tabu search algorithm
时间: 2023-11-21 18:16:05 浏览: 35
Multi-UAV-Assisted Mobile Edge Computing System是一个研究的对象,该系统中有多个无人机(UAV)与一个地面基站(TBS)相关联,以进行计算卸载。这个系统的设计理念是将移动边缘计算(Mobile Edge Computing,MEC)服务器部署在无线接入网附近,为用户提供丰富多彩的服务,并提供高质量的上网体验。在这个系统中,边缘云的计算资源和通信资源都是有限的,因此需要对计算资源进行合理的分配以提供更好的服务。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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Tabu search algorithm
禁忌搜索算法(Tabu Search Algorithm)是一种亚启发式算法,起源于对人类记忆功能的模仿。它通过从一个初始可行解开始,进行一系列特定搜索方向的试探移动,选择能够使特定目标函数值提升最多的移动。为了避免陷入局部最优解,禁忌搜索算法会记录已经执行过的搜索过程信息,并根据这些信息指导下一步的搜索方向。禁忌搜索算法通过设置禁忌表来禁忌一些曾经执行过的操作,并利用藐视准则来解禁一些优秀的解。这种算法可以应用于多个领域,例如求解车辆路径问题(VRPTW)等。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* [【VRP问题】基于禁忌搜索求解带软时间窗的车辆路径规划TWVRP问题](https://blog.csdn.net/m0_60703264/article/details/120136968)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
- *3* [禁忌搜索算法(Tabu Search)的基本原理与算法流程总结](https://blog.csdn.net/qq_44384577/article/details/105190912)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
[ .reference_list ]
tabu search python
Tabu search is a metaheuristic algorithm used for optimization problems. It is commonly used to find good approximations to the global minimum (or maximum) of a given objective function. In Python, you can implement Tabu search using various libraries, such as NumPy and SciPy. Here's an example implementation of Tabu search in Python:
```python
import numpy as np
def objective_function(x):
# Define your objective function here
return ...
def tabu_search(initial_solution, tabu_list_size, max_iterations):
current_solution = initial_solution
best_solution = initial_solution
tabu_list = []
for _ in range(max_iterations):
neighbors = get_neighbors(current_solution)
best_neighbor = None
for neighbor in neighbors:
if neighbor not in tabu_list and (best_neighbor is None or objective_function(neighbor) < objective_function(best_neighbor)):
best_neighbor = neighbor
if best_neighbor is None:
break
current_solution = best_neighbor
tabu_list.append(current_solution)
if len(tabu_list) > tabu_list_size:
tabu_list.pop(0)
if objective_function(current_solution) < objective_function(best_solution):
best_solution = current_solution
return best_solution
# Example usage
initial_solution = ...
tabu_list_size = ...
max_iterations = ...
best_solution = tabu_search(initial_solution, tabu_list_size, max_iterations)
print("Best solution:", best_solution)
```
You'll need to define your own objective function and the `get_neighbors` function based on the problem you're trying to solve. The `initial_solution` variable represents the starting point for the search, `tabu_list_size` determines the size of the tabu list, and `max_iterations` limits the number of iterations.
Remember to adapt the code to your specific problem and make any necessary modifications.
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