In principle, a multi-objective optimization problem (MOOP) provides a group of non-dominated solutions (popularly known as Pareto-optimal solutions) for the decision maker (DM). A DM is undecidable to claim one of these solutions to be better than another in the absence of any further information. Due to this reason, a DM needs as many Pareto-optimal solutions as possible. Classical optimization methods are unable to produce multiple solutions at a time because of converting the MOOP to a single-objective optimization problem (SOOP). In the past decades, multi-objective evolutionary algorithms (MOEAs) have been developed to be powerful techniques of identifying a complete picture of the Pareto-optimal solutions space, where a DM can select one out of these solutions according to his/her preference. Moreover, a more efficient MOEA can exploit the search in a better position if the DM provides some general views or ideas about the solution in terms of reference points or weights. Reference point based NSGA-II (R-NSGA-II) is such type of an MOEA where DM’s assigned reference points are used to search the solutions and its diversity is controlled efficiently. This paper presents the applicability of the R-NSGA-II algorithm to the system reliability design problem. The simulation results show the advantage of R-NSGA-II over NSGA-II.

Multi-objective optimization problem (MOOP), Multi-objective evolutionary algorithms (MOEAs), Reference points, System reliability, Pareto-optimal front (POF)

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