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目录
0 引言
1 改进原理
2 本文改进方法
3 改进蛇优化VGG13SE的故障诊断模型
4 结语
0 引言
在【博客】中，我们采用了蛇算法来对VGG13SE网络的15个超参数进行优化，为进一步提高论文的充实性，体现工作量，我们将对原始的蛇算法进行改进，具体是在蛇算法中融入模拟退火的思路。
1 改进原理
我个人认为所有的智能优化算法都是以下步骤组成：
步骤1.初始化。种群随机初始化获得初始最优解与初始最优适应度。
步骤2.寻优过程。进入for循环，利用更新公式对种群中的每个个体进行的位置（解）移动，并计算更新后的解对应的新适应度值。如果新适应度比旧的适应度更优，则更新每个个体的解与其适应度值。
步骤3.最优解更新。将种群的每个个体的适应度与最优适应度进行比较，如果个体的适应度更优，则更新最优适应度与最优解。
步骤4.寻优结束。一般是for结束退出，或者是达到了需要的值退出。
其中步骤3、4是固定的，没办法改。因此常见的改进都是对步骤1与步骤2改进。
改进步骤1的文章认为随机初始化并不是真正的随机，初始种群不会都遍布寻优空间，就跟吃鸡跳伞一样，最理想的情况肯定是整个地图均匀分布，但实际上很多人会集中往某个区域跳，那万一98k就在那些没得人覆盖的地方喃。因此改进步骤1就是用更均匀的方法让初始化解遍布寻优空间。
改进步骤2的一般有四种情况，①发现了原作者的公式存在某些弊端，显然咱只是用一下而已，根本不去管公式啥样，从3月29日写代码到4月2日写到这篇博客，蛇算法我都没看过公式与原理；②针对算法的某些固定的参数进行动态更新，就像吃鸡找装备一样，在空旷的区域可以快速跑动找建筑物，但是进建筑物之后要慢慢走，才能找到好的装备。③给算法加上随机变异，此部分人认为算法在后期可能会陷入局部最优解，就像吃鸡要是一开始方向就跑错了，结果玩半天都只找到一个波波枪，要是能随机将自己传送到其他建筑物里面该多好，说不定就能找到好的装备。④就是继续堆公式，在算法里面加入其他算法的思想，本来人家循环一次已经完成一次更新了，但在后面再加个公式，让其再更新一次，这样当然效果更好，但同时消耗的时间也更长。
2 本文改进方法
本文分别对步骤1与2进行一次改进，步骤1的随机初始化加上Tent映射，让初始化分布更均匀，步骤2在更新完毕之后，再进行一次模拟退火更新。具体代码如下：
'''tent映射'''
def tent(dim):x=np.zeros([dim])x[0]=np.random.rand()#初始点a=0.7 #参数a的值for i in range(dim-1):if x[i]<a:x[i+1]=x[i]/aelse:x[i+1]=(1-x[i]) / (1 - a)return x
''' Tent种群初始化函数 '''
def initialnew(pop, dim, ub, lb):X = np.zeros([pop, dim])for i in range(pop):tentvalue=tent(dim)for j in range(dim):X[i, j] = tentvalue[j]*(ub - lb) + lbreturn X
# 修正后的邻域生成函数
def neighbor(x, solution_bound):perturbation = np.random.uniform(-1, 1, size=x.shape)x_new = x + perturbation# 确保扰动后仍在边界内x_new = np.clip(x_new, solution_bound[0], solution_bound[1])return x_newdef sa_snake_optimization(search_agents_no, max_iter, dim, solution_bound):# 参数初始化T = 1000T_min = 0.01alpha = 0.9vec_flag = [1, -1]food_threshold = 0.25temp_threshold = 0.6c1 = 0.5model_threshold = 0.6c2 = 0.5c3 = 2# 种群初始化（使用numpy数组代替矩阵）#X = solution_bound[0] + np.random.rand(search_agents_no, dim) * (solution_bound[1] - solution_bound[0])X = initialnew(search_agents_no, dim, solution_bound[1] , solution_bound[0]) #初始化种群# 适应度计算fitness = np.array([chung_reynolds(x) for x in X])g_best_idx = np.argmin(fitness)food = X[g_best_idx].copy()gy_best = fitness[g_best_idx]# 性别分组male_number = search_agents_no // 2female_number = search_agents_no - male_numbermale = X[:male_number].copy()female = X[male_number:].copy()# 性别相关参数male_fitness = fitness[:male_number]female_fitness = fitness[male_number:]male_best_idx = np.argmin(male_fitness)male_best = male_fitness[male_best_idx]male_best_solution = male[male_best_idx].copy()female_best_idx = np.argmin(female_fitness)female_best = female_fitness[female_best_idx]female_best_solution = female[female_best_idx].copy()gene_best = np.zeros(max_iter)for t in range(max_iter):temp = math.exp(-t / max_iter)quantity = c1 * math.exp((t - max_iter) / max_iter)quantity = min(quantity, 1)new_male = male.copy()new_female = female.copy()if quantity < food_threshold:# 无食物模式for i in range(male_number):leader = random.choice(male)am = math.exp(-(chung_reynolds(leader) / (male_fitness[i] + 1e-30)))direction = random.choice(vec_flag)new_male[i] = leader + direction * c2 * am * np.random.rand(dim) * (solution_bound[1] - solution_bound[0])for i in range(female_number):leader = random.choice(female)am = math.exp(-(chung_reynolds(leader) / (female_fitness[i] + 1e-30)))direction = random.choice(vec_flag)new_female[i] = leader + direction * c2 * am * np.random.rand(dim) * (solution_bound[1] - solution_bound[0])else:if temp > temp_threshold:# 热态探索for i in range(male_number):direction = random.choice(vec_flag)new_male[i] = food + direction * c3 * temp * np.random.rand(dim) * (food - male[i])for i in range(female_number):direction = random.choice(vec_flag)new_female[i] = food + direction * c3 * temp * np.random.rand(dim) * (food - female[i])else:if random.random() < model_threshold:# 战斗模式for i in range(male_number):fm = math.exp(-female_best / (male_fitness[i] + 1e-30))direction = random.choice(vec_flag)new_male[i] = male[i] + direction * c3 * fm * np.random.rand(dim) * (quantity * male_best_solution - male[i])for i in range(female_number):ff = math.exp(-male_best / (female_fitness[i] + 1e-30))direction = random.choice(vec_flag)new_female[i] = female[i] + direction * c3 * ff * np.random.rand(dim) * (quantity * female_best_solution - female[i])else:# 交配模式for i in range(male_number):mm = math.exp(-female_fitness[i % female_number] / (male_fitness[i] + 1e-30))direction = random.choice(vec_flag)new_male[i] = male[i] + direction * c3 * np.random.rand() * mm * (quantity * female[i % female_number] - male[i])for i in range(female_number):mf = math.exp(-male_fitness[i % male_number] / (female_fitness[i] + 1e-30))direction = random.choice(vec_flag)new_female[i] = female[i] + direction * c3 * np.random.rand() * mf * (quantity * male[i % male_number] - female[i])if random.random() < 0.5:worst_male = np.argmax(male_fitness)new_male[worst_male] = solution_bound[0] + np.random.rand(dim) * (solution_bound[1] - solution_bound[0])worst_female = np.argmax(female_fitness)new_female[worst_female] = solution_bound[0] + np.random.rand(dim) * (solution_bound[1] - solution_bound[0])# 边界处理new_male = np.clip(new_male, solution_bound[0], solution_bound[1])new_female = np.clip(new_female, solution_bound[0], solution_bound[1])# 更新雄性种群for i in range(male_number):new_fit = chung_reynolds(new_male[i])if new_fit < male_fitness[i]:male[i] = new_male[i]male_fitness[i] = new_fit# 更新雌性种群for i in range(female_number):new_fit = chung_reynolds(new_female[i])if new_fit < female_fitness[i]:female[i] = new_female[i]female_fitness[i] = new_fit# 更新全局最优current_male_best = np.min(male_fitness)current_female_best = np.min(female_fitness)gene_best[t] = min(current_male_best, current_female_best)if current_male_best < current_female_best:if current_male_best < gy_best:gy_best = current_male_bestfood[:] = male[np.argmin(male_fitness)]else:if current_female_best < gy_best:gy_best = current_female_bestfood[:] = female[np.argmin(female_fitness)]# 模拟退火if T > T_min:food_new = neighbor(food, solution_bound)f_new = chung_reynolds(food_new)delta = f_new - gy_bestif delta < 0 or np.random.rand() < math.exp(-delta / T):food[:] = food_newgy_best = f_newT *= alphareturn food, gy_best, gene_best
if __name__ == "__main__":# Problem settingsdim = 10search_agents_no = 26bounds = (-100, 100)max_iter = 100initial_temp = 1000cooling_rate = 0.98best_solution2, best_fitness2, fitness_history2 = snake_optimization(search_agents_no, max_iter, dim, bounds)best_solution3, best_fitness3, fitness_history3 = sa_snake_optimization(search_agents_no, max_iter, dim, bounds)# Plot optimization processplt.figure(figsize=(10, 6))plt.plot(fitness_history2, linewidth=2,label='SO')plt.plot(fitness_history3, linewidth=2,label='SASO')plt.legend()plt.title("Optimization Process")plt.xlabel("Iteration")plt.ylabel("Best Fitness")plt.grid(True)plt.show()print("SO最佳解决方案：", best_solution2)print("SO最佳适应度：", best_fitness2)print("SASO最佳解决方案：", best_solution3)print("SASO最佳适应度：", best_fitness3)
SO最佳解决方案： [-7.18304079e-04 2.99050463e-04 4.11682735e-04 9.49958913e-05-3.24086231e-04 1.01366699e-03 -3.25997173e-04 1.86127080e-032.60402360e-04 -5.85704982e-04]
SO最佳适应度： 1.3505001036507069e-11
SASO最佳解决方案： [ 2.46793435e-05 1.43884017e-04 1.11257355e-04 8.70514697e-051.46319530e-05 2.40548315e-04 -3.72922538e-05 -1.02563608e-05-5.73090196e-05 2.23889093e-04]
SASO最佳适应度： 6.5132243165577145e-15
3 改进蛇优化VGG13SE的故障诊断模型
4月3日更新