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This paper is focused on the multi-objective performance optimization of buildings free cooling systems.
They also used a number of multi-objective performance measurements (hypervolume, inverse generational distance and contribution) to compare against other multi-objective algorithms.
Based on the introduced measures in "Multi-objective performance measures", the performance of the proposed algorithms, NSGA-II and NRGA, are evaluated.
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.
Moreover, this model incorporates production, delivery, and demand uncertainty, and provides a multi-objective performance vector for the entire SC network.
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 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.
Moreover, this paper employs the multi-objective performance evaluation model proposed in this paper to analyze the synthesized performance of design chain combinations.
We compare the results to the outcome of a greedy optimization heuristic (magnitude-based pruning) coupled with a multi-objective performance evaluation.
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