Tabu Search     

        Tabu search, created by Fred W. Glover in 1986 and formalized in 1989 is a meta-heuristic search method employing local search methods used for mathematical optimization. Tabu search is the enhanced version of local searches by relaxing its basic rules. Local searches always refuse any solution that is no better than the history best one, so local search algorithm can easily get trapped in the local optimal solution. Instead, tabu search can accept the worsening moves at each step if no improving move is available. In addition, prohibitions are introduced to discourage the search from coming back to previously-visited solutions. 

# -*- coding: utf-8 -*-"""Created on 2017/3/15 12:15 2017@author: Randolph.Lee"""import numpy as npimport randomimport copyclass TabuSearch:    def __init__(self, max_len, graph, total_nodes):        self.max_len = max_len        self.graph_mat = graph        self.node_num = total_nodes        self.current_solution = range(total_nodes)        random.shuffle(self.current_solution)        self.best_solution = []        self.aspiration = 0.0        self.candidates = []        self.fit_list = []        self.tabu_record = []        self.tabu_list = []    def cal_aspiration(self, solution):        current_aspiration = 0.0        for i in solution[:-1]:            for j in solution[1:]:                current_aspiration += self.graph_mat[i, j]        return current_aspiration    def initialize_aspiration(self):        self.aspiration = self.cal_aspiration(self.current_solution)        self.best_solution = copy.deepcopy(self.current_solution)    # generate candidate set based on the current solution    def generate_candidates(self):        random_num = random.randint(0, self.node_num - 1)        neighbor_base = copy.deepcopy(self.current_solution)        element = self.current_solution[random_num]        del neighbor_base[random_num]        temp_candidates = [neighbor_base[:i] + [element] + neighbor_base[i:] for i in range(self.node_num)]        self.candidates = filter(lambda x: x != self.current_solution, temp_candidates)        self.tabu_record = [(item[item.index(element)-1], element) if item.index(element) == self.node_num-1 else (element, item[item.index(element)+1]) for item in self.candidates]        self.fit_list = [self.cal_aspiration(item) for item in self.candidates]    def update_tabulist(self, selected_record):        if len(self.tabu_list) == self.max_len:            del self.tabu_list[0]        self.tabu_list.append(selected_record)    def relieve_ban(self, fitness):        if fitness < self.aspiration:            sign = True            self.aspiration = fitness        else:            sign = False        return signif __name__ == "__main__":    max_len = 4    max_iteration = 20    total_nodes = 8    graph_mat = abs(np.random.randn(8, 8))    tabu_search = TabuSearch(max_len, graph_mat, total_nodes)    tabu_search.initialize_aspiration()    res = []    i = 0    while i < max_iteration:        # produce the candidates        tabu_search.generate_candidates()        # rank the fitness        fit_rank = np.array(tabu_search.fit_list).argsort().tolist()        move_times = 0        if tabu_search.relieve_ban(tabu_search.fit_list[fit_rank[0]]):            tabu_search.update_tabulist(tabu_search.candidates[fit_rank[0]])            tabu_search.best_solution = copy.deepcopy(tabu_search.candidates[fit_rank[0]])        else:            while tabu_search.tabu_record[fit_rank[move_times]] in tabu_search.tabu_list:                move_times += 1        if move_times > len(tabu_search.candidates):            break        else:            tabu_move = tabu_search.tabu_record[fit_rank[move_times]]            tabu_search.update_tabulist(tabu_move)        res.append((i, tabu_search.aspiration))        print "iteration ", i        i += 1    print tabu_search.best_solution

                                             
        0        0           
	
				
	
					   
Tabu Search
	  	   
禁忌搜索算法(Tabu Search)
	  	   
禁忌搜索（Tabu Search或Taboo Search，简称TS）
	  	   
Finding a feasible course schedule using Tabu search(1)论文翻译
	  	   
Finding a feasible course schedule using Tabu search(2)论文翻译
	  	   
Finding a feasible course schedule using Tabu search(3)论文翻译
	  	   
tabu禁忌搜索
	  	   
通用局部搜索算法之tabu搜索（转）
	  	   
AI中的几种搜索算法---Tabu搜索算法
	  	   
AI中的几种搜索算法---Tabu搜索算法
	  	   
Search
	  	   
search
	  	   
search
	  	   
search
	  	   
search
	  	   
Search
	  	   
search
	  	   
Search
	     		  
	  	   
JAVA二进制的读写
	  	   
简单爬取百度贴吧中的美图
	  	   
欢迎使用CSDN-markdown编辑器
	  	   
xml的简单读写应用
	  	   
UNIX常用命令行指令(8)(变量)
	  	   
Tabu Search
	  	   
nginx开启pathinfo模式的两种方式
	  	   
java开发微信自动回复文本消息换行的问题。
	  	   
正则表达式（模拟网络爬虫、网络蜘蛛）
	  	   
触发器模板
	  	   
2-006. 树的遍历
	  	   
编译busybox
	  	   
【折半枚举】
	  	   
C