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We propose a new framework of computing-inspired multifocal cancer detectionprocedure (MCDP). Under the rubric of MCDP, the tumor foci to be detected areregarded as solutions of the objective function, the tissue region around thecancer areas represents the parameter space, and the nanorobots loaded withcontrast medium molecules for cancer detection correspond to the optimizationagents. The process that the nanorobots detect tumors by swimming in thehigh-risk tissue region can be regarded as the process that the agents searchfor the solutions of an objective function in the parameter space with someconstraints. For multimodal optimization (MMO) aiming to locate multipleoptimal solutions in a single simulation run, niche technology has been widelyused. Specifically, the Niche Genetic Algorithm (NGA) has been shown to beparticularly effective in solving MMO. It can be used to identify the globaloptima of multiple hump functions in a running, keep effectively the diversityof the population, and avoid premature of the genetic algorithm. Learning fromthe optimization procedure of NGA, we propose the NGA-inspired MCDP in order tolocate the tumor targets efficiently while taking into account realistic invivo propagation and controlling of nanorobots, which is different from the usescenario of the standard NGA. To improve the performance of the MCDP, we alsomodify the crossover operator of the original NGA from crossing within apopulation to crossing between two populations. Finally, we presentcomprehensive numerical examples to demonstrate the effectiveness of theNGA-inspired MCDP when the biological objective function is associated with theblood flow velocity profile caused by tumor-induced angiogenesis.

Nanorobots-assisted Detection of Multifocal Cancer: A Multimodal Optimization Perspective