Reference Vector Assisted Candidate Search with Aggregated Surrogate for Computationally Expensive Many Objective Optimization Problems
Published Online:29 Dec 2022https://doi.org/10.1287/ijoc.2022.1260
References
- (2016) Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection. J. Global Optim. 64:17–32.Crossref, Google Scholar
- (2008) Multiobjective optimization through a series of single-objective formulations. SIAM J. Optim. 19(1):188–210.Crossref, Google Scholar
- (2010) A mesh adaptive direct search algorithm for multiobjective optimization. Eur. J. Oper. Res. 204(3):545–556.Crossref, Google Scholar
- (2014) Steady state IBEA assisted by MLP neural networks for expensive multi-objective optimization problems. Proc. Genetic Evolutionary Comput. Conf. (ACM, New York), 581–588.Google Scholar
- (2011) HypE: An algorithm for fast hypervolume-based many-objective optimization. Evolutionary Comput. 19(1):45–76.Crossref, Google Scholar
- (2007) SMS-EMOA: Multiobjective selection based on dominated hypervolume. Eur. J. Oper. Res. 181(3):1653–1669.Crossref, Google Scholar
- (2018) Multiple surrogate-assisted many-objective optimization for computationally expensive engineering design. J. Mech. Design 140(5):051403.Google Scholar
- (2015) Evolutionary many-objective optimization: A quick-start guide. Survey Oper. Res. Management Sci. 20(2):35–42.Google Scholar
- (2016) A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Trans. Evolutionary Comput. 20(5):773–791.Crossref, Google Scholar
- (2017) A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem. Materials Manufacturing Processes 32(10):1172–1178.Crossref, Google Scholar
- (2018) A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization. IEEE Trans. Evolutionary Comput. 22(1):129–142.Crossref, Google Scholar
- (2004) A study of the parallelization of a coevolutionary multi-objective evolutionary algorithm. Monroy R, Arroyo-Figueroa G, Sucar LE, Sossa H, eds. Proc. Mexican Internat. Conf. on Artificial Intelligence (Springer, Berlin), 688–697.Google Scholar
- (2011) Experiments with Mixtures: Designs, Models, and the Analysis of Mixture Data (John Wiley & Sons, Hoboken, NJ).Crossref, Google Scholar
- (2014) An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Trans. Evolutionary Comput. 18(4):577–601.Crossref, Google Scholar
- (2002a) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evolutionary Comput. 6(2):182–197.Crossref, Google Scholar
- (2002b) Scalable multi-objective optimization test problems. Proc. Congress on Evolutionary Comput. (IEEE, Piscataway, NJ), 825–830.Google Scholar
- (2020) Indicator-based multi-objective evolutionary algorithms: A comprehensive survey. ACM Comput. Survey 53(2):1–35.Crossref, Google Scholar
- (2017) A hyper-heuristic of scalarizing functions. Proc. Genetic and Evolutionary Comput. Conf. (Association for Computing Machinery, New York), 577–584.Google Scholar
- (2001) A radial basis function method for global optimization. J. Global Optim. 19:201–227.Crossref, Google Scholar
- (2013) Borg: An auto-adaptive many-objective evolutionary computing framework. Evolutionary Comput. 21(2):231–259.Crossref, Google Scholar
- (2006) A review of multiobjective test problems and a scalable test problem toolkit. IEEE Trans. Evolutionary Comput. 10(5):477–506.Crossref, Google Scholar
- (2001) Failure of Pareto-based MOEAs: Does non-dominated really mean near to optimal? Proc. Congress on Evolutionary Comput. (IEEE, Piscataway, NJ), 957–962.Google Scholar
- (2002) A framework for evolutionary optimization with approximate fitness functions. IEEE Trans. Evolutionary Comput. 6(5):481–494.Crossref, Google Scholar
- (1998) Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4):455–492.Crossref, Google Scholar
- (2006) ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Trans. Evolutionary Comput. 10(1):50–66.Crossref, Google Scholar
- (2006) Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design. Adv. Water Resources 29(6):792–807.Crossref, Google Scholar
- (2002) Combining convergence and diversity in evolutionary multiobjective optimization. Evolutionary Comput. 10(3):263–282.Crossref, Google Scholar
- (2015) Many-objective evolutionary algorithms: A survey. ACM Comput. Survey 48(1):1–35.Crossref, Google Scholar
- (2016) Stochastic ranking algorithm for many-objective optimization based on multiple indicators. IEEE Trans. Evolutionary Comput. 20(6):924–938.Crossref, Google Scholar
- (2010) Dominance-based Pareto-surrogate for multi-objective optimization. Proc. Asia-Pacific Conf. on Simulated Evolution and Learn. (Springer, Berlin), 230–239.Google Scholar
- (1967) Some methods for classification and analysis of multivariate observations. Proc. 5th Berkeley Sympos. on Math. Statist. and Probability (University of California Press, Berkeley, CA), 281–297.Google Scholar
- (1998) Nonlinear Multiobjective Optimization (Springer Science & Business Media, Boston).Crossref, Google Scholar
- (2009) Benchmarking derivative-free optimization algorithms. SIAM J. Optim. 20(1):172–191.Crossref, Google Scholar
- (2017) SOCEMO: Surrogate optimization of computationally expensive multiobjective problems. INFORMS J. Comput. 29(4):581–596.Link, Google Scholar
- (2019) Surrogate optimization of computationally expensive black-box problems with hidden constraints. INFORMS J. Comput. 31(4):689–702.Link, Google Scholar
- (2019) A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization. IEEE Trans. Evolutionary Comput. 23(1):74–88.Crossref, Google Scholar
- (2013) Aggregate meta-models for evolutionary multiobjective and many-objective optimization. Neurocomputing 116:392–402.Crossref, Google Scholar
- (2008) Multiobjective optimization on a limited budget of evaluations using model-assisted s-metric selection. Rudolph G, Jansen T, Beume N, Lucas S, Poloni, C, eds. Internat. Conf. Parallel Problem Solving Nature (Springer, Berlin), 784–794.Google Scholar
- (1992) The theory of radial basis function approximation in 1990. Light W, ed. Advances in Numerical Analysis: Vol. 2: Wavelets, Subdivision Algorithms, and Radial Basis Functions (Oxford University Press, London), 105–210.Google Scholar
- (2007) On the evolutionary optimization of many conflicting objectives. IEEE Trans. Evolutionary Comput. 11(6):770–784.Crossref, Google Scholar
- (2019) A survey of many-objective optimisation in search-based software engineering. J. Systems Software 149:382–395.Crossref, Google Scholar
- (2004) Local function approximation in evolutionary algorithms for the optimization of costly functions. IEEE Trans. Evolutionary Comput. 8(5):490–505.Crossref, Google Scholar
- (2007) A stochastic radial basis function method for the global optimization of expensive functions. INFORMS J. Comput. 19(4):497–509.Link, Google Scholar
- (2010) On the influence of the number of objectives on the hardness of a multiobjective optimization problem. IEEE Trans. Evolutionary Comput. 15(4):444–455.Crossref, Google Scholar
- (2021) A kriging-assisted two-archive evolutionary algorithm for expensive many-objective optimization. IEEE Trans. Evolutionary Comput. 25(6):1013–1027.Crossref, Google Scholar
- (2016) Study of the approximation of the fitness landscape and the ranking process of scalarizing functions for many-objective problems. Proc. IEEE Congress on Evolutionary Comput. (IEEE, Piscataway, NJ), 4358–4365.Google Scholar
- (2022) Reference vector assisted candidate search with aggregated surrogate for computationally expensive many objective optimization problems. Accessed October 24, 2022, https://dx.doi.org/10.5281/zenodo.7243971.Google Scholar
- (2022) Integrating ε-dominance and RBF surrogate optimization for solving computationally expensive many-objective optimization problems. J. Global Optim. 82:965–992.Crossref, Google Scholar
- (2015) Two Arch2: An improved two-archive algorithm for many-objective optimization. IEEE Trans. Evolutionary Comput. 19(4):524–541.Crossref, Google Scholar
- (1963) Critical Values and Probability Levels for the Wilcoxon Rank Sum Test and the Wilcoxon Signed Rank Test (American Cyanamid).Google Scholar
- (2007) MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evolutionary Comput. 11(6):712–731.Crossref, Google Scholar
- (2015) A classification and Pareto domination based multiobjective evolutionary algorithm. Proc. IEEE Congress on Evolutionary Comput. (IEEE, Piscataway, NJ), 2883–2890.Google Scholar
- (2010) Expensive multiobjective optimization by MOEA/D with Gaussian process model. IEEE Trans. Evolutionary Comput. 14(3):456–474.Crossref, Google Scholar
- (2007) Convergence analysis of a self-adaptive multi-objective evolutionary algorithm based on grids. Inform. Processing Lett. 104(4):117–122.Crossref, Google Scholar
- (2011) Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm Evolutionary Comput. 1(1):32–49.Crossref, Google Scholar
- (2004) Indicator-based selection in multiobjective search. Proc. Internat. Conf. on Parallel Problem Solving from Nature (Springer, Berlin) 832–842.Google Scholar
- (2001) SPEA2: Improving the strength Pareto evolutionary algorithm. TIK-report, No. 103, Swiss Federal Institute of Technology, Zurich, Switzerland.Google Scholar
- (2008) A new evolutionary algorithm for solving many-objective optimization problems. IEEE Trans. Systems Man Cybernetics B Cybernetics 38(5):1402–1412.Crossref, Google Scholar

