Set-Based Discrete Particle Swarm Optimization Algorithm

This text presents parameters for a set-based discrete particle swarm optimization algorithm. To execute this algorithm, you need to configure your required parameters. Additionally, consider the following implementation factors: - Population Size: Refers to the number of particles in your population. Initialize using "global popsize" to define the swarm size. In code implementation, this typically determines the matrix dimensions for position and velocity storage. - Velocity Inertia Coefficient: This parameter influences particle velocity and position updates, directly affecting convergence behavior. Consider using random numbers between 0-1 for dynamic adjustment. The inertia weight controls the momentum of particles during optimization. - Cognitive and Social Coefficients: These parameters (c1 and c2) guide particle movement toward personal best and global best positions. Use "global c1" and "global c2" to balance exploration and exploitation. Higher c1 emphasizes individual experience, while higher c2 promotes collective intelligence. - Iteration Count and Current Iteration: These parameters ("global gen" for maximum generations and "global exetime" for execution time) control algorithm duration and solution quality. Proper iteration settings prevent premature convergence or excessive computation. - Maximum Velocity: This constraint limits particle movement speed using "global max_velocity". Velocity clamping prevents solution divergence and maintains search boundary compliance. Additional parameters include population count, optimal solution trajectory tracking, and convergence criteria. These elements help monitor algorithm performance and solution evolution. This information should assist in better understanding and implementing the discrete PSO algorithm effectively.