caa a22 4500 445877944 CHVBK 20180317145533.0 cr unu---uuuuu 170323e20110901xx s 000 0 eng 10.1007/s11081-011-9143-5 doi (NATIONALLICENCE)springer-10.1007/s11081-011-9143-5 On the optimal control of steel annealing processes via various versions of genetic and particle swarm optimization algorithms [Elektronische Daten] [M. Arumugam, Aarthi Chandramohan, Gajula Murthy] 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. Springer Science+Business Media, LLC, 2011 Particle swarm optimization (PSO) nationallicence Real coded genetic algorithm (RCGA) nationallicence Steel annealing processes (SAP) nationallicence Hybrid systems nationallicence Optimal control nationallicence Arumugam M. School of Engineering and Physical Sciences, Heriot-Watt University, Dubai campus, 294345 DIAC, Dubai, United Arab Emirates aut Chandramohan Aarthi School of Engineering and Physical Sciences, Heriot-Watt University, Dubai campus, 294345 DIAC, Dubai, United Arab Emirates aut Murthy Gajula Faculty of Engineering and Technology, Multimedia University, 75450, Melaka, Malaysia aut Optimization and Engineering Springer US; http://www.springer-ny.com 12/3(2011-09-01), 371-392 1389-4420 12:3<371 2011 12 11081 https://doi.org/10.1007/s11081-011-9143-5 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11081-011-9143-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Arumugam M. School of Engineering and Physical Sciences, Heriot-Watt University, Dubai campus, 294345 DIAC, Dubai, United Arab Emirates aut NATIONALLICENCE 700 1- Chandramohan Aarthi School of Engineering and Physical Sciences, Heriot-Watt University, Dubai campus, 294345 DIAC, Dubai, United Arab Emirates aut NATIONALLICENCE 700 1- Murthy Gajula Faculty of Engineering and Technology, Multimedia University, 75450, Melaka, Malaysia aut NATIONALLICENCE 773 0- Optimization and Engineering Springer US; http://www.springer-ny.com 12/3(2011-09-01), 371-392 1389-4420 12:3<371 2011 12 11081 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer