Abstract
Overlapping coalition formation is a very active research field in multi-agent systems (MAS). In overlapping coalition, each agent can participate in different coalitions corresponding to multiple tasks at the same time. As each agent has limited resources, resource conflicts will occur. In order to resolve resource conflicts, we develop an improved encoding revision algorithm in this paper which can revise an invalid two-dimensional binary encoding into a valid one by checking the encoding for each row. To verify the effectiveness of the algorithm, differential evolution was used as the experimental platform and compared with Zhang et al. The experimental results show that the algorithm in this paper is superior to Zhang et al. in both solution quality and encoding revision time.
Highlights
Distributed intelligent control based on multi-agent system (MAS) is booming(Bristow et al, 2014; Liao et al, 2014; Loia & Vaccaro, 2014; Service & Adams, 2011a; Ye et al, 2013), which is another leap in the development of control science
For the algorithm of Zhang et al (2011), the particle swarm optimization (PSO) algorithm is adopted as the experiment platform, with the particle number being 20 and the maximum particle speed being 6.0
Due to different platforms of the two algorithms, for the sake of fairness, the comparison is made in terms of encoding revision time rather than the total time consumed
Summary
Distributed intelligent control based on multi-agent system (MAS) is booming(Bristow et al, 2014; Liao et al, 2014; Loia & Vaccaro, 2014; Service & Adams, 2011a; Ye et al, 2013), which is another leap in the development of control science. In MAS, when an agent with limited resources meets with some tasks that could not be fulfilled, it will have to make interactive cooperation with other agents in the system for making a team to finish tasks mutually Such a team is called a coalition(Guerrero & Oliver, 2012; Ke et al, 2014; Liu et al, 2013).Coalition formation is the base of all activities in MAS, which will increase the effectiveness of individual agent to accomplish the tasks(He et al, 2017; Rahwan et al, 2012). Some small enterprises with sufficient resources can join in several coalitions to gain more reward through providing services This overlap can increase the utilization of agents resources and the efficiency of tasks fulfillment and the profit of the whole system, which is very popular among wireless network field(Wang et al, 2014; Xu et al, 2016). It is more suitable for the cases in the real world, improving the capability of a single agent to solve the problems in the tasks, and resulting in resource reorganization and complementing advantages mutually
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