搜索资源列表
NSGA
- 多目标遗传算法是NSGA-II[1]（改进的非支配排序算法）,该遗传算法相比于其它的多目标遗传算法有如下优点：传统的非支配排序算法的复杂度为 ，而NSGA-II的复杂度为 ，其中M为目标函数的个数，N为种群中的个体数。引进精英策略，保证某些优良的种群个体在进化过程中不会被丢弃，从而提高了优化结果的精度。采用拥挤度和拥挤度比较算子，不但克服了NSGA中需要人为指定共享参数的缺陷，而且将其作为种群中个体间的比较标准，使得准Pareto域
SSMOPSO code
- 形成策略被用来形成稳定的生态位，小生境/子种群被优化以并行地搜索和维护帕累托最优解。此外，还提出了一种自组织机制，以提高物种制定的效率和算法的性能。为了在决策空间和目标空间保持解决方案的多样性，SS-MOPSO采用了非主导排序方案和特殊拥挤距离技术。(In the proposed method, the speciation strategy is used to form stable niches and these niches
改进PSO算法及其测试函数
- 为了更好地改善多目标粒子群优化算法的收敛性和多样性的pso 算法(In order to improve the convergence and diversity of multi-objective particle swarm optimization algorithm, PSO algorithm is proposed)
Ant Lion Optimizer (ALO)
- ALO 算法核心思想是模拟蚁狮捕猎蚂蚁的狩猎机制以实现全局寻优。蚁狮在捕猎前会在在沙质土中利用其巨大的下颚挖出一个漏斗状的陷阱，并藏在陷阱底部等待猎物到来。一旦随机游走的蚂蚁落入陷阱时，蚁狮迅速将其捕食，随后重新修缮陷阱等待下一次捕猎。(The core idea of the ALO algorithm is to simulate the hunting mechanism of the ant lion hunting ants
Performance Analysis of Intelligent Reflecting Surface Assisted NOMA Networks
- Intelligent reflecting surface (IRS) is a promising technology to enhance the coverage and performance of wireless networks. We consider the application of IRS to non-orthogonal multiple access (NOMA), where a base st
RTR6285TM RF Transceiver IC Device Specification
- This document is part of a set of documents that describes the QUALCOMM RTR6285 RFIC and how best to use it. The RTR6285 device supports quadband UMTS (bands 1 through 6 and bands 8 through 9), quadband GSM/EDGE, GPS,