15,776 research outputs found
Updating velocities in heterogeneous comprehensive learning particle swarm optimization with low-discrepancy sequences
Heterogeneous comprehensive learning particle swarm optimization (HCLPSO) is
a type of evolutionary algorithm with enhanced exploration and exploitation
capabilities. The low-discrepancy sequence (LDS) is more uniform in covering
the search space than random sequences. In this paper, making use of the good
uniformity of LDS to improve HCLPSO is researched. Numerical experiments are
performed to show that it is impossible to effectively improve the search
ability of HCLPSO by only using LDS to generate the initial population.
However, if we properly choose some random sequences from the HCLPSO velocities
updating formula and replace them with the deterministic LDS, we can obtain a
more efficient algorithm. Compared with the original HCLPSO under the same
accuracy requirement, the HCLPSO updating the velocities with the deterministic
LDS can significantly reduce the iterations required for finding the optimal
solution, without decreasing the success rate.Comment: 29 pages, 5 figure
Optimizing Low-Discrepancy Sequences with an Evolutionary Algorithm
International audienceMany elds rely on some stochastic sampling of a given com- plex space. Low-discrepancy sequences are methods aim- ing at producing samples with better space-lling properties than uniformly distributed random numbers, hence allow- ing a more ecient sampling of that space. State-of-the-art methods like nearly orthogonal Latin hypercubes and scram- bled Halton sequences are congured by permutations of in- ternal parameters, where permutations are commonly done randomly. This paper proposes the use of evolutionary al- gorithms to evolve these permutations, in order to optimize a discrepancy measure. Results show that an evolution- ary method is able to generate low-discrepancy sequences of signicantly better space-lling properties compared to sequences congured with purely random permutations
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