Parallel genetic algorithm with a knowledge base for a redundancy allocation problem considering the sequence of heterogeneous components

This article presents a new version of the redundancy allocation problem with mixed components (RAPMC) considering the component sequence because it severely affects the reliability of a standby redundant system. It provides a system configuration with exceedingly higher reliability than existing RAPs under same constraints. However, its solution space is significantly expanded according to the number of candidate types and the scale of the system, and thus this study proposed a parallel genetic algorithm with a knowledge base (PGAKB) to efficiently solve it. It includes two strategies, which are the emulation of an expert system and the cooperation between GAs. An individual of the PGAKB creates and exploits the knowledge of the society, and the accumulated knowledge is used for the local search, the final stage for the PGAKB. In conclusion, for solving a complex optimization problem, the PGAKB operates in the form of an expert system and describes a society developing itself by accumulating knowledge. Furthermore, regarding the quality and robustness of solutions and computational time, the effectiveness of the PGAKB was analytically demonstrated by experiments on a famous example.