标题：On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems
摘要：This paper presents an alternative and efficient method for
solving the optimal control of single-stage hybrid manufacturing
systems which are composed with two different categories:
continuous dynamics and discrete dynamics. Three different inertia
weights, a constant inertia weight (CIW), time-varying inertia
weight (TVIW), and global-local best inertia weight (GLbestIW),
are considered with the particle swarm optimization (PSO)
algorithm to analyze the impact of inertia weight on the
performance of PSO algorithm. The PSO algorithm is simulated
individually with the three inertia weights separately to compute
the optimal control of the single-stage hybrid manufacturing
system, and it is observed that the PSO with the proposed inertia
weight yields better result in terms of both optimal solution and
faster convergence. Added to this, the optimal control problem is
also solved through real coded genetic algorithm (RCGA) and the
results are compared with the PSO algorithms. A typical numerical
example is also included in this paper to illustrate the efficacy
and betterment of the proposed algorithm. Several statistical
analyses are carried out from which can be concluded that the
proposed method is superior to all the other methods considered in
this paper.
