Shim–Suh–Furukawa–Yagawa–Yoshimura (SSFYY)
This family is represented by the SSFYY2 constructor. It implements Test problem 2, Equation (24), from “Pareto‐based continuous evolutionary algorithms for multiobjective optimization” [32]. The package currently provides no constructors for the paper's other test problems.
Overview
SSFYY2 has fixed dimensions and is unconstrained.
| Problem | Source test | nvar | nobj | Registered bounds | Recommended working box |
|---|---|---|---|---|---|
SSFYY2 | 2 | 1 | 2 | None | $[-100,100]$ |
The source says that the variable is initialized in $[-100,100]$ for its numerical experiment; it does not state this interval as a feasibility constraint. Accordingly, SSFYY2 does not register variable bounds. The same interval is recommended when reproducing the source's initialization setup.
An analytical Jacobian is registered. Hessians are not registered. The catalog metadata classifies $f_1$ as not strictly convex (:not_strictly_convex) and $f_2$ as strictly convex (:strictly_convex).
Mathematical formulation
Let $F:\mathbb{R}\to\mathbb{R}^2$ be defined by $F(x)=(f_1(x),f_2(x))$, where $x=(x_1)$. The constructor implements
\[\begin{aligned} f_1(x) &= 10+x_1^2-10\cos\left(\frac{\pi x_1}{2}\right),\\ f_2(x) &= (x_1-4)^2. \end{aligned}\]
Usage
julia> using MOProblems
julia> using Random
julia> prob = SSFYY2();
julia> lower, upper = recommended_bounds(prob);
julia> rng = MersenneTwister(1234);
julia> α = rand(rng, prob.nvar);
julia> x = lower .+ α .* (upper .- lower);
julia> values = eval_f(prob, x);
julia> J = eval_jacobian(prob, x);
julia> (length(values), size(J))
(2, (2, 1))Constructor reference
MOProblems.SSFYY2 — Function
SSFYY2()Construct the fixed one-variable, two-objective SSFYY2 problem.
The problem has no explicit variable bounds. An analytical Jacobian is registered; objective Hessians are not registered.