Distributed Multi-objective Particle Swarm Optimization using Time-delayed Virtual Global Best Method

Yuji Sato, Shota Ueno, Toshio Hirotsu

Abstract

To reduce the computational cost of particle swarm optimization (PSO) methods, research has begun on the use of Graphics Processing Units (GPUs) to achieve faster processing speeds. However, since PSO methods search based on a global best value, they are hampered by the frequent need for communication with global memory. Even using a standard PSO that uses a local best value does not solve this problem. In this paper, we propose a virtual global best method that speeds up computations by defining a time-delayed global best as a virtual global best in order to reduce the frequency of communication with low-speed global memory. We also propose a method that combines decomposition-based multi-objective PSO (MOPSO/D) with a virtual global best method to speed up multi-objective particle swarm optimization by running it in parallel while maintaining search accuracy, and we demonstrate the effectiveness of this approach by using a number of unimodal/multimodal single objective benchmark test functions and three classical benchmark test functions with two objectives.

Download


Paper Citation