论文标题
通过低静止序列更新异质综合学习粒子群优化速度
Updating velocities in heterogeneous comprehensive learning particle swarm optimization with low-discrepancy sequences
论文作者
论文摘要
异质综合学习粒子群优化（HCLPSO）是一种具有增强探索和开发能力的进化算法。与随机序列相比，覆盖搜索空间的低静止序列（LDS）更均匀。在本文中，研究了利用LDS的良好均匀性来改善HCLPSO。进行数值实验以表明仅通过使用LDS生成初始群体来有效地提高HCLPSO的搜索能力。但是，如果我们从HCLPSO速度更新公式中正确选择一些随机序列并将其替换为确定性LDS，则可以获得更有效的算法。与原始的HCLPSO在相同的精度要求下相比，使用确定性LDS更新速度的HCLPSO可以显着降低找到最佳解决方案所需的迭代，而不会降低成功率。
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.
