Paper Info
Title
A Scalable Parallel Implementation Of Evolutionary Algorithms For Multi-Objective Optimization On Gpus
Abstract
Multi-Objective Evolutionary Algorithms(MOEAs) have been gaining increased popularity and usage in different fields of engineering. For real world large scale optimization problems with large variable/search space, using a large population of individuals in proportion to the size of search space is ubiquitous. Solving such problems with current state of the art algorithms like NSGA-II [1] is pervasive. The strength of NSGA-II lies in its non-dominance selection procedure and non-dominance based sorting of a population of individuals. Although, the non-dominated sort is computationally efficient for a small population (10(2) - 10(3)) of solutions but becomes computationally expensive and slow for a large population (10(4) - 10(5)) of solutions. Also, various archive based algorithms [2], [3] have been proposed in past which make use of a large population apart from the principal population. Therefore, there is a huge need for a scalable and parallel implementation of NSGA-II. With advent of consumer level Graphics processing units(GPUs) and advancement of CUDA framework we try to fill this research gap using GPGPU architecture. In this paper we propose a parallel GPU based implementation of NSGA-II with major focus on non-dominated sorting. The proposed approach can be easily coupled with the original form of NSGA-II to solve real world problems using large populations.
Year
Venue
Keywords
2015
2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
Evolutionary Algorithms, Multi-Objective Optimization, BigOpt, Graphics processing units GPUs, Parallel Computing, CUDA, GPGPU
DocType
Citations 
PageRank 
Conference
10
0.53
References 
Authors
22
2
Name
Order
Citations
PageRank
Samarth Gupta1135.60
Gary Tan222726.86