1 research outputs found
Low-Complexity Particle Swarm Optimization for Time-Critical Applications
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.Comment: 24 pages, 1 figur