In large-scale natural disasters, humans are likely to fail when they attemptto reach high-risk sites or act in search and rescue operations. Robots,however, outdo their counterparts in surviving the hazards and handling thesearch and rescue missions due to their multiple and diverse sensing andactuation capabilities. The dynamic formation of optimal coalition of theseheterogeneous robots for cost efficiency is very challenging and research inthe area is gaining more and more attention. In this paper, we propose a novelheuristic. Since the population of robots in large-scale disaster settings isvery 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 otheralgorithms such as NSGA-II and SPEA-II are carried out. The experimentalresults show that the proposed algorithm outperforms the existing algorithmsnot only in terms of convergence but also in terms of diversity and processingtime.
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