  Constraint satisfaction problem (CSP) has been actively used for modeling and
solving a wide range of complex real-world problems. However, it has been
proven that developing efficient methods for solving CSP, especially for large
problems, is very difficult and challenging. Existing complete methods for
problem-solving are in most cases unsuitable. Therefore, proposing hybrid
CSP-based methods for problem-solving has been of increasing interest in the
last decades. This paper aims at proposing a novel approach that combines
incomplete and complete CSP methods for problem-solving. The proposed approach
takes advantage of the group search algorithm (GSO) and the constraint
propagation (CP) methods to solve problems related to the remote sensing field.
To the best of our knowledge, this paper represents the first study that
proposes a hybridization between an improved version of GSO and CP in the
resolution of complex constraint-based problems. Experiments have been
conducted for the resolution of object recognition problems in satellite
images. Results show good performances in terms of convergence and running time
of the proposed CSP-based method compared to existing state-of-the-art methods.
