ABSTRACT
Two popular swarm intelligence optimization techniques used for robot path planning, motion control, obstacle avoidance, and multi-robot control are described, namely, the artificial bee colony (ABC) and the firefly algorithm (FA). The ABC algorithm mimics the foraging behavior of honeybees, where bees work in different roles, such as employed, onlooker, and scout bees, collaborating to find the best food source. The algorithm iteratively updates potential solutions (representing robot movements) based on the quality of the food source (fitness function), allowing for both exploration (searching new areas) and exploitation (refining good solutions). The FA is based on the flashing behavior of fireflies, where brighter fireflies attract the less bright ones. Each firefly represents a potential solution, and the fireflies move toward brighter (better) solutions based on their relative brightness, gradually converging toward the optimal solution. The ABC algorithm is better suited for solving problems that involve exploring a large search space, thanks to its diverse roles within the bee population. FA is more suitable for solving complex, high-dimensional problems and fine-tuning solutions due to its attraction mechanism.
