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We also develop a heuristic procedure based on data envelopment analysis for characterizing and analyzing the route design choices on the frontier between costs and multi-objective measures of equity.
In order to evaluate the performance of multi-objective algorithms, the algorithms are assessed according to multi-objective measures.
Based on the introduced measures in "Multi-objective performance measures", the performance of the proposed algorithms, NSGA-II and NRGA, are evaluated.
Two multi-objective performance measures (solution spread measure and ratio of non-dominated individuals) are used to evaluate strength of the Pareto optimal fronts.
Two multi-objective performance measures (solution spread measure (SSM) and ratio of non-dominated individuals (RNIs)) are used to evaluate the strength of the Pareto optimal fronts.
Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto optimal fronts.
Several measures are considered for quantification of the stochastic performance, including the standard H2 and multi-objective H2 measures related to the second-order statistics of the response, as well as a measure related to first-passage system reliability.
Further research requires longitudinal and intervention studies, utilizing multi-level design methodologies and objective measures of environmental attributes.
This new paradigm implies a complete change in the optimization algorithm that now natively handles multi objective optimization problems involving vertex-related measures.
Two more multi-objective performance measures namely optimizer overhead (OO) and algorithm effort are used to find the computational effort of MOGA, NSGA-II and MODE algorithms.
Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find the computational effort of NSGA-II and MOPSO algorithms.
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