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B. Khumawala, Marvin Arostegui (1997)
An empirical comparison of tabu search, simulated annealing, and genetic algorithms for facilities location problemsInternational Journal of Production Economics, 103
C. Fonseca, P. Fleming (1995)
An Overview of Evolutionary Algorithms in Multiobjective OptimizationEvolutionary Computation, 3
P. Hajela, Chyi-Yeu Lin (1991)
Genetic search strategies in multicriterion optimal designStructural optimization, 4
M. Zohdy (2018)
Genetic algorithmsRobust Control Systems with Genetic Algorithms
Lou Liang, R. Thompson, D. Young (2004)
Optimising the design of sewer networks using genetic algorithms and tabu searchEngineering, Construction and Architectural Management, 11
R. Bhattacharya, S. Bandyopadhyay (2010)
Solving conflicting bi-objective facility location problem by NSGA II evolutionary algorithmThe International Journal of Advanced Manufacturing Technology, 51
E. Zitzler, K. Deb, L. Thiele (2000)
Comparison of Multiobjective Evolutionary Algorithms: Empirical ResultsEvolutionary Computation, 8
K. Deb (1999)
Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test ProblemsEvolutionary Computation, 7
M. Agarwal, Sudhanshu Aggarwal, V. Sharma (2010)
Optimal redundancy allocation in complex systemsJournal of Quality in Maintenance Engineering, 16
C. Romero, M. Tamiz, Dylan Jones (1998)
Goal programming, compromise programming and reference point method formulations: linkages and utility interpretationsJournal of the Operational Research Society, 49
J. Sadeghi, S. Sadeghi, S. Niaki (2014)
A hybrid vendor managed inventory and redundancy allocation optimization problem in supply chain management: An NSGA-II with tuned parametersComput. Oper. Res., 41
O. Jadaan, Lakishmi Rajamani, C. Rao (2008)
NON-DOMINATED RANKED GENETIC ALGORITHM FOR SOLVING MULTI-OBJECTIVE OPTIMIZATION PROBLEMS: NRGA, 2
A. Rao, P. Shyju (2010)
A Meta‐Heuristic Algorithm for Multi‐Objective Optimal Design of Hybrid Laminate Composite StructuresComputer‐Aided Civil and Infrastructure Engineering, 25
Yi-Hui Liang (2008)
Combining neural networks and genetic algorithms for predicting the reliability of repairable systemsInternational Journal of Quality & Reliability Management, 25
H. Aytug, C. Saydam (2002)
Solving large-scale maximum expected covering location problems by genetic algorithms: A comparative studyEur. J. Oper. Res., 141
Randall Sexton, R. Dorsey, John Johnson (1999)
Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealingEur. J. Oper. Res., 114
J. Dorn, Mario Girsch, Günther Skele, W. Slany (1996)
Comparison of iterative improvement techniques for schedule optimizationEuropean Journal of Operational Research, 94
H. Youssef, S. Sait, H. Adiche (2001)
Evolutionary algorithms, simulated annealing and tabu search: a comparative studyEngineering Applications of Artificial Intelligence, 14
N. Srinivas, K. Deb (1994)
Muiltiobjective Optimization Using Nondominated Sorting in Genetic AlgorithmsEvolutionary Computation, 2
Quan-Ke Pan, Rubén Ruiz (2013)
A comprehensive review and evaluation of permutation flowshop heuristics to minimize flowtimeComput. Oper. Res., 40
K. Deb, S. Agrawal, Amrit Pratap, T. Meyarivan (2002)
A fast and elitist multiobjective genetic algorithm: NSGA-IIIEEE Trans. Evol. Comput., 6
Yuji Nakagawa, S. Miyazaki (1981)
An Experimental Comparison of the Heuristic Methods for Solving Reliability Optimization ProblemsIEEE Transactions on Reliability, R-30
Purpose – The purpose of this paper is to present a real world application of an innovative hybrid system reliability optimization algorithm combining Tabu search with an evolutionary algorithm (TSEA). This algorithm combines Tabu search and Genetic algorithm to provide a more efficient search method. Design/methodology/approach – The new algorithm is applied to an aircraft structure to optimize its reliability and maintain its structural integrity. For retrofitting the horizontal stabilizer under severe stall buffet conditions, a decision support system (DSS) is developed using the TSEA algorithm. This system solves a reliability optimization problem under cost and configuration constraints. The DSS contains three components: a graphical user interface, a database and several modules to provide the optimized retrofitting solutions. Findings – The authors found that the proposed algorithm performs much better than state-of-the-art methods such as Strength Pareto Evolutionary Algorithms on bench mark problems. In addition, the proposed TSEA method can be easily applied to complex real world optimization problem with superior performance. When the full combination of all input variables increases exponentially, the DSS become very efficient. Practical implications – This paper presents an application of the TSEA algorithm for solving nonlinear multi-objective reliability optimization problems embedded in a DSS. The solutions include where to install doublers and stiffeners. Compromise programming is used to rank all non-dominant solutions. Originality/value – The proposed hybrid algorithm (TSEA) assigns fitness based upon global dominance which ensures its convergence to the non-dominant front. The high efficiency of this algorithm came from using Tabu list to guidance the search to the Pareto-optimal solutions.
International Journal of Structural Integrity – Emerald Publishing
Published: Dec 7, 2015
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