Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multiple criteria classification with an application in water resources planning
Computers and Operations Research
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
An interactive sorting method for additive utility functions
Computers and Operations Research
A territory defining multiobjective evolutionary algorithms and preference incorporation
IEEE Transactions on Evolutionary Computation
A multi-criteria sorting procedure with Tchebycheff utility function
Computers and Operations Research
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
On the computational complexity of reliability redundancy allocation in a series system
Operations Research Letters
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In this study, we consider a bi-objective redundancy allocation problem on a series-parallel system with component level redundancy strategy. Our aim is to maximize the minimum subsystem reliability, while minimizing the overall system cost. The Pareto solutions of this problem are found by the augmented @e-constraint approach for small and moderate sized instances. After finding the Pareto solutions, we apply a well known sorting procedure, UTADIS, to categorize the solutions into preference ordered classes, such as A, B, and C. In this procedure, consecutive classes are separated by thresholds determined according to the utility function constructed from reference sets of classes. In redundancy allocation problems, reference sets may contain a small number of solutions (even a single solution). We propose the @t-neighborhood approach to increase the number of references. We perform experiments on some reliability optimization test problems and general test problems.