Growing technology and complexity of industrial processes, have resulted in assuring system expected performance. Intricate systems, high level of failure costs, redesigning and business credit reduction have driven manufacturers to improvement of design and produce phase to reduce the damages caused by the operation phase. Reliability optimization based on the increasing reliability and decreasing costs strategies, is one of the most important issue to researchers. The usual assumption in reliability theory is that any subsystems and components work independently and no one influence the others, while there are many real subsystems and components which effect each other operation due to the their performance level. Hence systems reliability modeling considering correlated components to get closer to reality has become more important. Series-parallel systems reliability optimization based on the redundancy allocation problem (RAP) is studied in this thesis which in the system components are correlated unlike to previous studies. NSGA-II and MOPSO algorithms are used to solve numerical examples of mathematical models, reliability and cost results for correlated component system and independent component system are compared. Metaheuristic algorithms evaluation metrics show that NSGA-II algorithm is more efficient than MOPSO algorithm. .
