Implementation of Parallel Simplified Swarm Optimization in CUDA

TL;DR

This paper introduces a CUDA-based Parallel Simplified Swarm Optimization algorithm that significantly reduces time complexity and avoids resource preemption, making it suitable for GPU-accelerated optimization tasks.

Contribution

It presents the first GPU implementation of Simplified Swarm Optimization, improving efficiency and resource management compared to previous methods.

Findings

Time complexity reduced by an order of magnitude of N

Resource preemption issues were eliminated

Effective parallelization on CUDA platform

Abstract

As the acquisition cost of the graphics processing unit (GPU) has decreased, personal computers (PC) can handle optimization problems nowadays. In optimization computing, intelligent swarm algorithms (SIAs) method is suitable for parallelization. However, a GPU-based Simplified Swarm Optimization Algorithm has never been proposed. Accordingly, this paper proposed Parallel Simplified Swarm Optimization (PSSO) based on the CUDA platform considering computational ability and versatility. In PSSO, the theoretical value of time complexity of fitness function is O (tNm). There are t iterations and N fitness functions, each of which required pair comparisons m times. pBests and gBest have the resource preemption when updating in previous studies. As the experiment results showed, the time complexity has successfully reduced by an order of magnitude of N, and the problem of resource preemption was avoided entirely.