A memetic algorithm with simplex crossover for solving constrained optimization problems

In this paper, we propose a new memetic algorithm (MA) for solving constrained optimization problems over continuous search spaces. Our MA is composed by a global search mechanism based on differential evolution (DE), a constraint-handling technique called stochastic ranking (SR) and a local search (LS) procedure which adopts a simplex crossover (SPX) operator. We show that the performance of our algorithm is improved by the influence of its LS mechanism. In order to avoid premature convergence, we adopt a diversity mechanism and a replacement strategy. Our proposal is validated using a set of standard test problems taken from the specialized literature. The results are compared with respect to those produced by three representative algorithms of the state-of-the-art in the area.

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