Low-Complexity Particle Swarm Optimization for Time-Critical Applications

TL;DR

This paper introduces two techniques to reduce computational complexity and accelerate convergence in particle swarm optimization, making it more suitable for time-critical applications with limited resources.

Contribution

The paper proposes two novel techniques that can be applied to any PSO variant to improve speed and efficiency for time-sensitive, resource-constrained scenarios.

Findings

Reduced computational complexity in PSO algorithms

Faster convergence speeds achieved with the proposed techniques

Maintained acceptable error performance in simulations

Abstract

Particle swam optimization (PSO) is a popular stochastic optimization method that has found wide applications in diverse fields. However, PSO suffers from high computational complexity and slow convergence speed. High computational complexity hinders its use in applications that have limited power resources while slow convergence speed makes it unsuitable for time critical applications. In this paper, we propose two techniques to overcome these limitations. The first technique reduces the computational complexity of PSO while the second technique speeds up its convergence. These techniques can be applied, either separately or in conjunction, to any existing PSO variant. The proposed techniques are robust to the number of dimensions of the optimization problem. Simulation results are presented for the proposed techniques applied to the standard PSO as well as to several PSO variants. The results show that the use of both these techniques in conjunction results in a reduction in the number of computations required as well as faster convergence speed while maintaining an acceptable error performance for time-critical applications.