On the performance of different mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation problems
This is an incremental improvement for researchers in robotics and optimization, focusing on constrained combinatorial problems with cooperative tasks.
The paper tackled multi-robot task allocation problems by developing a subpopulation-based genetic algorithm using only mutation and selection, finding that inversion mutation performed best for non-cooperative tasks and a swap-inversion combination for cooperative tasks, with the algorithm outperforming classical genetic algorithms.
The performance of different mutation operators is usually evaluated in conjunc-tion with specific parameter settings of genetic algorithms and target problems. Most studies focus on the classical genetic algorithm with different parameters or on solving unconstrained combinatorial optimization problems such as the traveling salesman problems. In this paper, a subpopulation-based genetic al-gorithm that uses only mutation and selection is developed to solve multi-robot task allocation problems. The target problems are constrained combinatorial optimization problems, and are more complex if cooperative tasks are involved as these introduce additional spatial and temporal constraints. The proposed genetic algorithm can obtain better solutions than classical genetic algorithms with tournament selection and partially mapped crossover. The performance of different mutation operators in solving problems without/with cooperative tasks is evaluated. The results imply that inversion mutation performs better than others when solving problems without cooperative tasks, and the swap-inversion combination performs better than others when solving problems with cooperative tasks.