A taxonomy of heterogeneity and dynamics in particle swarm optimisation

We propose a taxonomy for heterogeneity and dynamics of swarms in PSO, which separates the consideration of homogeneity and heterogeneity from the presence of adaptive and non-adaptive dynamics, both at the particle and swarm level. It thus supports research into the separate and combined contributions of each of these characteristics. An analysis of the literature shows that most recent work has focussed on only parts of the taxonomy. Our results agree with prior work that both heterogeneity and dynamics are useful. However while heterogeneity does typically improve PSO, this is often dominated by the improvement due to dynamics. Adaptive strategies used to generate heterogeneity may end up sacrificing the dynamics which provide the greatest performance increase. We evaluate exemplar strategies for each area of the taxonomy and conclude with recommendations.

Evolutionary market agents for resource allocation in decentralised systems

We introduce self-interested evolutionary market agents, which act on behalf of service providers in a large decentralised system, to adaptively price their resources over time. Our agents competitively co-evolve in the live market, driving it towards the Bertrand equilibrium, the non-cooperative Nash equilibrium, at which all sellers charge their reserve price and share the market equally. We demonstrate that this outcome results in even load-balancing between the service providers. Our contribution in this paper is twofold; the use of on-line competitive co-evolution of self-interested service providers to drive a decentralised market towards equilibrium, and a demonstration that load-balancing behaviour emerges under the assumptions we describe. Unlike previous studies on this topic, all our agents are entirely self-interested; no cooperation is assumed. This makes our problem a non-trivial and more realistic one.