Set-Based Discrete Particle Swarm Optimization Algorithm

Implementation of a set-based discrete particle swarm optimization algorithm with customizable parameters for immediate execution. Key variables include: global popsize (population size), global pop (population matrix), global c1 (cognitive coefficient), global c2 (social coefficient), global gbest_x (global best x-coordinate), and global gbest (global best solution). The algorithm requires proper parameter initialization for optimal performance.

Hybrid Simulated Annealing with Particle Swarm Optimization Algorithm

Implementation of hybrid simulated annealing with particle swarm optimization algorithm. Simply configure the required parameters to execute! For example: global popsize; % Population size %global popnum; % Population number global pop; % Population matrix %global c0; % Velocity inertia coefficient (random value between 0-1) global c1; % Individual best guidance coefficient global c2; % Global best guidance coefficient global gbest_x; % X-coordinate of global best solution global gbest_

Particle Swarm Optimization Algorithm

A nature-selection-based particle swarm optimization algorithm that runs with customizable parameters. For example: global popsize; % population size %global popnum; % population count global pop; % population %global c0; % velocity inertia coefficient (random number between 0-1) global c1; % individual best guidance coefficient global c2; % global best guidance coefficient global gbest_x; % x-coordinate of global best solution global gbest