An self-adaptive Flower Pollination Algorithm for hybrid flowshop scheduling problem

Flower pollination algorithm (FPA) is a relatively new biological evolution methods which based on the process of flower pollinate, that has been used in many optimization areas and achieved good results. In this study, FPA was used to deal with hybrid flow shop optimization problem (HFSP) which is known as typical NP-hard problem. Meanwhile, an effective self-adaptive FPA algorithm (SAFPA) is presented for the global optimization purpose in which a self-adaptive parameter adjusting strategy in terms of stop iterations is proposed to enhance FPA' capacity in escaping from the local optimal when the stagnant situation is met. Finally, computational results and comparisons demonstrate that the FPA can solve the HFSP effectively and the SAFPA can be more quickly converge to the optimal solution than normal FPA.