BSP-GA: A new Genetic Algorithm for System Optimization and Excellent Schema Selection

The significance of Internet‐of‐Things to Supply Chain Management has been dramatically increasing. The performance of supply chain based on Internet‐of‐Things is largely dependent on its optimization. Genetic algorithms (GAs) are important intelligent methods for complex system optimization problems, but they have some internal drawbacks such as premature and slow convergence to the global optimum. In this paper, we present a new schema protection based GA (BSP‐GA). First, we propose three principles for selecting excellent schema based on the schema theory; second, we propose the concept of K‐intensive effect synthesis operator, and we give a general five‐intensive effect synthesis operator and its proof; third, we give the selection process of excellent schema through an example, and further we give the implementation steps of BSP‐GA. The performance of BSP‐GA has been compared with simple GA by using two carefully chosen benchmark problems. It has been observed that BSP‐GA can yield the global optimum more efficiently than commonly used simple GA. Furthermore, a theorem is presented to guarantee the convergence of BSP‐GA.