A modified crow search algorithm based on group strategy and adaptive mechanism

Abstract

ABSTRACT As a swarm-based metaheuristic algorithm, the crow search algorithm (CSA) has attracted a lot of attention owing to its simplicity and flexibility. However, CSA tends to have low efficiency. To improve the optimization efficiency, this article proposes a modified version of CSA based on group strategy with an adaptive mechanism (GCSA). On this basis, crows are divided into multiple competing groups, and are assigned different roles and statuses. Then, the group strategy including different search modes is implemented to increase the solution diversity and search efficiency. Moreover, benefiting from the adaptive mechanism, the search range of crows changes in different stages to balance exploration and exploitation capabilities. To evaluate the performance of the proposed algorithm, 35 benchmark test functions (including 10 CEC2020 functions) and three engineering design problems are solved by GCSA and 11 other algorithms. The results prove that GCSA generally provides more competitive results than other metaheuristic algorithms.