Name
Title | ||
|---|---|---|
Emosoa: A New Evolutionary Multi-Objective Seagull Optimization Algorithm For Global Optimization |
Abstract | ||
|---|---|---|
This study introduces the evolutionary multi-objective version of seagull optimization algorithm (SOA), entitled Evolutionary Multi-objective Seagull Optimization Algorithm (EMoSOA). In this algorithm, a dynamic archive concept, grid mechanism, leader selection, and genetic operators are employed with the capability to cache the solutions from the non-dominatedPareto. The roulette-wheel method is employed to find the appropriate archived solutions. The proposed algorithm is tested and compared with state-of-the-art metaheuristic algorithms over twenty-four standard benchmark test functions. Four real-world engineering design problems are validated using proposedEMoSOAalgorithm to determine its adequacy. The findings of empirical research indicate that the proposed algorithm is better than other algorithms. It also takes into account those optimal solutions from theParetowhich shows high convergence. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s13042-020-01189-1 | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS |
Keywords | DocType | Volume |
Seagull Optimization Algorithm, Multi-objective Optimization, Evolutionary, Pareto, Engineering Design Problems, Convergence, Diversity | Journal | 12 |
Issue | ISSN | Citations |
2 | 1868-8071 | 5 |
PageRank | References | Authors |
0.38 | 55 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gaurav Dhiman | 1 | 73 | 7.99 |
| Krishna Kant Singh | 2 | 5 | 0.38 |
| Adam Slowik | 3 | 46 | 12.95 |
| Victor Chang | 4 | 1202 | 107.48 |
| Ali Riza Yildiz | 5 | 5 | 0.38 |
| Amandeep Kaur | 6 | 110 | 20.46 |
| Meenakshi Garg | 7 | 14 | 1.20 |