Opposition learning adaptive cross-generation differential evolution algorithm based multi-objective optimization of rolling schedule for tandem cold rolling

With the combination of opposition learning and adaptive cross-generation differential evolution algorithm a new algorithm is proposed. Meanwhile the optimization model of rolling schedule is established. Power distribution, rolling energy consumption and the slip rate are selected as objective functions. Applying the opposition learning adaptive cross-generation differential evolution algorithm to the optimization model, rolling schedule for strips with 2.6mm∗900mm specification was optimized. Results show values of the three objectives were reduced compared with the used rolling schedule.

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