xfgh_solution vs xFgh_solution vs xf_solution about metaheuristics.jl HOT 4 CLOSED

Hi!

• is_better(a, b) returns a boolean indicating whether solution a is better than b (minimum objective function value).
• compare(a, b) has been designed for multi-objective problems to compare solutions in objective space (using Pareto dominance). This method returns an integer.

Struct xf_solution is to save decision $x$ and function value $f(x)$ of single-objective unconstrained problems, xfgh_solution for single-objective and constrained. Finally, xFgh_solution for multi-objective problems.

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Thanks a lot for the answer! That makes sense now.

Are multi-objective algorithms are only compatible with multi-objective problems then? E.g. for an objective function which has a programmatically generated set of objectives I would need to use a different algorithm for cases where there is only a single objective than when there are multiple objectives.

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MO algorithms work better for MO problems. They can also solve single-objective problems with a proper definition of an objective function (convert the number fx into a vector [fx]). Of course, the selection of an optimization algorithm is depending on the optimization problem.

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Thank you for clarifying, that seems reasonable.

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