A simple multimembered evolution strategy to solve constrained optimization problems

By Efrén Mezura-Montes, Carlos Artemio Coello Coello. IEEE Transactions on Evolutionary Computation, 2005

598 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/516628e899df80c4c6e3aea57a837d1f90cd1241

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Citations in the corpus

Multiple trial vectors in differential evolution for engineering design (2007, methodology): “Furthermore, a comparison of the performance obtained by the proposed DE against two state-of-the-art approaches was included: (i) the self-adaptive penalty function (Tessema and Yen 2006 ) and (ii) the simple multimembered evolution strategy (SMES) (Mezura-Montes and Coello Coello 2005 ).”
Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization (2010, methodology): “PSO-DE CRGA [8] SAPF [4] CDE [5] CULDE [23] CPSO-GD [15] SMES [10]”
Harris hawks optimization: Algorithm and applications (2019, unknown): “The results of HHO are compared to those of GWO [55], GA [73], HPSO [83], G-QPSO [84], WEO [79], IACO [85], BA [80], MFO [56], CSS [86], ESs [81], CPSO [76], BIANCA [87], MDDE [88], DELC [77], WOA [18], GA3 [74], Lagrangian multiplier (Kannan) [18], and Branch-bound (Sandgren) [18].”
Oppositional biogeography-based optimization (2009, methodology): “The following method, which we employ to test our algorithms, is published in [168, 169].”
Biogeography-Based Optimization (2008, background): “An ES [8], [20]–[22] is an algorithm that generally gives about equal importance to recombination and mutation, and that allows more than two parents to contribute to an offspring.”
Differential evolution with dynamic stochastic selection for constrained optimization (2008, methodology): “In particular, the following constraint violation definition has been often used as the penalty function [4, 13 ]: /ðx Þ¼ X”
Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems (2012, methodology): “The performance of TLBO is also compared with other optimization methods with different constraint handling techniques, i.e. evolutionary strategies with stochastic ranking (ES + SR) (Runarsson and Yao 2000), simple multimembrane evolutionary strategy (SMES) (Mezura-Montes and Coello 2005), adaptive”
Constraint-handling in nature-inspired numerical optimization: Past, present and future (2011, methodology): “Motivated by the idea of keeping suitable infeasible solutions [27,43,62,170] Ray et al.”