Global Best PSO (GBest PSO): In GBest PSO, each particle updates its position based on the global best solution found by any particle in the entire swarm.
Local Best PSO (LBest PSO): LBest PSO divides the swarm into smaller groups or neighborhoods, and particles update their positions based on the best solution found within their respective neighborhood.
Constriction Coefficient PSO: This variation introduces a constriction factor that helps control the velocity update equation to ensure convergence and stability.
Inertia Weight PSO: Inertia weight PSO adjusts the influence of the previous velocity on the current velocity to balance exploration and exploitation.
Adaptive PSO: Adaptive PSO dynamically adjusts parameters such as inertia weight, acceleration coefficients, or neighborhood sizes during the optimization process to improve performance.
Hybrid PSO: Hybrid PSO combines PSO with other optimization techniques such as genetic algorithms, simulated annealing, or local search methods to enhance its exploration and exploitation capabilities.
Multi-objective PSO (MOPSO): MOPSO extends PSO to handle multi-objective optimization problems by maintaining a set of non-dominated solutions (Pareto front) instead of a single best solution.
Dynamic PSO: Dynamic PSO adapts to changes in the optimization landscape over time by adjusting its parameters or search strategy dynamically.
Quantum PSO (QPSO): QPSO incorporates principles from quantum mechanics to enhance exploration capabilities and improve convergence speed.
Discrete PSO: Discrete PSO modifies the standard PSO algorithm to handle discrete or combinatorial optimization problems by discretizing the search space and applying appropriate update rules.
Chaotic PSO: Chaotic PSO incorporates chaos theory principles to introduce randomness and enhance the exploration capability of the algorithm.
Parallel PSO: Parallel PSO utilizes parallel computing techniques to speed up the optimization process by distributing the computation across multiple processors or threads.
Events are raised when:
Iterations complete
Termination criteria reached