Robust portfolios are identified by contrasting the multi-objective results attained for (1) historically observed baseline conditions versus (2) future global change scenarios.
The results of the proposed approach show important improvements, i.e., in average, 31.09% (8.43%) and 18.63% (6.09%) of improvement in ROUGE-2 (ROUGE-L) have been obtained with respect to the best single-objective and multi-objective results in the scientific literature.
Compared with the results of the original design, the multi-objective optimization results are more satisfactory, the volume of tube bundle is increased by 6.49%, meanwhile the total annual cost is decreased by 71.46% and exhaust pressure drop is reduced by 27.6%.
Multi-objective optimization results show that the optimum solution depend on the importance of each objective to the final solution.
At the end, the multi-objective optimization results obtained in this paper will be compared with the related results of the asymptotic method and the analytical approach, which are single-objective optimization methods and aimed at increasing the amount of heat transfer from the plates; and very useful and valuable conclusions will be obtained.
Multi-objective optimization results are compared with the current solution to determine if the current solution is non-dominated.
This work presents the results of the multi objective geometric optimization of a phase change material (PCM) based cylindrical heat sink with thermal conductivity enhancers (TCEs) in the form of an internal stem with radial fins.
Tradeoff (Pareto) curves are developed to represent the multi-objective optimization results and tradeoffs.
The multi-objective optimization results indicate an increase in exergy efficiency (56.3%), and decrease in total annual plant cost ($409.3 million).
Multi-objective optimization results using genetic algorithm are reported for a cogeneration system based on indirect integration of solid oxide fuel cell and gas turbine.
First, the ineffectiveness of the Pareto dominance relation, which is the most important criterion in multi-objective optimization, results in the underperformance of traditional Pareto-based algorithms.
