Advancing Biotechnological Applications with a Hybrid Sparrow Search Algorithm: Innovations and Implications

Abstract

This study introduces an enhanced version of the Sparrow Search Algorithm (SSA), termed the Improved Sparrow Search Algorithm (ISSA), which integrates elements of the affinity propagation algorithm to address challenges in optimization processes such as reduced population diversity and premature convergence to local optima. Initially, the ISSA utilizes affinity propagation in the population initialization phase to enrich the diversity of initial solutions, setting a robust foundation for subsequent optimization steps. Additionally, the integration of crossover and variational operators refines the algorithm's convergence capabilities and bolsters its local search proficiency, enabling it to effectively escape local optima and achieve stability in approaching the global optimum. Comparative analyses conducted on 10 benchmark test functions reveal that ISSA significantly outperforms the original SSA and other contemporary intelligent optimization algorithms in terms of convergence speed, search precision, and overall stability. The enhanced features of ISSA are particularly evident in its application to complex biotechnological problems, such as the multiple traveling salesman problem (MTSP), where its superior global search capability demonstrates potential for substantial improvements in operational efficiencies. These advancements underscore the utility of ISSA in biotechnological applications where optimization challenges are prevalent, offering a promising tool for complex problem-solving in genetic sequencing, metabolic pathway analysis, and other critical biotechnological endeavors.