stract Swarm intelligence algorithms are taking the spotlight in the field of function optimization. In this research our attention centers on combining the Particle Swarm Optimization (PSO) algorithm with food foraging behavior of honey bees. The resulting algorithm (called HBF-PSO) 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 environments which are multimodal and dynamic in nature. 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. We also propose variants of the algorithm in which each bee has a different position update equation and we utilize genetic programming (GP) for continuous evolution of these position update equations. This process ensures adaptability and diversity in the swarm which leads to faster convergence and helps to avoid premature convergence. We also explore the use of opposite numbers in our algorithm and incorporate opposition based initialization, opposition based generation jumping and opposition based velocity calculation. The proposed algorithm and its variants are tested on a suite of benchmark optimization problems. In the final portion of our work we report our experiments on the training of feedforward neural networks utilizing our proposed algorithms.