Multi-objective optimization of a series–parallel system using GPSIA

The optimal solution of a multi-objective optimization problem (MOP) corresponds to a Pareto set that is characterized by a tradeoff between objectives. Genetic Pareto Set Identification Algorithm (GPSIA) proposed for reliability-redundant MOPs is a hybrid technique which combines genetic and heuris...

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Bibliographic Details
Published inReliability engineering & system safety Vol. 103; pp. 61 - 71
Main Authors Okafor, Ekene Gabriel, Sun, You-Chao
Format Journal Article
LanguageEnglish
Published Oxford Elsevier Ltd 01.07.2012
Elsevier
Subjects
Applied sciences
Decision theory. Utility theory
Exact sciences and technology
Genetic algorithm
Genetic Pareto set identification algorithm (GPSIA)
Hybridization
Mathematical programming
Multi-objective optimization
Operational research and scientific management
Operational research. Management science
Reliability theory. Replacement problems
Series-parallel systems
NSGA-II
PEAS
TCS
Hybridization
WBGA
GUB
MOGA
GA
GADSCRRSU
SPEA
DMOEA
VEGA
WSA
PESA
Genetic Pareto set identification algorithm (GPSIA)
EMOO
RDGA
Series-parallel systems
GPSIA
MOO
MOP
Genetic algorithm
NC
POS
SPEA-2
NPGA
RWGA
Multi-objective optimization
NSGA
Costs
Pareto optimum
Redundancy
Multiobjective programming
Series system
Optimization
Genetic Pareto set identification algorithm
Active system
Parallel system
Heuristic method
Cost function
Robustness
Objective function
Reliability
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Summary:The optimal solution of a multi-objective optimization problem (MOP) corresponds to a Pareto set that is characterized by a tradeoff between objectives. Genetic Pareto Set Identification Algorithm (GPSIA) proposed for reliability-redundant MOPs is a hybrid technique which combines genetic and heuristic principles to generate non-dominated solutions. Series–parallel system with active redundancy is studied in this paper. Reliability and cost were the research objective functions subject to cost and weight constraints. The results reveal an evenly distributed non-dominated front. The distances between successive Pareto points were used to evaluate the general performance of the method. Plots were also used to show the computational results for the type of system studied and the robustness of the technique is discussed in comparison with NSGA-II and SPEA-2.
ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2012.03.014