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Performance impact of mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation

Chun Liu¹, Andreas Kroll²

¹School 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.

|September 3, 2016

Summary

This summary is machine-generated.

A new genetic algorithm efficiently solves multi-robot task allocation problems, especially those with cooperative tasks. The study found specific mutation operators, like inversion and swap-inversion, yield superior results for complex industrial inspections.

Keywords:

Constrained combinatorial optimization Genetic algorithms Multi-robot task allocation Mutation operators Subpopulation

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Area of Science:

Robotics
Artificial Intelligence
Operations Research

Background:

Multi-robot task allocation is a complex combinatorial optimization problem.
Cooperative tasks in multi-robot systems introduce significant spatial and temporal constraints.
Existing methods may struggle with the complexity of cooperative task allocation.

Purpose of the Study:

To develop an efficient algorithm for multi-robot task allocation, particularly for cooperative tasks.
To analyze the impact of various mutation operators within a genetic algorithm framework.
To compare the performance of the proposed algorithm against a standard genetic algorithm.

Main Methods:

A subpopulation-based, crossover-free genetic algorithm employing mutation operators and elitism selection was developed.
The algorithm was tested on industrial plant inspection problems, including those with cooperative tasks.
Various mutation operators (swap, insertion, inversion, displacement, and combinations) were analyzed.

Main Results:

The proposed genetic algorithm outperformed a binary tournament genetic algorithm with partially mapped crossover.
Inversion mutation was most effective for non-cooperative tasks.
A combination of swap and inversion mutations proved superior for cooperative tasks.

Conclusions:

The subpopulation-based genetic algorithm is effective for multi-robot task allocation, especially with cooperative constraints.
Mutation operator selection is crucial; combined operators (e.g., swap-inversion) offer enhanced performance.
The findings suggest using multiple mutation operators for similar combinatorial optimization problems.