Swarm intelligence algorithms are taking the spotlight in the field of function optimization. In this book our attention centers on a new framework inspired from the food foraging behavior of honey bees. Utilizing the Particle Swarm Optimization (PSO) algorithm within this framework we have developed a novel algorithm called Honey Bee Foraging Particle Swarm Optimization (HBF-PSO). The HBF-PSO algorithm and its variants are suitable for solving multimodal and dynamic optimization problems. We focus on the niching and speciation capabilities of these algorithms which allow them to locate and track multiple peaks in multimodal and dynamic environments. The HBF-PSO algorithm performs a collective foraging for fitness in promising neighborhoods in combination with individual scouting searches in other areas. The strength of the algorithm lies in its continuous monitoring of the whole scouting and foraging process with dynamic relocation of the bees (solution/particles) if more promising regions are found. Those looking for a novel approach to function optimization utilizing the food foraging behavior of honey bees can benefit from the information presented in this book.