An improved ant colony optimization for constrained engineering design problems
By Ali Kaveh, Siamak Talatahari. 2010
345 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/54546d7036873616eafc5594341854a23cbdaaaf
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
- Seyedali Mirjalili
- Seyed Mohammad Mirjalili
- Siamak Talatahari
- Andrew Lewis
- Amir H. Gandomi
- Gaurav Dhiman
- Vijay Kumar
- Ali Asghar Heidari
In Computational Theory and Mathematics
- Seyedali Mirjalili
- Seyed Mohammad Mirjalili
- Siamak Talatahari
- Andrew Lewis
- Amir H. Gandomi
- Gaurav Dhiman
- Ali Asghar Heidari
- Vijay Kumar
In Statistics and Probability
- Hamid Salimi
- Seyed Mohammad Mirjalili
- Seyedali Mirjalili
- Andrew Lewis
- Ali Kaveh
- Siamak Talatahari
- Abdolreza Hatamlou
Citations in the corpus
- 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].”
- Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm (2015, methodology): “It is evident from the results that MFO, SOS, and MBA significantly outperformed PSO, PSOPC, HPSO, and DHPSACO.”
- 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].”
- Stochastic Fractal Search: A powerful metaheuristic algorithm (2015, methodology): “Also, This problem was solved by He and Wang [50] using CPSO, Montes and Coello [52] employing evolution strategies and the Kaveh and Talatahari [51] using the ACO.”
- A novel heuristic optimization method: charged system search (2010, methodology): “Lagrangian multiplier approach [28], genetic adaptive search [29], a GA-based co-evolution model [21], a feasibility-based tournament selection scheme [22], a co-evolutionary particle swarm optimization [23], an evolution strategy [24], and an improved ant colony optimization [ 1 ].”
- Grey Wolf Optimizer (2014, methodology): “The heuristic methods that have been adopted to optimize this problem are: PSO [58], GA [57, 60, 69], ES [59], DE [62], and ACO [70].”