Hybrid Simulated Annealing with Particle Swarm Optimization Algorithm

In your code implementation, we can effectively combine simulated annealing with particle swarm optimization. You only need to configure the necessary parameters to execute the algorithm. For instance, you can set parameters such as: population size (global popsize), population number (global popnum), population matrix (pop), velocity inertia coefficient (global c0), individual best guidance coefficient (global c1), global best guidance coefficient (global c2), x-coordinate of global best solution (gbest_x), y-coordinate of global best solution (gbest_y), optimal solution value (best_fitness), trajectory of optimal solution changes (best_in_history), lower bound of x (x_min), upper bound of x (x_max), lower bound of y (y_min), upper bound of y (y_max), maximum iterations (gen), current iteration count (exetime), and maximum velocity (max_velocity). These parameters enable proper initialization and control of the hybrid algorithm's execution flow, helping you achieve more accurate optimization results through balanced exploration (simulated annealing's temperature mechanism) and exploitation (particle swarm's social learning).