用c#编写麻雀搜索算法求最值

以下是使用C#编写麻雀搜索算法求解最值的示例代码：

using System;

class SparrowSearchAlgorithm
{
    static void Main(string[] args)
    {
        int maxIter = 100; // 最大迭代次数
        int popSize = 50; // 种群大小
        double w = 0.5; // 惯性权重
        double c1 = 2.0; // 自我认知学习因子
        double c2 = 2.0; // 社会认知学习因子
        double vmax = 0.5; // 最大速度

        // 定义适应度函数，这里以求解函数f(x)=x^2的最小值为例
        Func<double, double> fitnessFunc = (x) => x * x;

        // 初始化种群
        double[] positions = new double[popSize];
        double[] velocities = new double[popSize];
        double[] pbestPositions = new double[popSize];
        double[] pbestFitness = new double[popSize];
        double gbestPosition = 0.0;
        double gbestFitness = double.MaxValue;
        Random rand = new Random();
        for (int i = 0; i < popSize; i++)
        {
            positions[i] = rand.NextDouble() * 10.0 - 5.0;
            velocities[i] = (rand.NextDouble() * 2.0 - 1.0) * vmax;
            pbestPositions[i] = positions[i];
            pbestFitness[i] = fitnessFunc(positions[i]);
            if (pbestFitness[i] < gbestFitness)
            {
                gbestPosition = pbestPositions[i];
                gbestFitness = pbestFitness[i];
            }
        }

        // 开始迭代
        for (int iter = 0; iter < maxIter; iter++)
        {
            for (int i = 0; i < popSize; i++)
            {
                // 鸟巢筛选
                double fitness = fitnessFunc(positions[i]);
                if (fitness < pbestFitness[i])
                {
                    pbestFitness[i] = fitness;
                    pbestPositions[i] = positions[i];
                    if (pbestFitness[i] < gbestFitness)
                    {
                        gbestPosition = pbestPositions[i];
                        gbestFitness = pbestFitness[i];
                    }
                }

                // 探索行为
                double r1 = rand.NextDouble();
                double r2 = rand.NextDouble();
                velocities[i] = w * velocities[i] + c1 * r1 * (pbestPositions[i] - positions[i]) + c2 * r2 * (gbestPosition - positions[i]);

                // 限制速度
                if (velocities[i] > vmax)
                {
                    velocities[i] = vmax;
                }
                else if (velocities[i] < -vmax)
                {
                    velocities[i] = -vmax;
                }

                // 移动麻雀位置
                positions[i] += velocities[i];

                // 局部搜索
                double r3 = rand.NextDouble();
                double r4 = rand.NextDouble();
                if (r3 < 0.5)
                {
                    positions[i] += r4 * (pbestPositions[i] - positions[i]);
                }
                else
                {
                    positions[i] += r4 * (gbestPosition - positions[i]);
                }
            }
        }

        // 输出结果
        Console.WriteLine("最优解为：{0}", gbestPosition);
        Console.WriteLine("最优值为：{0}", gbestFitness);
    }
}

注意，这里的适应度函数以求解函数f(x)=x^2的最小值为例，实际应用中需要根据具体情况进行修改。另外，麻雀搜索算法的参数设置也需要根据具体问题进行调整。