On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems

This paper presents an alternative and efficient method forsolving the optimal control of single-stage hybrid manufacturingsystems which are composed with two different categories:continuous dynamics and discrete dynamics. Three different inertiaweights, a constant inertia weight (CIW), time-varying inertiaweight (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 theperformance of PSO algorithm. The PSO algorithm is simulatedindividually with the three inertia weights separately to computethe optimal control of the single-stage hybrid manufacturingsystem, and it is observed that the PSO with the proposed inertiaweight yields better result in terms of both optimal solution andfaster convergence. Added to this, the optimal control problem isalso solved through real coded genetic algorithm (RCGA) and theresults are compared with the PSO algorithms. A typical numericalexample is also included in this paper to illustrate the efficacyand betterment of the proposed algorithm. Several statisticalanalyses are carried out from which can be concluded that theproposed method is superior to all the other methods considered inthis paper.