization
Mazumdar, Atanu; Chugh, Tinkle; Hakanen, Jussi; Miettinen, Kaisa (IEEE, 2022)In offline data-driven multiobjective optimization, no new data is available during the optimization process. Approximation models, also known as surrogates, are built using the provided offline data. A multiobjective ... -
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, ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.