块自适应算法smi代码

块自适应算法（Block Self-Adaptive Algorithm）是一种用于解决无约束组合优化问题的启发式算法。其主要思路是将问题划分为若干个块，每个块内部使用不同的局部搜索算法来寻找最优解，并根据不同块的表现来自适应地调整搜索策略和参数，以达到更优的结果。

以下是块自适应算法的SMI（Selective Memory Insertion）代码实现：

def block_self_adaptive_algorithm():
    # 初始化参数
    blockSize = 10
    maxIteration = 200
    memorySize = 10
    searchMethods = ['hill_climbing', 'simulated_annealing', 'genetic_algorithm']
    bestSolution = None
    bestFitness = float('-inf')
    results = []
    
    # 初始化块
    blocks = []
    for i in range(blockSize):
        block = {'searchMethod': searchMethods[i % len(searchMethods)],
                 'currentSolution': None,
                 'currentFitness': None,
                 'bestSolution': None,
                 'bestFitness': float('-inf'),
                 'memory': []}
        blocks.append(block)
    
    # 开始搜索
    for iteration in range(maxIteration):
        for i in range(blockSize):
            # 在当前块中进行局部搜索
            searchMethod = blocks[i]['searchMethod']
            currentSolution = blocks[i]['currentSolution']
            currentFitness = blocks[i]['currentFitness']
            newSolution, newFitness = perform_local_search(searchMethod, currentSolution, currentFitness)
            
            # 记录搜索结果
            blocks[i]['currentSolution'] = newSolution
            blocks[i]['currentFitness'] = newFitness
            
            if newFitness > blocks[i]['bestFitness']:
                blocks[i]['bestSolution'] = newSolution
                blocks[i]['bestFitness'] = newFitness
            
            # 将搜索结果加入记忆
            memory = blocks[i]['memory']
            memory.append((newSolution, newFitness))
            if len(memory) > memorySize:
                memory.pop(0)
                
            # 从记忆中选择一个好的解作为下次搜索的起点
            solutions = [x[0] for x in memory]
            fitnesses = [x[1] for x in memory]
            maxIndex = fitnesses.index(max(fitnesses))
            blocks[i]['currentSolution'] = solutions[maxIndex]
            blocks[i]['currentFitness'] = fitnesses[maxIndex]
            
        # 记录全局最优解
        for i in range(blockSize):
            if blocks[i]['bestFitness'] > bestFitness:
                bestSolution = blocks[i]['bestSolution']
                bestFitness = blocks[i]['bestFitness']
                
        results.append(bestFitness)
        
    return results

该代码首先初始化了块的参数，然后创建了若干个块，每个块使用不同的搜索算法来进行局部搜索，并将搜索结果记录到记忆中。最后，在每次迭代结束后，从所有块中选出最好的解作为全局最优解，并将其记录在results中。