Selection of data products: a hybrid AFSA-MABAC approach

Abstract

With the growing demands of data products, the selection of satellite image data products becomes a challenging decision issue for customers. The objective of this study is to propose a practically sound decision-making approach for solving the satellite image data products selection problems. First, the influencing factors of selecting satellite image data products are identified. Then, hybrid evaluation information is recommended to represent these criteria. That is, numerical and interval-valued quantification is used for quantitative criteria, and picture fuzzy numbers (PFNs) are considered to express qualitative criteria. To reflect decision makers’ preferences, a non-linear optimization is implemented to treat criteria weights with constraints. Thereafter, some penalty functions are defined and the artificial fish swarm algorithm (AFSA) is improved to calculate weight values. Furthermore, six main parameters of AFSA are analyzed. Compared with other commonly used algorithms (such as genetic algorithm (GA) and particle swarm optimization (PSO)), the largest advantage of AFSA is its high robustness of parameters and initial values. Finally, the traditional multi-attributive border approximation area comparison (MABAC) is modified with likelihood measures to obtain the best data product in hybrid evaluation environments. Furthermore, the feasibility and effectiveness of the proposed approach is validated by comparing with existing methods in some representative literature. The results demonstrate that the proposed method is feasible and can provide useful guidelines for the selection and pricing of satellite image data products.