An Effective Oriented Genetic Algorithm for solving redundancy allocation problem in multi-state power systems

The main goal in the design of industrial systems is to improve their reliability. This can be achieved by reducing the complexity of the system by increasing the reliability of its components (reliability allocation) or hardware redundancy (redundancy allocation) or a combination of these two approaches. In this paper, we are interested in solving the redundancy allocation problems RAP in serial-parallel multi-state systems MSS. Our contribution is the Effective Oriented Genetic Algorithm (EOGA) which aims to improve the genetic algorithm by introducing a new parameter which is the degree of relevance of the component, thus allowing a more oriented search in the generation of the initial population, and also the use of specific operators and fitness function, giving better results. In the following, we will cite some errors in the calculations encountered in previous work. Our goal is to improve the redundancy of the system to satisfy a required demand under the constraint of maximizing the reliability of the system and minimizing its cost. To evaluate the reliability, we will hybridize our algorithm with the universal generating function UGF. A comparison with the literature will be discussed at the end of this work showing the effectiveness of our approach, and the significant gap between our results and previous work.