Swarm controlled emergence for ant clustering

Abstract

Purpose: Swarm controlled emergence is proposed as an approach to control emergent effects in (artificial) swarms. The method involves the introduction of specific control agents into the swarm systems. Control agents behave similar to the normal agents and do not directly influence the behavior of the normal agents. The specific design of the control agents depends on the particular swarm system considered. The aim of this paper is to apply the method to ant clustering. Ant clustering, as an emergent effect, can be observed in nature and has inspired the design of several technical systems, e.g. moving robots, and clustering algorithms. Design/methodology/approach: Different types of control agents for that ant clustering model are designed by introducing slight changes to the behavioural rules of the normal agents. The clustering behaviour of the resulting swarms is investigated by extensive simulation studies. Findings: It is shown that complex behavior can emerge in systems with two types of agents (normal agents and control agents). For a particular behavior of the control agents, an interesting swarm size dependent effect was found. The behaviour prevents clustering when the number of control agents is large, but leads to stronger clustering when the number of control agents is relatively small. Research limitations/implications: Although swarm controlled emergence is a general approach, in the experiments of this paper the authors concentrate mainly on ant clustering. It remains for future research to investigate the application of the method in other swarm systems. Swarm controlled emergence might be applied to control emergent effects in computing systems that consist of many autonomous components which make decentralized decisions based on local information. Practical implications: The particular finding, that certain behaviours of control agents can lead to stronger clustering, can help to design improved clustering algorithms by using heterogeneous swarms of agents. Originality/value: In general, the control of (unwanted) emergent effects in artificial systems is an important problem. However, to date not much research has been done on this topic. This paper proposes a new approach and opens a different research direction towards future control principles for self-organized systems that consist of a large number of autonomous components

Engineering self-organizing urban superorganisms

“…In our opinion, the work on regulated norm-based multiagent systems and electronic institutions (Jones et al, 2013;Pitt et al, 2012) can be an effective starting point towards achieving predictable and controllable behaviors in urban superorganisms. In addition, those works proposing special classes of control agents to be injected in order to act as leader and affect the global behaviors of the multi-agent system (Brintrup et al, 2009;Caprari et al, 2005;Scheidler et al, 2013), can be promising as well.…”

“…To control emergent behaviors, one can think at deploying in the infrastructure special classes of agents that, by spreading "fake" LSAs that have the only goal of triggering some coordination-laws, eventually affecting the way coordination laws apply to other LSAs of other agents (Brintrup et al, 2009;Halloy et al, 2013;Scheidler et al, 2013). The result could be in an overall adaptation of the behavior of the superorganism, yet obtained in a fully decentralized way.…”

Greedy particle swarm and biogeography-based optimization algorithm

“…The simplicity and generality of the PSO algorithm have made it attractive over conventional and evolutionary optimization algorithms. The PSO algorithm has been successfully tested and used on many optimization problems, including travelling salesman problem (Clerc, 2004), phased antenna array synthesis (Boeringer and Werner, 2004), design of digital filters (Ababneh and Bataineh, 2008), physical modelling and design of coplanar waveguide square spiral inductor (Dib and Ababneh, 2008), design of multi-band multi-section transmission line transformer (Khodir et al, 2008), numerical optimization (Xinchao, 2010), Job shop scheduling (Yen and Ivers, 2009), finding RNA secondary structures (Geis and Middendorf, 2011), sending data packets in networks ( Janson et al, 2008), and for Ant clustering (Scheidler et al, 2013).…”