双目标主动可靠性冗余分配问题的简化群优化算法
Simplified Swarm Optimization for Bi-Objection Active Reliability Redundancy Allocation Problems
摘要：
可靠性冗余分配问题（RRAP）是系统设计、开发和管理中的一个著名工具。RRAP通常被建模为非线性混合整数非确定性多项式时间硬度（NP-hard）问题。为了最大化系统的可靠性，必须确定整数（部件活动冗余水平）和实变量（部件可靠性），以确保满足成本限制和一些非线性约束。在这项研究中，一个双目标RRAP制定通过改变成本约束作为一个新的目标，因为它是必要的，以平衡整个系统的可靠性和成本的影响，在实际应用中。为了解决该问题，基于全因子设计中选取的罚函数、实单型解结构、基于数量的自适应更新机制、约束非支配解选择和pBest替换策略，提出了一种新的简化群优化算法（SSO），以高效地找到Pareto解.所提出的SSO优于几种元启发式最先进的算法，例如，非支配排序遗传算法II（NSGA-II）和多目标粒子群优化（MOPSO），根据实验结果的四个基准问题，涉及双目标主动RRAP。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
