麻雀优化算法 SSA python实现

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前言

 麻雀优化算法是2020年提出来的，该算法利用麻雀的角色分工和协作机制高效搜索，具有全局优化性能好、寻优性能强的特点，适合与其他技术相融合以改进算法性能。

麻雀优化算法

 SSA是基于麻雀觅食与反捕食行为而模拟的一种生物群智能优化策略，也是一种采用发现者、跟随者和警戒者 3类麻雀觅食行为实现算法位置不断变换迭代的优化模型。
 种群规模为 n的麻雀位置可表示为：

 麻雀觅食位置适应度值的数学表示形式为：

具体过程参考论文：[1]薛建凯. 一种新型的群智能优化技术的研究与应用[D].东华大学,2020.

部分python代码

class SSA():
    def __init__(self, func, n_dim=None, pop_size=20, max_iter=50, lb=-512, ub=512, verbose=True):
        self.func = func
        self.n_dim = n_dim  # dimension of particles, which is the number of variables of func
        self.pop = pop_size  # number of particles
        P_percent = 0.2 # # 生产者的人口规模占总人口规模的20%
        D_percent = 0.1 # 预警者的人口规模占总人口规模的10%
        self.pNum = round(self.pop * P_percent)  # 生产者的人口规模占总人口规模的20%
        self.warn = round(self.pop * D_percent)  # 预警者的人口规模占总人口规模的10%

        self.max_iter = max_iter  # max iter
        self.verbose = verbose  # print the result of each iter or not

        self.lb, self.ub = np.array(lb) * np.ones(self.n_dim), np.array(ub) * np.ones(self.n_dim)
        assert self.n_dim == len(self.lb) == len(self.ub), 'dim == len(lb) == len(ub) is not True'
        assert np.all(self.ub > self.lb), 'upper-bound must be greater than lower-bound'

        self.X = np.random.uniform(low=self.lb, high=self.ub, size=(self.pop, self.n_dim))

        self.Y = [self.func(self.X[i]) for i in range(len(self.X))] # y = f(x) for all particles
        self.pbest_x = self.X.copy()  # personal best location of every particle in history
        self.pbest_y = [np.inf for i in range(self.pop)]  # best image of every particle in history
        self.gbest_x = self.pbest_x.mean(axis=0).reshape(1, -1)  # global best location for all particles
        self.gbest_y = np.inf  # global best y for all particles
        self.gbest_y_hist = []  # gbest_y of every iteration
        self.update_pbest()
        self.update_gbest()
        #
        # record verbose values
        self.record_mode = False
        self.record_value = {'X': [], 'V': [], 'Y': []}
        self.best_x, self.best_y = self.gbest_x, self.gbest_y  # history reasons, will be deprecated
        self.idx_max = 0
        self.x_max = self.X[self.idx_max, :]
        self.y_max = self.Y[self.idx_max]

    def cal_y(self, start, end):
        for i in range(start, end):
            self.Y[i] = self.func(self.X[i])

    def update_pbest(self):
        '''
        personal best
        :return:
        '''
        for i in range(len(self.Y)):
            if self.pbest_y[i] > self.Y[i]:
                self.pbest_x[i] = self.X[i]
                self.pbest_y[i] = self.Y[i]

    def update_gbest(self):
        '''
        global best
        :return:
        '''
        idx_min = self.pbest_y.index(min(self.pbest_y))
        if self.gbest_y > self.pbest_y[idx_min]:
            self.gbest_x = self.X[idx_min, :].copy()
            self.gbest_y = self.pbest_y[idx_min]

测试函数

 采用两格函数测试分别是：Ackley函数和Drop_Wave函数如下图所示：

 Ackley函数最小值为0，Drop_Wave函数最小值为-1.这两个函数都有很多局部最优解。

测试结果

参数设置如下

n_dim =2
lb = [-20 for i in range(n_dim)]
ub = [20 for i in range(n_dim)]
demo_func = test_function.Ackley
pop_size = 100
max_iter = 1000
ssa = SSA(demo_func, n_dim=n_dim, pop_size=pop_size, max_iter=max_iter, lb=lb, ub=ub,verbose=True)
ssa.run()

参数设置如下

n_dim =2
lb = [-20 for i in range(n_dim)]
ub = [20 for i in range(n_dim)]
demo_func = test_function.Drop_Wave
pop_size = 100
max_iter = 1000
ssa = SSA(demo_func, n_dim=n_dim, pop_size=pop_size, max_iter=max_iter, lb=lb, ub=ub,verbose=True)
ssa.run()

结论

 结果还是很好的，函数图像的代码和麻雀算法的源码的获取可以私聊作者。未来将对比一下其他算法，比如粒子群或者灰狼算法。