Behavior of crossover operators in NSGA III for large scale optimization problems
Abstract Traditional multi objective optimization evolutionary algorithms (MOEAs) do not usually meet the requirements for online data processing because of their high computational costs. This drawback has resulted in difficulties in the deployment of MOEAs for multi objective, large scale optimization problems. Among different evolutionary algorithms, non dominated sorting genetic algorithm the third version (NSGA III) is a fairly new method capable of solving large scale optimization problems with acceptable computational requirements. In this paper, the performance of three crossover operators of the NSGA III algorithm is benchmarked using a large scale optimization problem based on human electroencephalogram (EEG) signal processing. The studied operators are simulated binary (SBX) uniform crossover (UC) and single point (SI) crossovers. Furthermore, enhanced versions of the NSGA III algorithm are proposed through introducing the concept of Stud and designing several improved crossover operators of SBX, UC, and SI. The performance of the proposed NSGA III variants is verified on six large scale optimization problems. Experimental results indicate that the NSGA III methods with UC and UC Stud (UCS) outperform the other developed variants.
Inf. Sci.