A lower-dimensional-search evolutionary algorithm and its application in constrained optimization problems

This paper proposes a new evolutionary algorithm, called lower-dimensional-search evolutionary algorithm (LDSEA). The crossover operator of the new algorithm searches a lower-dimensional neighbor of the parent points where the neighbor center is the barycenter of the parents therefore the new algorithm converges fast, especially for high-dimensional constrained optimization problems. The niche-impaction operator and the mutation operator preserve the diversity of the population to make the LDSEA algorithm not to be trapped in local optima as much as possible. What´s more is that the LDSEA algorithm is simple and easy to be implemented. We have used the 24 constrained benchmark problems [18] to test the LDSEA algorithm. The experimental results show it works better than or competitive to a known effective algorithm [7] for higher-dimensional constrained optimization problems.