ABSTRACT
This chapter describes the bacterial foraging optimization (BFO) and salp swarm algorithm (SSA) used in robotic path planning and navigation. The BFO algorithm mimics the foraging behavior of E. coli bacteria, including chemotaxis (movement toward nutrients), swarming (clustering around food sources), and a reproduction, elimination, and dispersal mechanism. A population of virtual bacteria navigates the search space, updating their positions based on local information about the nutrient (the optimal solution) and adjusting their movement according to chemotaxis, swarming, and reproduction steps. The SSA is motivated by the schooling behavior of salps, where individuals follow a leader and maintain a certain distance from each other while moving in a coordinated manner. A population of salps is represented as points in the search space, with a leader salp guiding the movement of the others. The salps update their positions based on a balance between exploration (random movement) and exploitation (moving toward the best solution found so far). The BFO algorithm tends to excel in exploration due to its random movement during chemotaxis and dispersal phases. At the same time, SSA strikes a good balance between exploration and exploitation by adjusting the influence of the leader salp. The BFO is relatively simpler to implement, while SSA requires more fine-tuning of parameters due to its leader-follower structure. Detailed comparisons are made among the Genetic Algorithm (GA) and particle swarm optimization (PSO) algorithms; the PSO, artificial bee colony (ABC), and ant colony optimization (ACO) algorithms; the firefly, ABC, and ACO algorithms; the BFO, ABC, and ACO algorithms; the firefly and BFO algorithms; and the SSA, ABC, and ACO algorithms.
