Gray Wolf Optimization (GWO) is a heuristic optimization algorithm based on the social behavior of gray wolves and is used to solve various optimization problems. This article will introduce how to implement the gray wolf optimization algorithm in MATLAB and provide the corresponding source code.
Algorithm overview:
The gray wolf optimization algorithm simulates the social behavior of the gray wolf group, including factors such as the population size of the wolf, the location of the wolf, and the fitness of the wolf. The basic idea of the algorithm is to search for the optimal solution by simulating the four behaviors of wolves (searching, chasing, attacking and updating).
Algorithm steps:
- Initialization parameters: including the number of wolves, number of iterations, search space range, etc.
- Initialize the population: Randomly generate a certain number of gray wolves and assign an initial position to each gray wolf.
- Calculate fitness: Calculate the fitness of each gray wolf based on the problem's specific objective function.
- Update location: Update the location of each gray wolf based on its current location and fitness.
- Search behavior: Simulate the search behavior of the gray wolf and update the location of the gray wolf.
- Chasing behavior: simulate the chasing behavior of the gray wolf and update the position of the gray wolf.
- Attack behavior: simulate the attack behavior of the gray wolf and update the position of the gray wolf.
- Update optimal solution: Update the global optimal solution based on the current position and fitness.
- Repeat steps 4-8 until the specified number of iterations is reached.
The following is the code to implement the gray wolf optimization algorithm in MATLAB:
function [bestPosition, bestFitness]