Simplified Swarm Optimization for Bi-Objection Active Reliability Redundancy Allocation Problems

TL;DR

This paper introduces a simplified swarm optimization method to effectively solve the bi-objective reliability redundancy allocation problem, balancing reliability and cost in complex system design.

Contribution

A novel simplified swarm optimization algorithm with adaptive mechanisms and penalty functions for bi-objective RRAP, outperforming existing metaheuristics.

Findings

The proposed SSO achieves better Pareto solutions than NSGA-II and MOPSO.

Experimental results on benchmark problems demonstrate the effectiveness of the method.

The approach efficiently balances reliability and cost in system redundancy allocation.

Abstract

The reliability redundancy allocation problem (RRAP) is a well-known tool in system design, development, and management. The RRAP is always modeled as a nonlinear mixed-integer non-deterministic polynomial-time hardness (NP-hard) problem. To maximize the system reliability, the integer (component active redundancy level) and real variables (component reliability) must be determined to ensure that the cost limit and some nonlinear constraints are satisfied. In this study, a bi-objective RRAP is formulated by changing the cost constraint as a new goal, because it is necessary to balance the reliability and cost impact for the entire system in practical applications. To solve the proposed problem, a new simplified swarm optimization (SSO) with a penalty function, a real one-type solution structure, a number-based self-adaptive new update mechanism, a constrained nondominated-solution selection, and a new pBest replacement policy is developed in terms of these structures selected from full-factorial design to find the Pareto solutions efficiently and effectively. The proposed SSO outperforms several metaheuristic state-of-the-art algorithms, e.g., nondominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO), according to experimental results for four benchmark problems involving the bi-objective active RRAP.