黏菌算法SMA伪代码

以下是黏菌算法SMA的伪代码：

Initialize the slime mold population
Compute the initial fitness of each slime mold
While stopping criterion is not met:
    For each slime mold i in the population:
        Compute the attractiveness of each neighbor j:
            a_i,j = exp(-d_i,j^2/(2*r^2))
            where d_i,j is the distance between slime mold i and neighbor j, and r is the interaction radius
        Compute the aggregation force on slime mold i:
            F_i = 1/n_i * sum(a_i,j*(x_j - x_i))
            where n_i is the number of neighbors of slime mold i, x_j is the position of neighbor j, and x_i is the position of slime mold i
        Update the position of slime mold i:
            x_i = x_i + delta_t*F_i + epsilon_i
            where delta_t is the time step, and epsilon_i is a vector of random values drawn from a normal distribution
        If the new position x_i is outside the search space, reposition it randomly within the search space
        Compute the fitness of the new position x_i
        If the fitness of the new position is better than the fitness of the old position, replace the old position with the new position
        If the fitness of the new position is better than the fitness of the best position in the population, update the best position
    End for
End while

其中，黏菌算法SMA通过模拟黏菌聚集的行为来进行优化。每个黏菌代表一个潜在解，其位置表示解的参数值。黏菌之间通过相互吸引的力量进行聚集，力量的强度由距离和一个交互半径决定。每个黏菌根据其邻居的吸引力和聚集力来更新其位置。在搜索过程中，每个黏菌还有一定概率进行随机游走以增加搜索多样性。最终，算法的输出结果是最优解。