Orthogonal simplified swarm optimization for the series–parallel redundancy allocation problem with a mix of components

This work presents a novel orthogonal simplified swarm optimization scheme (OSSO) that combines repetitive orthogonal array testing (ROA), re-initialize population (RIP), and SSO for solving intractable large-scale engineering problems. This scheme is applied to the series–parallel redundancy allocation problem (RAP) with a mix of components. RAP involves setting reliability objectives for components or subsystems in order to meet the resource consumption constraint, e.g., the total cost. RAP has been an active area of research for the past four decades. The difficulties confronted by RAP are to maintain feasibility with respect to three nonlinear constraints, namely, cost-, weight-, and volume-related constraints. As evidence of the utility of the proposed approach, we present extensive computational results on random test problems. The computational results compare favorably with previously developed algorithms in the literature. The results in this paper show that the proposed OSSO can perform excellently in a limited computation time.