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Simplified swarm optimization with modular search for the general multi-level redundancy allocation problem in series-parallel systems

机译:简化了群体搜索串行系统中的常规多级冗余分配问题的模块化优化

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In recent decade, reliability has been an important factor which may affect the performance of the system. In order to enhance the system reliability, redundancy allocation problem (RAP) is becoming an increasingly important tool in the stages of planning, designing, and controlling of systems. Moreover, the multi-level redundancy allocation problem (MRAP) and multiple multi-level redundancy allocation problem (MMRAP) are extensions derived from the redundancy allocation problem (RAP) for practical modeling of real-life problems. However, while formulating the model, the two problems mentioned above have some restrictions which may not deal with real world problem and lost its generality. Therefore, this paper formulates a new kind of MRAP called general multi-level redundancy allocation problem (GMRAP) to break the restrictions and generalize previous problems. Furthermore, a novel algorithm called simplified swarm optimization with modular search (SSO-MS) is proposed to solve the GMRAP in this paper. Finally, the results obtained by SSO-MS are compared with those obtained from genetic algorithm and particle swarm optimization algorithm. The comparative results show that the proposed SSO-MS is promising among three algorithms and demonstrate the effectiveness of the proposed model and method.
机译:近年来,可靠性是可能影响系统性能的重要因素。为了提高系统可靠性,冗余分配问题(RAP)正成为系统规划,设计和控制阶段的越来越重要的工具。此外,多级冗余分配问题(MMRAP)和多个多级冗余分配问题(MMRAP)是从冗余分配问题(RAP)的扩展,用于实际建模的现实问题。然而,在制定模型时,上述两个问题具有一些限制,可能无法处理现实世界问题并失去了普遍性。因此,本文制定了一种称为一般多级冗余分配问题(GMRAP)的新型MRAP,以打破限制并概括先前的问题。此外,提出了一种具有模块化搜索(SSO-MS)的简化群优化的新型算法,以解决本文的GMRAP。最后,将SSO-MS获得的结果与从遗传算法和粒子群优化算法获得的结果进行比较。比较结果表明,所提出的SSO-MS在三种算法中具有很有希望,并证明所提出的模型和方法的有效性。

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