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:
- Initialize the population, determine the population size, the number of iterations and the amount of food.
- The frog judges whether it can catch food according to its fitness value, and calculates the energy value.
- If the food can be caught, the jumping strategy is adopted, otherwise the swimming strategy is adopted.
- 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