Bio inspired source seeking: a Hybrid Speeding Up and Slowing Down Algorithm
Ayesha Khan, Vivek Kumar Mishra, Fumin Zhang · 2016
A novel bio-inspired strategy, the Hybrid Speeding Up Slowing Down (Hybrid SUSD) strategy, is introduced to achieve distributed control of a multi-agent system for the localization of multiple sources in a search space. Hybrid SUSD switches between bio-inspired exploration algorithms and exploitation algorithms. The exploration algorithms provide coverage of the workspace with non-zero probability. The exploitation algorithms leverage the SUSD strategy for source seeking without explicit gradient estimation. Conditions for switching between exploration and exploitation are developed based on measurements taken by an agent and the number of neighbors an agent may have. Given a confined search space, the convergence of the hybrid SUSD to locate a source is rigorously justified. Simulation results confirm that the strategy allows each agent to converge to one of the source locations. The Hybrid SUSD may be used as a distributed optimization algorithm that is able to find all minima of a function over a confined search space.