  In large-scale natural disasters, humans are likely to fail when they attempt
to reach high-risk sites or act in search and rescue operations. Robots,
however, outdo their counterparts in surviving the hazards and handling the
search and rescue missions due to their multiple and diverse sensing and
actuation capabilities. The dynamic formation of optimal coalition of these
heterogeneous robots for cost efficiency is very challenging and research in
the area is gaining more and more attention. In this paper, we propose a novel
heuristic. Since the population of robots in large-scale disaster settings is
very large, we rely on Quantum Multi-Objective Particle Swarm Optimization
(QMOPSO). The problem is modeled as a multi-objective optimization problem.
Simulations with different test cases and metrics, and comparison with other
algorithms such as NSGA-II and SPEA-II are carried out. The experimental
results show that the proposed algorithm outperforms the existing algorithms
not only in terms of convergence but also in terms of diversity and processing
time.
