Headless Chicken Guaranteed Convergence Particle Swarm Optimization Algorithms for Improved Diversity in a Dynamically Changing Environment

This paper investigates various strategies for incorporating the headless chicken macromutation operator and the guaranteed convergence particle swarm optimization velocity update into a dynamic particle swarm optimization algorithm. Three different dynamic headless chicken guaranteed convergence particle swarm optimization algorithms are proposed and evaluated on a diverse set of single-objective dynamic benchmark problems. Competitive performance is demonstrated by a Von Neumann headless chicken guaranteed convergence particle swarm optimization algorithm.