Multiple trial vectors in differential evolution for engineering design

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Constraint-handling in nature-inspired numerical optimization: Past, present and future (2011, methodology): “A further improved version of the aforementioned algorithm was proposed by Takahama and Sakai in [142], where an archive to store solutions and the ability of a vector to generate more than one trial vector [143] were added.”
Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems (2011, background): “[76] also violate the first and third constrains, respectively.”
Slime mould algorithm: A new method for stochastic optimization (2020, unknown): “From the data of Table 21 , it is obvious that SMA can obtain fairly superior optimal values compared with MFO[23], BA[73], HPSO[74], CSS[5], CPSO[75], ACO[76], GWO[21], WOA[50], MDDE[77], Lagrangian multiplier[78] and Branch-bound[79].”
Harris hawks optimization: Algorithm and applications (2019, result): “73], [ G-QPSO [84] The results of HHO are compared to those of GWO [55], GA [ HPSO 83], G-QPSO , 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].”