Swarm intelligence algorithm for cancer treating nanorobots: a review

Nanorobots are future in-vivo surgeons that will locomote in human vascular system manoeuvring their specific medical task such as identifying and destroying cancer cells, repairing tissues, clinical trials and so on. Since, they are expected to carry out complex tasks with their simple designs; they need a swarm intelligence system, that will ensure their collaboration, biocompatibility of these nanorobots as well as ability to adapt in dynamic environment in human body. Many Swarm Algorithms have been suggested so far for nanorobot functioning invivo. In this Review, three major swarm algorithms are discussed, namely: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Artificial Bee Colony Optimization (ABC). Many other algorithms are derived from these three algorithms. Their application invivo for Cancer treatment is detailed and major problems based on their design and function, with respect to their collaborated biocompatibility are analysed. Possible solutions are suggested to overcome these problems so that Nanorobots may find their actual application in the field of medicine.