A novel evolutionary strategy optimization algorithm for reliability redundancy allocation problem with heterogeneous components

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2024-08-10 DOI:10.1016/j.swevo.2024.101695

A.D. Hesampour , K. Ziarati , S. Zarezadeh

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Abstract

The reliability-redundancy allocation problem (RRAP) is an optimization problem that maximizes system reliability under some constraints. In most studies on the RRAP, either active redundant components or cold standby components are used in a subsystem. This paper presents a new model for the RRAP of a system with a mixed redundancy strategy, in which all components can be heterogeneous. This formulation leads to a more precise solution for the problem; however, RRAP is an np-hard problem, and the new mixed heterogeneous model will be more complicated to solve. After formulating the issue, a novel design of an evolutionary strategy optimization algorithm is proposed to solve that. The problem consists of discrete and continuous variables, and different mutation strategies are designed for each. The new formulation of the problem and the new method for solving it lead to better results than those reported in other recent papers. We implement the new suggested heterogeneous model with the PSO and SPSO algorithms to better compare the proposed algorithm. Results show improvement in both system reliability and fitness evaluation count.

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针对异构组件可靠性冗余分配问题的新型进化策略优化算法

可靠性-冗余分配问题（RRAP）是一个优化问题，可在某些约束条件下最大限度地提高系统可靠性。在有关 RRAP 的大多数研究中，子系统中要么使用主动冗余组件，要么使用冷备用组件。本文为混合冗余策略系统的 RRAP 提出了一个新模型，其中所有组件都可以是异构的。然而，RRAP 是一个 np-hard 问题，新的混合异构模型的求解将更加复杂。在对问题进行表述后，提出了一种新颖的进化策略优化算法设计来解决该问题。问题由离散变量和连续变量组成，并分别设计了不同的突变策略。与近期发表的其他论文相比，新的问题表述和新的求解方法带来了更好的结果。我们用 PSO 和 SPSO 算法实现了新建议的异构模型，以更好地比较建议的算法。结果表明，系统可靠性和适配性评估计数都有所提高。

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来源期刊

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

期刊介绍： Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.