A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization

By Qie He, Ling Wang. Applied Mathematics and Computation, 2007

489 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/3584cbcc67ee32e15fd9d285e75bbf9fd85daae3

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In Computational Theory and Mathematics

Citations in the corpus

Hybrid Nelder-Mead simplex search and particle swarm optimization for constrained engineering design problems (2009, methodology): “Table 2 contains a summary of the execution results of the same three benchmark functions, which are compared with those of homomorphous mapping (HM) (Koziel & Michalewicz, 1999), stochastic ranking (SR) (Runarsson & Yao, 2000), evolutionary programming (EP) (Coello & Becerra, 2004) and hybrid parti”
Dung beetle optimizer: a new meta-heuristic algorithm for global optimization (2022, methodology): “The optimization results achieved by the DBO algorithm are compared with other techniques including WOA [16], HHO algorithm [17], GWO algorithm [15], CPSO algorithm [45], GSA [46], PSO algorithm [45], MVO algorithm [40], HPSO algorithm [47], and PSO-SCA [42].”
A ranking selection-based particle swarm optimizer for engineering design optimization problems (2008, methodology): “... 1990), an augmented Lagrange multiplier-based method (Kannan and Kramer 1994), a combined genetic search algorithm (Deb 1997), mixed variable evolutionary programming (Cao and Wu 1999), a GA-based technique employing coevolution to adapt penalty factors (Coello 2000), a GA using a dominance-base”
Joint optimization of well placement and control for nonconventional well types (2014, unknown): “He and Wang, 2007; Kou et al., 2009).”
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].”
Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization (2010, methodology): “9E 12 42,100 Spring design HPSO [26] 0.”