The ongoing mutations in the coronavirus (SARS-CoV-2) spike glycoprotein create major challenges for developing vaccines and antiviral treatments. The virus can infect host cells through the receptor binding domain (RBD) of the spike protein, which interacts directly with the ACE2 receptor in human cells. This study introduces a computational method to find conserved patterns in spike glycoprotein sequences from various coronavirus strains. Detection of these conserved sequence patterns in the spike protein is pivotal to understanding the infection and interspecies spread of the virus and the development of a single medication with broad-spectrum efficiency. This study proposes an integrated method which combines diverse optimization techniques to effectively navigate the wide biological sequence search space, as opposed to conventional methods of motif search. Three such hybrid approaches have been tested: GA + PSO, GA + SA, and ACO + PSO. The spike sequences of coronaviruses were obtained from the UniProt database. The goal was to retrieve motifs while dealing with random noise and sequence variability. Experimental results demonstrated that all models successfully retrieved the target motif with comparable mismatch scores. The results illustrate how reliable hybrid metaheuristics are for the recognition of motifs and lay the foundation for future improvements to their scoring methods and scalability. Applications may arise in virology, evolutionary biology, and therapeutic target identification in particular to the development of cross-strain vaccines and antiviral agents.