A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization

By Tinkle Chugh, Yaochu Jin, Kaisa Miettinen, Jussi Hakanen, Karthik Sindhya. IEEE Transactions on Evolutionary Computation, 2018

538 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/6d82c5527cfa5ddc6c0e934b5eaf144468079212

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Citations in the corpus

A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization (2019, methodology): “Furthermore, many SAEAs for expensive multiobjective optimization were proposed in the past decades, e.g., the generalized surrogateassisted multiobjective memetic algorithm (GS-MOMA) [36], the weighted aggregation-based multiobjective optimization assisted by EGO (ParEGO) [37], the EGO assisted MOE”
Data-Driven Evolutionary Optimization: An Overview and Case Studies (2019, methodology): “A recent work on this topic has been proposed [15] for bi-objective optimization problems, where an algorithm called HK-RVEA was applied to solve problems with objectives of different computation time.”
A Random Forest-Assisted Evolutionary Algorithm for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems (2020, methodology): “the surrogates, such as Kriging model (Gaussian processes regression model) [13], [14]; radial basis function (RBF) networks [15]–[18]; polynomial regression [19]; and artificial neural networks [20], [21].”
A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem (2017, methodology): “To elevate the scope of the surrogate-assisted optimization to many-objective problems, recently, an algorithm called K-RVEA [16] was developed for computationally expensive problems, where Kriging models [17] are used in part of the computationally expensive objective functions.”
Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles (2019, methodology): “Many regression or classiﬁ-cation techniques can be used as surrogate models in SAEAs, such as radial basis function networks (RBFNs) [8], [9], Kriging models [10]–[13], polynomial regression (PR) models [14], among many others.”
Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems (2017, methodology): “prohibitively high computational complexity as the dimension of the problem becomes high [18].”
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] (2017, methodology): “…[68] 2005 Efﬁcient global optimization for Pareto optimization SMS-EGO [69] 2008 S-metric-selection-based efﬁcient global optimization K-RVEA [70] 2016 Kriging assisted RVEA [71] 1999 Multi-objective 0/1 knapsack problem and behavior of MOEAs on this problem analyzed in [72] ZDT1–ZDT6 [73]…”