Headless Chicken Particle Swarm Optimization Algorithms

This paper investigates various strategies for implementing the headless chicken macromutation operator in the particle swarm optimization domain. Three different headless chicken particle swarm optimization algorithms are proposed and evaluated against a standard guaranteed convergence PSO algorithm on a diverse set of benchmark problems. Competitive performance is demonstrated by a Von Neumann headless chicken particle swarm optimization algorithm when compared to a classic guaranteed convergence particle swarm optimization algorithm. Statistically significantly superior results are obtained over a number of difficult benchmark problems.

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