Leapfrog Algorithm Matlab Realization

Leapfrog Algorithm Matlab Realization

Jumping Frog Algorithm (Jumping Frog Algorithm) is a heuristic optimization algorithm based on animal behavior, which simulates the process of frogs catching food. The algorithm is mainly used to solve nonlinear, non-convex, multimodal and constrained optimization problems.

Algorithm flow:

1. Initialize the population, determine the population size, the number of iterations and the amount of food.
2. The frog judges whether it can catch food according to its fitness value, and calculates the energy value.
3. If the food can be caught, the jumping strategy is adopted, otherwise the swimming strategy is adopted.
4. Repeat steps 2 to 3 until the maximum number of iterations is reached or the optimal solution is found.

Matlab implementation:

Here is a simple Matlab program to find the minimum of Rosenbrock's function:

function y = Rosenbrock(x)
% Rosenbrock function
y = sum(100.*(x(2:end)-x(1:end-1).^2).2 + (1-x(1:end-1)).^2);

function [x,fval] = JFA(Rosenbrock)
% Jumping Frog Algorithm
pop_size = 30; % population size
max_iter = 1000; % maximum number of iterations
food_num = 3; % food quantity
dim = 10; % variable dimension

% Initialize population
pop = zeros(pop_size,dim);
for i = 1:pop_size
pop(i,:) = -5 + 10*rand(1,dim); % Randomly generate initial solution
end

% Calculate the fitness value and energy value
fit = zeros(pop_size