Abstract
In the present study, a novel strategy of Lower-dimensional-Search Algorithm (LDSA) is proposed for solving the complex numerical optimization problems. The crossover operator of the LDSA algorithm searches a lower-dimensional neighbor of the parent points where the neighbor center is the parents’ barycenter, therefore, the new algorithm converges fast. The niche impaction operator and the offspring mutation operator preserve the diversity of the population. The proposed LDSA strategies are applied to 22 test problems. These functions are widely used as benchmark in numerical optimization. The experimental results are reported here show that the LDSA algorithm is an effective algorithm for the complex numerical optimization problems. What’s more is that the LDSA algorithm is simple and easy to be implemented.
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Shi, H. et al. (2007). A Novel Lower-Dimensional-Search Algorithm for Numerical Optimization. In: Kang, L., Liu, Y., Zeng, S. (eds) Advances in Computation and Intelligence. ISICA 2007. Lecture Notes in Computer Science, vol 4683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74581-5_23
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DOI: https://doi.org/10.1007/978-3-540-74581-5_23
Publisher Name: Springer, Berlin, Heidelberg
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