Reliability optimal allocation is a critical method in engineering and operation fields to improve and ensure product quality. However, conventional model generally uses the single objective with research and development (R & D) cost or product reliability. Moreover, the popular particle swarm optimization (PSO) algorithm solving the model has insufficient global search ability. In this paper, a novel reliability optimal allocation method is developed based on double-objective model and multi-population PSO (MPPSO) algorithm. The cost function is established considering all the cost related to reliability using analytical hierarchical process (AHP) and ordered weighted averaging (OWA) operator. Multi-population particle swarm optimization algorithm is developed based on information exchange mechanism among subpopulations. In the algorithm, the inertia weight is optimized by chaotic map, and different mutation operations are carried out on the subpopulations. An illustrative example, which implements to computer numerical control (CNC) grinding machine, is presented to illustrate the practicality and application of the proposed method. Finally, the comparison and analysis are implemented to show the advantage of proposed model and MPPSO algorithm.
YuHZhangGRanY, et al.A comprehensive and practical reliability allocation method considering failure effects and reliability cost. Eksploat Niezawodn Maintenance Realiability2018; 20: 244–251. DOI: 10.17531/ein.2018.2.09.
2.
ChengQWangHLiuZ, et al.Reliability allocation method based on maximum entropy ordered weighted average and hesitant fuzzy Linguistic term set. J Intelligence Fuzzy Syst2019; 37: 7991–8004. DOI: 10.3233/JIFS-190376.
3.
CatelaniMCianiLPatriziG, et al.Reliability allocation procedures in complex redundant systems. IEEE Syst J2018; 12: 1182–1192. DOI: 10.1109/JSYST.2017.2651161.
4.
CoitDZioE. The evolution of system reliability optimization. Reliability Eng Syst Saf2019; 192: 106259. DOI: 10.1016/j.ress.2018.09.008.
5.
MellalMAZioE. System reliability-redundancy optimization with cold-standby strategy by an enhanced nest cuckoo optimization algorithm. Reliability Eng Syst Saf2020; 201: 106973. DOI: 10.1016/j.ress.2020.106973.
6.
LiuYFanJMuD. Reliability allocation method based on multidisciplinary design optimization for electromechanical equipment. Proc Inst Mech Eng Part C-Journal Mech Eng Sci2015; 229: 2573–2585. DOI: 10.1177/0954406214560597.
7.
YiX-jDhillonBSShiJ, et al.Reliability optimization allocation method for multifunction systems with multistate units based on goal-oriented methodology. SME J Risk Uncertainty Eng Syst Part B-Mechanical Eng2017; 3(4): 1–9. DOI: 10.1115/1.4037123.
8.
YehC-TFiondellaLChangP-C. Cost-oriented component redundancy allocation for a communication system subject to correlated failures and a transmission reliability threshold. Proc Inst Mech Eng Part O: J Risk Reliability2017; 232: 248–261. DOI: 10.1177/1748006X17742778.
9.
LiWZengQShenL, et al.A multi-objective optimization method for system reliability allocation. Adv Mater Res2014; 860-863: 2766–2773. DOI: 10.4028/www.scientific.net/AMR.860-863.2766.
10.
WangWXiongJXieM. Cold-standby redundancy allocation problem with degrading components. Int J Gen Syst2015; 44: 876–888. DOI: 10.1080/03081079.2015.1028541.
11.
GargH. An efficient biogeography based optimization algorithm for solving reliability optimization problems. Swarm Evol Comput2015; 24: 1–10. DOI: 10.1016/j.swevo.2015.05.001.
12.
HuangXCoolenFCoolen-MaturiT. A heuristic survival signature based approach for reliability-redundancy allocation. Reliability Eng Syst Saf2019; 185: 511–517. DOI: 10.1016/j.ress.2019.02.010.
13.
GargHRaniMSharmaSP, et al.Bi-objective optimization of the reliability-redundancy allocation problem for series-parallel system. J Manufacturing Syst2014; 33: 335–347. DOI: 10.1016/j.jmsy.2014.02.008.
14.
SheikhpourSMahaniA. Particle swarm optimization with intelligent mutation for nonlinear mixed-integer reliability-redundancy allocation. Int J Comput Intelligence Appl2017; 16(1): 1750003. DOI: 10.1142/S1469026817500031.
15.
SamantaABasuK. A novel particle swarm optimization with fuzzy adaptive inertia weight for reliability redundancy allocation problems. Intell Decis Technologies-Netherlands2019; 13: 91–99. DOI: 10.3233/IDT-190357.
16.
MousaviSAlikarNTavanaM, et al.An improved particle swarm optimization model for solving homogeneous discounted series-parallel redundancy allocation problems. J Intell Manufacturing2019; 30: 1175–1194. DOI: 10.1007/s10845-017-1311-9.
17.
PathakVKSinghAK. Form error evaluation of noncontact scan data using constriction factor particle swarm optimization. J Adv Manufac Syst2017; 16(03): 205–226. DOI: 10.1142/S0219686717500135.
18.
GangwarSPathakVK. Dry sliding wear characteristics evaluation and prediction of vacuum casted marble dust (MD) reinforced ZA-27 alloy composites using hybrid improved bat algorithm and ANN. Mater Today Commun2020; 25: 101615. DOI: 10.1016/j.mtcomm.2020.101615.
19.
GoyalAGautamNPathakVK. An adaptive neuro-fuzzy and NSGA-II-based hybrid approach for modelling and multi-objective optimization of WEDM quality characteristics during machining titanium alloy. Neural Comp Appl2021congress on evolutionary computation. DOI: 10.1007/s00521-021-06261-7.
20.
SinghRGauravKPathakVK, et al.Best-worst-play (BWP): a metaphor-less optimization algorithm. J Phys Conf Ser2020; 1455: 012007. DOI: 10.1088/1742-6596/1455/1/012007.
21.
PathakVKSinghRGangwarS. Optimization of three-dimensional scanning process conditions using preference selection index and metaheuristic method. Measurement2019; 146: 653–667. DOI: 10.1016/j.measurement.2019.07.013.
22.
KumarSSinghAKPathakVK. Modelling and optimization of injection molding process for PBT/PET parts using modified particle swarm algorithm. Indian J Eng Mater Sci2020; 27(3): 403–415.
23.
Ben GuedriaNHassineH. A swarm intelligence-based approach for reliability-redundancy allocation problems. J Braz Soc Mech Sci2019; 41(7). DOI: 10.1007/s40430-019-1792-x.
24.
ZhangHYangZ. Accelerated particle swarm optimization to solve large-scale network plan optimization of resource-leveling with a fixed duration. Math Probl Eng2018; 2018: 1–11. DOI: 10.1155/2018/9235346.
25.
LinC-JLiT-HSKuoP-H, et al.Integrated particle swarm optimization algorithm based obstacle avoidance control design for home service robot. Comput Electr Eng2016; 56: 748–762. DOI: 10.1016/j.compeleceng.2015.05.019.
26.
AbdiSAfsharKAhmadiS, et al.Optimal recloser and autosectionalizer allocation in distribution networks using IPSO-Monte Carlo approach. Int J Electr Power Energy Syst2014; 55: 602–611. DOI: 10.1016/j.ijepes.2013.10.012.
27.
EriyadiMAbdullahAGMuliaSB, et al.Street lighting efficiency with particle swarm optimization algorithm following indonesian standard. J Phys Conf Ser2019; 1402(4): 044009. DOI: 10.1088/1742-6596/1402/4/044009.
28.
FarivarFShoorehdeliMA. Stability analysis of particle dynamics in gravitational search optimization algorithm. Inf Sci2016; 337: 25–43. DOI: 10.1016/j.ins.2015.12.017.
29.
MirjaliliS. The ant lion optimizer. Adv Eng Softw2015; 83: 80–98. DOI: 10.1016/j.advengsoft.2015.01.010.
30.
MirjaliliS. Sca: a sine cosine algorithm for solving optimization problems. Knowledge-Based Syst2016; 96: 120–133. DOI: 10.1016/j.knosys.2015.12.022.
31.
ShehabMAbualigahLAl HamadHA, et al.Moth–flame optimization algorithm: variants and applications. Neural Comput Appl2020; 32(14): 9859–9884. DOI: 10.1007/s00521-019-04570-6.
32.
MirjaliliSLewisA. The whale optimization algorithm. Adv Eng Software2016; 95: 51–67. DOI: 10.1016/j.advengsoft.2016.01.008.
33.
WangHWangWZhouX, et al.Firefly algorithm with neighborhood attraction. Inf Sci2017; 382: 374–387. DOI: 10.1016/j.ins.2016.12.024.
34.
YanCLLiNZShuJZ. Review of genetic algorithm. Adv Mater Res2011; 179-180: 365–367. DOI: 10.4028/www.scientific.net/AMR.179-180.365.
35.
LiYLiuLYangS, et al.A multi-objective topology optimization methodology and its application to electromagnetic actuator designs. IEEE Trans Magnetics2020; 56(2): 1–4. DOI: 10.1109/TMAG.2019.2952834.
36.
LeungCSKLauHYK. A hybrid multi-objective AIS-based algorithm applied to simulation-based optimization of material handling system. Appl Soft Comput2018; 71: 553–567. DOI: 10.1016/j.asoc.2018.07.034.
37.
AliMMKhompatrapornCZabinskyZB. A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. J Glob Optimization2005; 31(4): 635–672. DOI: 10.1007/s10898-004-9972-2.
38.
MaHSuSSimonDJ, et al.Ensemble multi-objective biogeography-based optimization with application to automated warehouse scheduling. Eng Appl Artif Intelligence2015; 44: 79–90. DOI: 10.1016/j.engappai.2015.05.009.
39.
CarabanteKMPrinyawiwatkulW. Data analyses of a multiple‐samples sensory ranking test and its duplicated test: a review. J Sensory Stud2018; 33(4): e12435. DOI: 10.1111/joss.12435.
40.
CapanuMJonesGARandlesRH. Testing for preference using a sum of Wilcoxon signed rank statistics. Comput Stat Data Anal2006; 51(2): 793–796. DOI: 10.1016/j.csda.2005.12.017.
