The green supplier selection is one of the popular multiple attribute group decision making (MAGDM) problems. The spherical fuzzy sets (SFSs) can fully express the complexity and fuzziness of evaluation information for green supplier selection. Furthermore, the classic MABAC (multi-attributive border approximation area comparison) method based on the cumulative prospect theory (CPT-MABAC) is designed, which is an optional method in reflecting the psychological perceptions of decision makers (DMs). Therefore, in this article, we propose a spherical fuzzy CPT-MABAC (SF-CPT-MABAC) method for MAGDM issues. Meanwhile, considering the different preferences of DMs to attribute sets, we obtain the objective weights of attributes through entropy method. Focusing on the current popular problems, this paper applies the proposed method for green supplier selection and proves for green supplier selection based on SF-CPT-MABAC method. Finally, by comparing existing methods, the effectiveness of the proposed method is certified.

Biogeography-based optimization (BBO) is a new evolutionary algorithm which mimics the immigration and emigration of species among islands. Used widely in packaging and printing to obtain a colorful appearance, the spot color matching (SCM) is formulated as a complex multi-dimensional optimization problem. In this article, BBO is combined with the harmony search (HS) and opposition-based learning (OBL) approaches to construct an effective hybrid algorithm for solving the SCM problem. HS is used to enhance the local searching ability of BBO, and OBL is employed to increase the diversity of initial population; consequently, the exploration and exploitation abilities of the hybrid algorithm are enhanced and well balanced. Experiment results are presented to show the effectiveness of the proposed scheme.

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