Comprehensive Particle Swarm Optimization Algorithms

Particle Swarm Optimization (PSO) is a population-based optimization algorithm that simulates the collective behavior of bird flocks or fish schools to search for optimal solutions. While the standard PSO algorithm is simple and efficient, it may underperform on complex optimization problems. Researchers have consequently developed various enhanced versions such as CLPSO, CPSO, and FIPSO to improve algorithmic performance.

CLPSO (Comprehensive Learning PSO) CLPSO enhances population diversity through a comprehensive learning strategy that mitigates traditional PSO's tendency to converge prematurely to local optima. In velocity updates, instead of relying solely on personal best and global best positions, each particle learns from historical best solutions of other particles. Implementation typically involves random selection of exemplars from the population for velocity component updates using a tournament selection mechanism, significantly boosting global exploration capabilities.

CPSO (Chaotic PSO) CPSO incorporates chaotic mapping (e.g., Logistic map) to modulate particle trajectories and prevent premature convergence. The chaotic characteristics introduce higher randomness into particle movement patterns, which enhances exploration - particularly beneficial for multimodal optimization problems. Code implementation often replaces random number generators with chaotic sequences for parameter initialization and inertia weight adjustment.

FIPSO (Fitness Inheritance PSO) FIPSO reduces computational costs through fitness inheritance mechanisms. During fitness evaluation, selected particles inherit fitness values from parent particles or neighboring solutions instead of undergoing costly recalculation. This approach significantly reduces computation time for expensive fitness functions while maintaining optimization quality. Implementation requires designing inheritance probability models and neighborhood selection criteria to balance exploration-exploitation tradeoffs.

These enhanced PSO variants offer distinct characteristics suitable for different optimization scenarios including high-dimensional problems, dynamic environments, and computationally constrained applications. Selecting appropriate variants can substantially improve optimization performance based on problem-specific requirements.