A Hybrid APM-CPGSO Approach for Constraint Satisfaction Problem Solving: Application to Remote Sensing

TL;DR

This paper introduces a novel hybrid approach combining an improved group search algorithm and constraint propagation to efficiently solve complex constraint satisfaction problems in remote sensing applications, notably satellite image object recognition.

Contribution

It presents the first hybridization of an enhanced GSO with CP for solving large, complex CSPs in remote sensing, demonstrating improved performance over existing methods.

Findings

Faster convergence compared to state-of-the-art methods.

Reduced running time in object recognition tasks.

Effective handling of large-scale CSPs in remote sensing.

Abstract

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.