1·Particle Swarm Optimization (PSO) algorithm has existed premature convergence for multimodal search problems.
粒子群优化(PSO)算法对于多峰搜索问题一直存在早熟收敛问题。
2·The simulative experimental results show that it has obvious improvement in multimodal function optimization problems with the case of the average run time reduced to 56% of the former.
仿真实验的结果也表明该算法在平均运行时间减少了56%的情况下多峰函数的优化效果得到了显著改善。
3·This lays a practical operational foundation for the study of metric universalities and global regularities in arbitrary multimodal maps.
这使得对任意多峰映射的度量普适性和整体规则性的研究有了可操作的现实基础。
4·Referred to the character of particle swarm optimization and immune network theory, an immune particle swarm network algorithm for multimodal function optimization is proposed.
针对多峰函数优化问题，借鉴粒子群优化特性和免疫网络理论，提出一种免疫粒子群网络算法。
5·The varied feature of conditional distributions makes the HMDAR model capable of modeling time series with asymmetric or multimodal distribution.
HMDAR模型分布形式的灵活性使得它能够对具有非对称或多峰分布的序列进行建模。