title: Performance impact of mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation problems.
abstract: Multi-robot task allocation determines the task sequence and distribution for a group of robots in multi-robot systems, which is one of constrained combinatorial optimization problems and more complex in case of cooperative tasks because they introduce additional spatial and temporal constraints. To solve multi-robot task allocation problems with cooperative tasks efficiently, a subpopulation-based genetic algorithm, a crossover-free genetic algorithm employing mutation operators and elitism selection in each subpopulation, is developed in this paper. Moreover, the impact of mutation operators (swap, insertion, inversion, displacement, and their various combinations) is analyzed when solving several industrial plant inspection problems. The experimental results show that: (1) the proposed genetic algorithm can obtain better solutions than the tested binary tournament genetic algorithm with partially mapped crossover; (2) inversion mutation performs better than other tested mutation operators when solving problems without cooperative tasks, and the swap-inversion combination performs better than other tested mutation operators/combinations when solving problems with cooperative tasks. As it is difficult to produce all desired effects with a single mutation operator, using multiple mutation operators (including both inversion and swap) is suggested when solving similar combinatorial optimization problems.
authors: School of Automation, Beijing University of Posts and Telecommunications, No 10, Xitucheng Road, 100876 Beijing, China ; Department of Measurement and Control, Mechanical Engineering, University of Kassel, Mönchebergstraße 7, 34125 Kassel, Germany.
authors: Chun
authors: 0000-0002-2834-9461
authors: C
authors: Liu
authors: Department of Measurement and Control, Mechanical Engineering, University of Kassel, Mönchebergstraße 7, 34125 Kassel, Germany.
authors: Andreas
authors: A
authors: Kroll
keywords: Constrained combinatorial optimization; Genetic algorithms; Multi-robot task allocation; Mutation operators; Subpopulation