异构综合学习粒子群算法中低偏差序列的更新速度
Updating velocities in heterogeneous comprehensive learning particle swarm optimization with low-discrepancy sequences
摘要：
异构综合学习粒子群优化算法（HCLPSO）是一种具有增强探索和利用能力的进化算法。低差异序列（LDS）在覆盖搜索空间方面比随机序列更加均匀。本文利用LDS良好的一致性对HCLPSO算法进行了改进研究。数值实验表明，单纯使用LDS生成初始种群不能有效提高HCLPSO的搜索能力。然而，如果我们从HCLPSO速度更新公式中适当地选择一些随机序列，并用确定性LDS代替它们，我们可以得到一个更有效的算法。在相同精度要求下，与原HCLPSO相比，采用确定性LDS更新速度的HCLPSO在不降低成功率的情况下，可以显著减少寻找最优解所需的迭代次数.
英文摘要：
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.
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