Multirobot task allocation based on an improved particle swarm optimization approach. (7th June 2017)

• Record Type: Journal Article
• Authors: Zhu, Zhanxia; Tang, Biwei; Yuan, Jianping
• Is Part Of: International journal of advanced robotic systems. Volume 14:Number 3(2017:May/Jun.)
• Publication Date: 2017-06-07
• Subjects: Multirobot task allocation -- improved particle swarm optimization -- evolutionary game theory -- convergence of particle swarm optimization; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881417710312
• Languages: English
• ISSNs: 1729-8806

Abstract

Due to its complexity and non-deterministic polynomial-time hard characteristic, multirobot task allocation problem remains a challenging issue in the field of cooperative robotics. Thanks to its easy implementation and promising convergence speed, the particle swarm optimization method has recently aroused increasing research interest in the area of multirobot task allocation problem. However, the efficiency of the standard particle swarm optimization is hindered by several deficiencies such as the inefficient capabilities in balancing exploration and exploitation, as well as the high likelihood of plunging into stagnation. Aiming at enhancing the performance of particle swarm optimization via remedying these two drawbacks, this paper proposes an improved particle swarm optimization method, which integrates standard particle swarm optimization 2011 with evolutionary game theory. To prevent particles being locked into stagnation, particles in the proposed particle swarm optimization first adopt the updating rules of standard particle swarm optimization 2011 to undertake their movements. Subsequently, attempting to well trade off the exploration and exploitation capabilities of particles, a novel self-adaptive strategy, which is determined by the evolutionary stable strategies of evolutionary game theory and the iteration number of particle swarm optimization, is presented to adaptively adjust the main control parameters of particles in the proposed particle swarm optimization. Since the convergence of particle swarm optimization remains paramount and dramatically affects the performance of particle swarm optimization, this paper also analytically investigates the convergence of the proposed particle swarm optimization and provides a convergence-guaranteed parameter selection principle for the proposed method. Finally, leveraging the development of the proposed particle swarm optimization, this paper completes the design of a new particle swarm optimization–based multirobot task allocation method. The performance of the new particle swarm optimization–based multirobot task allocation method is tested through three different allocation cases against four well-known evolutionary methods. Experimental results confirm that the proposed method generally outperforms its contenders in terms of the solution quality. Moreover, the proposed method performs slightly better than the majority of its peers as far as the computation time is concerned.