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Genetic algorithm for solving bi-objective redundancy allocation problem with k-out-of-n subsystems

机译：用n出n个子系统解决双目标冗余分配问题的遗传算法

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摘要

Reliability optimization problem is an important type of optimization problems that has many practical applications in the real-world systems such as manufacturing systems, telecommunication systems, transformation systems and electrical systems. This research focuses on redundancy allocation problem (RAP) that is a special type of reliability optimization problems. A bi-objective RAP, which is related to a system of s independent k-out-of-n subsystems in series, is considered in this study. Maximization of the system reliability and minimization of the system cost are the objectives of the problem, and the system is constrained by a predefined weight. The components of a subsystem are supposed to be non identical. To deal with this problem, we propose some multi-objective meta-heuristic algorithms based on the elitist non-dominated sorting genetic algorithm (NSGA-Ⅱ). New modified methods of diversity preservation and constraint handling are introduced in this study. According to these methods and some existing methods, we propose four multi-objective genetic algorithms for solving the considered problem. A numerical example, a statistical method and three performance metrics are utilized for analyzing and comparing the performance of these four genetic algorithms. The comparison represents the positive effect of modified methods of diversity preservation and constraint handling on the performance of the algorithms.
机译：可靠性优化问题是优化问题的一种重要类型，在现实世界的系统中具有许多实际应用，例如制造系统，电信系统，转换系统和电气系统。这项研究的重点是冗余分配问题（RAP），它是可靠性优化问题的一种特殊类型。在这项研究中考虑了一个双目标RAP，该RAP与s个独立的k个n个子系统串联而成。系统可靠性的最大化和系统成本的最小化是问题的目标，并且系统受到预定义的权重的约束。子系统的组件应该是不同的。针对这一问题，提出了基于精英非支配排序遗传算法（NSGA-Ⅱ）的多目标元启发式算法。本研究介绍了新的改进的多样性保存和约束处理方法。根据这些方法和一些现有方法，我们提出了四种多目标遗传算法来解决所考虑的问题。数值示例，统计方法和三个性能指标用于分析和比较这四个遗传算法的性能。比较表明改进的多样性保留和约束处理方法对算法性能的积极影响。

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