On the performance of different mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation problems

TL;DR

This paper evaluates various mutation operators in a subpopulation-based genetic algorithm tailored for complex multi-robot task allocation problems, highlighting the effectiveness of specific mutations depending on task cooperation.

Contribution

It introduces a mutation-only, subpopulation-based genetic algorithm for multi-robot task allocation and compares mutation operators' performance on constrained problems with and without cooperation.

Findings

Inversion mutation outperforms others in non-cooperative tasks.

Swap-inversion combination is most effective for cooperative tasks.

The proposed algorithm surpasses classical genetic algorithms in solution quality.

Abstract

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.