1 research outputs found
Challenges of Convex Quadratic Bi-objective Benchmark Problems
Convex quadratic objective functions are an important base case in
state-of-the-art benchmark collections for single-objective optimization on
continuous domains. Although often considered rather simple, they represent the
highly relevant challenges of non-separability and ill-conditioning. In the
multi-objective case, quadratic benchmark problems are under-represented. In
this paper we analyze the specific challenges that can be posed by quadratic
functions in the bi-objective case. Our construction yields a full factorial
design of 54 different problem classes. We perform experiments with
well-established algorithms to demonstrate the insights that can be supported
by this function class. We find huge performance differences, which can be
clearly attributed to two root causes: non-separability and alignment of the
Pareto set with the coordinate system