Name
Title | ||
|---|---|---|
An improved NSGA-III algorithm with adaptive mutation operator for Big Data optimization problems. |
Abstract | ||
|---|---|---|
One of the major challenges of solving Big Data optimization problems via traditional multi-objective evolutionary algorithms (MOEAs) is their high computational costs. This issue has been efficiently tackled by non-dominated sorting genetic algorithm, the third version, (NSGA-III). On the other hand, a concern about the NSGA-III algorithm is that it uses a fixed rate for mutation operator. To cope with this issue, this study introduces an adaptive mutation operator to enhance the performance of the standard NSGA-III algorithm. The proposed adaptive mutation operator strategy is evaluated using three crossover operators of NSGA-III including simulated binary crossover (SBX), uniform crossover (UC) and single point crossover (SI). Subsequently, three improved NSGA-III algorithms (NSGA-III SBXAM, NSGA-III SIAM, and NSGA-III UCAM) are developed. These enhanced algorithms are then implemented to solve a number of Big Data optimization problems. Experimental results indicate that NSGA-III with UC and adaptive mutation operator outperforms the other NSGA-III algorithms. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.future.2018.06.008 | Future Generation Computer Systems |
Keywords | Field | DocType |
Big Data optimization,Evolutionary multi-objective optimization,NSGA-III,Mutation operator,Adaptive operators | Crossover,Adaptive mutation,Evolutionary algorithm,Computer science,Algorithm,Sorting,Operator (computer programming),Optimization problem,Genetic algorithm,Binary number | Journal |
Volume | ISSN | Citations |
88 | 0167-739X | 8 |
PageRank | References | Authors |
0.49 | 62 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiao-Hong Yi | 1 | 17 | 0.95 |
| Suash Deb | 2 | 1926 | 82.86 |
| Junyu Dong | 3 | 99 | 23.43 |
| Amir Hossein Alavi | 4 | 1016 | 45.59 |
| Gai-Ge Wang | 5 | 1251 | 48.96 |