Paper Info
Title
A hybrid enhanced bat algorithm for the generalized redundancy allocation problem.
Abstract
A majority of existing works dealing with redundancy allocation problems are based on traditional series-parallel structures. While in many real-life scenarios, the way of connecting subsystems is not limited to a series-only configuration. This paper considers a generalized redundancy allocation problem (GRAP), where the system structure is a more general network. Since the reliability evaluation in GRAPs is a NP-hard problem and the traditional exact symbolic reliability calculation is not suitable, a cellular automata based monte carlo simulation method is implemented in this paper to estimate the system reliability. It is a relatively simple but effective method without knowing the MPs/MCs. Moreover, to deal with GRAPs, a novel discrete bat algorithm is proposed in this paper with a goal of determining an optimal system structure that achieves the minimum cost under several constraints by using redundant components in parallel. Computational complexity of the proposed algorithm is also calculated in this paper. In the end, three experiments are carried out based on ten networks to set parameters, measure the effectiveness of the modifications, and compare with other state-of-the-art algorithms, separately. The reported computational results show that the proposed algorithm is powerful, which is more superior on this sort of problems.
Year
DOI
Venue
2019
10.1016/j.swevo.2019.100562
Swarm and Evolutionary Computation
Keywords
Field
DocType
Generalized redundancy allocation problem,Cellular automata,Monte Carlo simulation,Constriction coefficient,Transfer function,Discrete bat algorithm,Estimation of distribution algorithm with differential perturbation
Cellular automaton,Monte Carlo method,Bat algorithm,Effective method,Computer science,sort,Algorithm,Redundancy (engineering),GRAP,Computational complexity theory
Journal
Volume
ISSN
Citations 
50
2210-6502
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Yue Xu111.36
De-Chang Pi217739.40