دانلود رایگان مقاله بهینه سازی مسیر تخلیه بر اساس الگوریتم کلونی مورچه کوانتومی

Abstract

Evacuation planning contains more than a few decisions which have to be made in a very short period of time and in the most appropriate way. Evacuation path optimization has vital importance in reducing the human and social harm and saving the aid time. Significant research efforts have been made in the literature to deal with evacuation optimization on the basis of deterministic optimization model, nevertheless the stochastic aspects or uncertainty of real-world evacuation have not been taken into account comprehensively. Inspired by the promising performance of heuristic algorithms to solve combinatorial problems, this paper proposes an improved quantum ant colony algorithm (QACA) for exhaustive optimization of the evacuation path that people can evacuate from hazardous areas to safe areas. In comparison with ACO (ant colony optimization) based method, QACA has the capability of finding a good solution faster using fewer individuals and possesses strong robustness, as a result of the quantum representation and updating of pheromone. Experiment results show that the proposed approach executes more effectively during evacuation.

6. Conclusion

Evacuation planning covers more than a few decisions which have to be made in a very short time and in the most appropriate way. This paper proposes a QACA based evacuation optimization approach. Basic concepts and principles of QEA are introduced into ACO based optimization method, therefore, it is expected to avoid slow convergence and improve efficiency. Simulation results by comparing ACO based and QACA based solutions show that QACA is efficient in solving this problem, and the advantage of QACA based solution tends to expand as the number of iterations increases. Besides, it should be noticed that the research focus is not confined to a single path between two locations (origin–destination), and the proposed method is suitable for multiple source nodes to multiple destination nodes.