Optimal task allocation among the suitably formed robot groups is one of the key issues to be investigated for the smooth operations of multi-robot systems. Considering the complete execution of available tasks, the problem of assigning available resources (robot features) to the tasks is computationally complex, which may further increase if the number of tasks increases. Popularly this problem is known as multi-robot coalition formation (MRCF) problem. Genetic algorithms (GAs) have been found to be quite efficient in solving such complex computational problems. There are several GA-based approaches to solve MRCF problems but none of them have considered the dynamic GA variants. This paper considers immigrants-based GAs viz. random immigrants genetic algorithm (RIGA) and elitism based immigrants genetic algorithm (EIGA) for optimal task allocation in MRCF problem. Further, it reports a novel use of these algorithms making them adaptive with certain modifications in their traditional attributes by adaptively choosing the parameters of genetic operators and terms them as adaptive RIGA (aRIGA) and adaptive EIGA (aEIGA). Extensive simulation experiments are conducted for a comparative performance evaluation with respect to standard genetic algorithm (SGA) using three popular performance metrics. A statistical analysis with the analysis of variance has also been performed. It is demonstrated that RIGA and EIGA produce better solutions than SGA for both fixed and adaptive genetic operators. Among them, EIGA and aEIGA outperform RIGA and aRIGA, respectively.
Correlation clustering based coalition formation for multi-robot task allocation
