记忆就是产生纳米粒子，然后这些微小的纳米细胞渗入脑细胞中，产生促使记忆大幅增长的化学物质。
The creation of nano-particles that can be consumed where the tiny nano cells would penetrate the cells in the brain to release chemicals that allow for massive increases in memory.
但是粒子群算法没有遗传操作如交叉和变异，而是根据自己的速度来决定搜索。粒子还有一个重要的特点是记忆。
But the particle swarm without the inheritance operation such as cross and variation, decided the searching according to its speed, and particle has an important memory character.
为了加快粒子群算法的收敛速度，论文在传统粒子群算法中引入了记忆机制。
In order to speed up convergence, this paper implants the memory mechanism in the traditional binary PSO.
本文介绍了粒子群优化算法的基本原理，并通过建立记忆表，详尽描述了粒子群优化算法中个体极优和全局极优的搜寻求解过程。
This paper reviews the basic theory, and describes the seeking procedure of the personal best and the global best in PSO through establishing memory table.
本文作者还结合记忆策略、差异进化算法和粒子群优化算法提出记忆进化算法(MCOEA)。
Constrained optimization evolutionary algorithm based on memory, which integrates particle swarm optimization (PSO) with differential evolution (DE), named MCOEA, is proposed.
本文作者还结合记忆策略、差异进化算法和粒子群优化算法提出记忆进化算法(MCOEA)。
Constrained optimization evolutionary algorithm based on memory, which integrates particle swarm optimization (PSO) with differential evolution (DE), named MCOEA, is proposed.
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