An Angle-Based Bi-Objective Optimization Algorithm for Redundancy Allocation in Presence of Interval Uncertainty

Uncertainty is a practical issue in system design optimization because some characteristics of components, such as reliability and cost, cannot be determined precisely many situations. Considering the imprecise few works have focused on multi-objective for redundancy allocation due to challenges comparing multi intervals. To tackle issue, novel angle-based bi- objective algorithm proposed this study, introducing three original contributions: 1) An interval crowding distance (ICA) especially designed effective performance reduced computational time; 2) Two techniques are applied problem: elite selection mutation presented generating better offsprings; A penalty-guided constraint handling technique introduced converting problem into an unconstrained one. 3) Since set optimal solutions obtained by method no preference uncertainties provided, paper proposes knee help DMs make decision. specific, ICA can describe distribution whole population intuitively effectively, considering not only angle between two compared individuals but also range values. The results from typical experiments demonstrate that more efficient than other state-of-the-art algorithms, Pareto sets with less repeating individuals, stronger convergence, wider distribution, imprecision, time. Note Practitioners—This article motivated problems presence uncertainty: First, tries solve which rarely considered field design. Second, calculation needs extra time cost efficient. designed, embedded most evolutionary algorithms compute diversity individuals. goal study allocate economy high-reliable components practitioners. verify its effectiveness efficiency. Besides, cases practitioners know or preferences, point analysis values allows select solution large hypervolume imprecision among solutions.