Comprehensive Learning Crow Search Algorithm for Global Optimization

This study presents the Comprehensive Learning Crow Search Algorithm (CL-CSA), a nature-inspired metaheuristic based on the intelligent behavior of crows in hiding and recovering a food source. The core of the CL-CSA is the improved learning strategy, which enables individuals to adaptively update their positions based on the recollection of multiple peers, rather than just one. This learning scheme will control the exploration versus exploitation balance, enabling the swarm to have memories from multiple sources to remember other food sources, which sustains greater diversity and avoids potentially trapping the swarm into local optima. The effectiveness of the proposed CL-CSA has been demonstrated based on complete tests involving a good number of benchmark functions. Several results are comparing the CL-CSA to many recent optimization algorithms and different CSA variants, which show clearly that the proposed framework is very effective for complex optimization problems. In addition, we applied the CL-CSA in real structural optimization problems to evaluate its overall performance and effectiveness in real-world engineering design applications.