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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Published in | Reliability engineering & system safety Vol. 103; pp. 61 - 71 |
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Main Authors | , |
Format | Journal Article |
Language | English |
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Oxford
Elsevier Ltd
01.07.2012
Elsevier |
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ISSN | 0951-8320 1879-0836 |
DOI | 10.1016/j.ress.2012.03.014 |
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Abstract | 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. |
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AbstractList | 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. |
Author | Okafor, Ekene Gabriel Sun, You-Chao |
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Keywords | 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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SubjectTerms | 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 |
Title | Multi-objective optimization of a series–parallel system using GPSIA |
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