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The approach generates the whole set of optimal solutions compared to the one point solution of the deterministic approaches (MINLP) while allowing additional complexity of process level models in the site-wide integration due to the systematic structural decomposition of a system into its basic elements/subsystems.
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Meanwhile, each proposed method tries to reach a more optimal or near optimal solution compared to previous works.
Increasing the number of iterations for each run of the algorithm on the two large-scale problem instances led to obtaining solutions closer to the optimal solution compared to the ones obtained with fewer number iterations.
As it can be observed, SA-PSO and ACO obtained the optimal solutions compared with the other algorithms.
However, even though the statistical test shows that DE-SK is not significantly different compared with the BEE algorithm, the DE-SK is able to obtain more optimal solutions (87.5%) compared to the BEE algorithm that can obtain only 12.5% optimal solutions.
The optimal solutions are compared to those from mono-objective optimization by using an aggregative method and a constraint problem in GenOpt.
The optimal solution is compared to other solutions which appear as natural alternatives to it on the basis of the same value of the total area of heat exchangers and the same total power output.
The longer the reaction chain the longer it takes evolution to find the optimal solution (compare the time scales in Figure 2c and Figure 3a), but the solution is always found.
To assess the quality of the novel solution approach, the quality of its Pareto-optimal solutions is compared to those generated by an existing powerful multi-objective genetic algorithm from the recent literature and to exact solutions obtained by a commercial solver.
The experimental results show that our method can find multiple optimal solutions more efficiently compared to other methods and can be easily scaled up to larger problems.
The heuristics give quite good solutions when compared to the optimal solution.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com