An improved NSGA-III algorithm with adaptive mutation operator for Big Data optimization problems

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-111). On the other hand, a concern about the NSGA-Ill 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-111 algorithm. The proposed adaptive mutation operator strategy is evaluated using three crossover operators of NSGA-111 including simulated binary crossover (SBX), uniform crossover (UC) and single point crossover (SI). Subsequently, three improved NSGA-111 algorithms (NSGA-111 SBXAM, NSGA-III SIAM, and NSGAIII 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-111 algorithms. (C) 2018 Elsevier B.V. All rights reserved.