An effective co-evolutionary particle swarm optimization for constrained engineering design problems

By Qie He, Ling Wang. Engineering applications of artificial intelligence, 2007

1,082 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/478716ddc597df56c30e092a6a6402d6fdf91959

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Seyedali Mirjalili
2. Amir H. Gandomi
3. Ali Asghar Heidari
4. Seyed Mohammad Mirjalili
5. Huiling Chen
6. Andrew Lewis
7. Siamak Talatahari
8. Hamid Salimi

In Civil and Structural Engineering

1. Ali Kaveh
2. Seyed Mohammad Mirjalili
3. Siamak Talatahari
4. Seyedali Mirjalili
5. Vahid Reza Mahdavi
6. Hamid Salimi
7. Pinar Çivicioglu
8. Andrew Lewis

In Computational Theory and Mathematics

1. Seyedali Mirjalili
2. Amir H. Gandomi
3. Ali Asghar Heidari
4. Seyed Mohammad Mirjalili
5. Andrew Lewis
6. Huiling Chen
7. Siamak Talatahari
8. Hamid Salimi

Citations in the corpus

• A survey on optimization metaheuristics (2013, background): “Some variants of Cooperati ve CoEAs have been propos ed, such as co-evolutionar y particle swarms [131] and coevolutionary differential evolution [246].”
• Harris hawks optimization: Algorithm and applications (2019, neutral): “0126763 Arora [72] 0.053396 0.399180 9.185400 0.012730 GA2 [73] 0.051480 0.351661 11.632201 0.012704 GA3 [74] 0.051989 0.363965 10.890522 0.012681 Belegundu [75] 0.05 0.315900 14.250000 0.012833 CPSO [76] 0.051728 0.357644 11.244543 0.012674 DEDS [64] 0.051689 0.356717 11.288965 0.012665 GSA [25] 0.”
• Butterfly optimization algorithm: a novel approach for global optimization (2018, methodology): “8 Schematic representation of spring pared with Grey Wolf Optimization (GWO) (Mirjalili et al. 2014), Gravitational Search Algorithm (GSA) (Rashedi et al. 2009), PSO (He and Wang 2007), ES (Mezura-Montes and Coello 2008), GA (Coello 2000a), HS (Mahdavi et al. 2007), and DE (Huang et al. 2007).”
• An effective co-evolutionary differential evolution for constrained optimization (2007, methodology): “Table 6 Comparison of the best solution for Example 9 found by different methods Design variables CDE Sandgren [25] Kannan [24] Deb [23] Coello [4] Coello and Montes [8] CPSO [6]”
• Colliding bodies optimization: A novel meta-heuristic method (2014, methodology): “Table 1 Comparison of CBO optimized designs with literature for the welded beam problem Design Variables Best solution found Ragsdell and Phillips [14] Deb [15] Coello [16] Coello and Montes [17] He and Wang [18] Montes and Coello [19] Kaveh and Talatahari [6] Present work”
• Slime mould algorithm: A new method for stochastic optimization (2020, neutral): “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].”
• Multi-Verse Optimizer: a nature-inspired algorithm for global optimization (2015, result): “We compare the results to PSO [48], GA [49–51], ES [52], DE [53], and ACO [54].”