On the optimal control of steel annealing processes via various versions of genetic and particle swarm optimization algorithms

Abstract

7 This paper elucidates the computation of optimal controls for steel annealing processes as hybrid systems which comprise of one or more furnaces integrated with plant-wide planning and scheduling operations. A class of hybrid system is considered to capture the trade-off between metallurgical quality requirement and timely product delivery. Various optimization algorithms including particle swarm optimization algorithm (PSO) with time varying inertia weight methods, PSO with globally and locally tuned parameters (GLBest PSO), parameter free PSO (pf-PSO) and PSO like algorithm via extrapolation (ePSO), real coded genetic algorithm (RCGA) and two-phase hybrid real coded genetic algorithm (HRCGA) are considered to solve the optimal control problems for the steel annealing processes (SAP). The optimal solutions including optimal line speed, optimal cost, and job completion time and convergence rate obtained through all these optimization algorithms are compared with each other and also those obtained via the existing method, forward algorithm (FA). Various statistical analyses and analysis of variance (ANOVA) test and hypothesis t-test are carried out in order to compare the performance of each method in solving the optimal control problems of SAP. The comparative study of the performance of the various algorithms indicates that the PSO like algorithms, pf-PSO and ePSO are equally good and are also better than all the other optimization methods considered in this chapter.