Abstract
In this paper a differential evolution technique is proposed in order to tackle continuous optimization problems subject to a set of linear equality constraints, in addition to general non-linear equality and inequality constraints. The idea is to exactly satisfy the linear equality constraints, while the remaining constraints can be dealt with via standard constraint handling techniques for metaheuristics. A procedure is proposed in order to generate a random initial population which is feasible with respect to the linear equality constraints. Then a mutation scheme that maintains such feasibility is defined. The procedure is applied to test-problems from the literature and its performance is also compared with the case where the constraints are handled via a selection scheme or an adaptive penalty technique.
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