Evolutionary game theory for multiple-mobile-robot flocking systems containing mutations Online publication date: Sat, 22-Apr-2017
by Jingyun Qi; Lei Cheng; Quanmin Zhu; Qiuyue Yu; Huaiyu Wu; Yang Chen
International Journal of Modelling, Identification and Control (IJMIC), Vol. 27, No. 3, 2017
Abstract: This paper studies some major issues encountered in the evolution of strategy choice for a multiple-mobile-robot flocking system that contains mutated individuals. The main objectives of the study are to analyse the effects of mutated strategies on the dynamic performance of the system (i.e., the convergence time and stabilisation time) and to explore the strategy evolution and distributions in flocking movement. Accordingly a payoff matrix directly related to these dynamic performance indicators is designed. The simulation results indicate that the system can achieve common consistency for any given initial conditions, and the final evolution reveals some degree of inherent regularity in the values of the payoff matrix elements. Furthermore, the simulation results confirm the validity of evolutionary game theory to enhance the dynamic performances of a system. The procedure developed from the work can facilitate group-based strategy selection as a creative solution.
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