An improved shuffled frog leaping algorithm on multi-objective parallel machine batch scheduling

To solve the batch scheduling problem of equivalent parallel machines, the mathematical model to minimize makespan and minimize the cost of production is constructed. An improved shuffled frog leaping algorithm is developed, which embedded the elite group self-learning mechanism. The batch schemes and scheduling scheme are combined by using integrated optimization strategy. Elite group self-learning mechanism are embedded in global iteration to search through the elite space, which can further improve the ability of global optimization and lead the evolution of algorithm for the better. Simulation examples of different scales verify the effectiveness of the algorithm. The comparison indicates that the composite indicator of this algorithm is superior to that of shuffled frog leaping algorithm (SFLA).