Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Spaces

By Jasper A. Vrugt, Bruce A. Robinson, James M. Hyman. IEEE Transactions on Evolutionary Computation, 2009

350 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/b7ac74d455fcb6812eb03bc99c3ec7ffc7c264ea

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Citations in the corpus

Assessment of parametric uncertainty for groundwater reactive transport modeling (2014, methodology): “DREAM(ZS) uses Differential Evolution Adaptive Metropolis (DREAM) [Vrugt et al., 2008a, 2009a, 2009b] as its main building block, which runs multiple Markov chains from different starting points in parallel for global search in the parameter space to make it possible to search multiple parameter…”
Bio-inspired computation: Where we stand and what's next (2019, background): “For instead, proposals like [291] or [242] combine different classic algorithms and some specific local search methods, obtaining very competitive results that clearly surpass those obtained by each of its components.”
Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation (2013, background): “Recent developments include simple randomized adaptation (Mazi et al., 2004; Tol-son and Shoemaker, 2007), multimethod ensemble (Vrugt and Robinson, 2007; Vrugt et al., 2009b), and ﬁltering based (Pauwels, 2008) parameter estimation methods that further improve search efﬁciency and reliability.”
Optimization and uncertainty assessment of strongly nonlinear groundwater models with high parameter dimensionality (2010, methodology): “The recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm [Vrugt et al., 2008a, 2009a, 2009b], which searches for objective function minima while simultaneously providing estimates of parameter uncertainty, overcomes this limitation while maintaining relatively high levels…”
A social spider algorithm for global optimization (2015, background): “Some schemes encode the parameters into the solution and evolve the parameters together with the population [33].”
Maximum likelihood estimation of the dynamic coregionalization model with heterotopic data (2011, result): “Moreover, this is also consistent with the equifinality approach related to a multiplicity of local maxima giving equivalent models (see e.g. Vrugt et al. (2009)).”
Modeling Soil Processes: Review, Key Challenges, and New Perspectives (2016, methodology): “|D) is often highly dimensional and analytically intractable, and Monte Carlo simulation methods are required to approximate the target (Vrugt et al., 2008, 2009a,b; Laloy and Vrugt, 2012).”
Self-adaptive learning based particle swarm optimization (2011, methodology): “Successful examples were observed on combinatorial optimization problems [7,13,14], global numerical optimization problems [33,43,46,54,59], and multi-objective optimization problems [58].”
Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014, background): “In recognition of this fact, a number of self-tuning and selforganising optimisation algorithms have been developed (Hadka and Reed, 2013; McClymont et al., 2013; Vrugt and Robinson, 2007; Vrugt et al., 2009).”
Numerical assessment of metamodelling strategies in computationally intensive optimization (2012, methodology): “, 2003) or AMALGAM (Vrugt et al., 2009) might be good additional algorithms”
Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework (2013, background): “Vrugt and Robinson (2007) and Vrugt et al. (2009) address this issue by introducing an adaptive multi-operator hybrid called AMALGAM.”