Abstract
Differential evolution (DE) has been extensively applied to
continuous problems, its mechanics naturally lending themselves to such.
While some efforts have been made to adapt it to combinatorial problems, these
have largely been problem specific and have not dealt extensively with
constraint handling beyond penalty approaches. In this paper, a simple and
generic strategy, relying on pre-developed heuristic units, is applied to DE and
the generalised assignment problem. In addition, a simple, parameter-free
approach to adapting control parameters is used. The results are competitive
with other well established meta-heuristics. However, there is still scope for
further improvement in the way that DE may be applied to constrained
combinatorial optimisation.
continuous problems, its mechanics naturally lending themselves to such.
While some efforts have been made to adapt it to combinatorial problems, these
have largely been problem specific and have not dealt extensively with
constraint handling beyond penalty approaches. In this paper, a simple and
generic strategy, relying on pre-developed heuristic units, is applied to DE and
the generalised assignment problem. In addition, a simple, parameter-free
approach to adapting control parameters is used. The results are competitive
with other well established meta-heuristics. However, there is still scope for
further improvement in the way that DE may be applied to constrained
combinatorial optimisation.
Original language | English |
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Pages (from-to) | 279-297 |
Number of pages | 19 |
Journal | International Journal of Metaheuristics |
Volume | 1 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2011 |