This algorithm uses mutation mechanism to make particles jump out of the local optimal effectively, and uses the growth process of human to define the particles. Division of growth stages can make particles have different learning factors at different stages.
该算法采用变异机制使得粒子群能有效地跳出局部极值;采用人在社会中扮演的角色以及人的成长过程来定义粒子,通过划分粒子的成长阶段,使得处于不同阶段的粒子采用不同的学习因子。
参考来源 - 粒子群算法的改进及其在基因表达数据聚类中的应用·2,447,543篇论文数据，部分数据来源于NoteExpress
In the study of mutation mechanism, it was found in 6 cases that there were gains and losses of sites due to nucleotide substitution.
在研究突变机制时，发现有6例由于核苷酸的替换引起了限制性位点的增减。
The second mechanism is a more gradual process of adaptive mutation, whereby the capability of the virus to bind to human cells increases during subsequent infections of humans.
第二种机制是一个渐进的适应性变异过程，在这一过程中，伴随人类受到感染，病毒与人体细胞相结合的能力增加了。
The new mathematical results allow calculation of this mechanism when the fitness function and the mutation, recombination and horizontal gene transfer rates are known.
当适应度函数和突变、重组及水平基因转移率已知时，该项新的数学上的结果可以使这一机制运算化。
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