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

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