A Comparative Study of Constraint-Handling Techniq
2016-08-23
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Solving constrained multiobjective optimization
problems is one of the most challenging areas in the evolutionary
computation research community. To solve a constrained
multiobjective optimization problem, an algorithm should tackle
the objective functions and the constraints simultaneously. As a
result, many constraint-handling techniques have been proposed.
However, most of the existing constraint-handling techniques
are developed to solve test instances (e.g., CTPs) with low
dimension and large feasible region. On the other hand,
experimental comparisons on different constraint-handling
techniques remain scarce. In view of these two issues, in this
paper we first construct 18 test instances, each of which exhibits
different properties. Afterward, we choose three representative
constraint-handling techniques and combine them with
nondominated sorting genetic algorithm II to study the
performance difference on various conditions. By the
experimental studies, we point o
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