Name
Abstract | ||
|---|---|---|
The Intellects-Masses Optimizer (IMO) is a recently-proposed cultural algorithm, which is easy to understand, use, and implement. IMO requires (almost) no parameter tuning and has successfully been used to tackle unconstrained continuous optimization problems. A modified variant of IMO, called MIMO, is proposed in this paper. The proposed method uses improved update equations, a self-adaptive scaling factor, duplicates removal, and a local search to improve the performance of IMO. The MIMO method is tested on the 22 IEEE CEC 2011 real-world benchmark problems and is compared with 14 state-of-the-art algorithms. The results demonstrate the outperformance of the proposed method and its superiority compared to the original IMO algorithm. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.swevo.2018.02.015 | Swarm and Evolutionary Computation |
Keywords | Field | DocType |
Cultural algorithms,Intellects-Masses Optimizer,Metaheuristics,Stochastic search,Real-world optimization,Continuous optimization | Continuous optimization,Scale factor,Mathematical optimization,Computer science,MIMO,Cultural algorithm,Local search (optimization),Optimization problem | Journal |
Volume | ISSN | Citations |
41 | 2210-6502 | 3 |
PageRank | References | Authors |
0.36 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahamed G. H. Omran | 1 | 648 | 35.28 |
| Salah al-Sharhan | 2 | 106 | 13.21 |
| Maurice Clerc | 3 | 1923 | 180.04 |