Pareto front representations for with adaptive density obtained by solving the CONSTR- problem using the ROSE algorithm and the NBI algorithm coupled with posteriori filtering via a SF are depicted, along with a visualization of the solution process of the ROSE algorithm in the objective space.

Pareto front representations for with adaptive density obtained by solving the CONSTR- problem using the ROSE algorithm and the NBI algorithm coupled with posteriori filtering via a SF are depicted, along with a visualization of the solution process of the ROSE algorithm in the objective space.

<p>The stopping criterion of the ROSE algorithm, which aligns with the SF specification, was set to .</p>

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Bibliographic Details
Main Author:	Ihab Hashem (21156806) (author)
Other Authors:	Viviane De Buck (17695851) (author), Seppe Seghers (21956770) (author), Jan Van Impe (5404772) (author)
Published:	2025
Subjects:	Biotechnology Sociology Science Policy Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified two conflicting objectives straightforward parameterization guided reduced computational cost minimizing biomass cost improved without worsening existing deterministic methods computationally efficient method creating denser branches based solution strategy xlink "> bi solution process within pareto front based algorithm &# 8217 objective optimization problems include scalarization techniques objective space recursively pareto front branches within standard scalarization process needs problems produces strategy demonstrates alternative strategy tackles bi solved independently rose ): rich segments points called light energy least one detailed representation balancing exploration algorithm navigates adaptive representation
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