Agent Based Models of Competition and Collaboration

Swarm intelligence is a popular paradigm for algorithm design. Frequently drawing inspiration from natural systems, it assigns simple rules to a set of agents with the aim that, through local interactions, they collectively solve some global problem. Current variants of a popular swarm based optimization algorithm, particle swarm optimization (PSO), are investigated with a focus on premature convergence. A novel variant, dispersive PSO, is proposed to address this problem and is shown to lead to increased robustness and performance compared to current PSO algorithms. A nature inspired decentralised multi-agent algorithm is proposed to solve a constrained problem of distributed task allocation. Agents must collect and process the mail batches, without global knowledge of their environment or communication between agents. New rules for specialisation are proposed and are shown to exhibit improved eciency and exibility compared to existing ones. These new rules are compared with a market based approach to agent control. The eciency (average number of tasks performed), the exibility (ability to react to changes in the environment), and the sensitivity to load (ability to cope with differing demands) are investigated in both static and dynamic environments. A hybrid algorithm combining both approaches, is shown to exhibit improved eciency and robustness. Evolutionary algorithms are employed, both to optimize parameters and to allow the various rules to evolve and compete. We also observe extinction and speciation. In order to interpret algorithm performance we analyse the causes of eciency loss, derive theoretical upper bounds for the eciency, as well as a complete theoretical description of a non-trivial case, and compare these with the experimental results. Motivated by this work we introduce agent "memory" (the possibility for agents to develop preferences for certain cities) and show that not only does it lead to emergent cooperation between agents, but also to a signicant increase in efficiency

Techno-economic and uncertainty analysis of Biomass to Liquid (BTL) systems for transport fuel production

This work examines the technical and economic feasibility of Biomass-To-Liquid (BTL) processes for the manufacture of liquid hydrocarbon fuels. Six BTL systems are modelled and evaluated which are based on pressurised oxygen gasification of woody biomass, and specifically on circulating fluidised bed and entrained flow gasification systems. Three fuel synthesis technologies are considered: Fischer-Tropsch synthesis, methanol conversion followed by Methanol to Gasoline (MTG) and the Topsoe Integrated Gasoline (TIGAS) synthesis. Published modelling studies of BTL systems based on gasification have only used deterministic estimates of fuel production costs to assess economic viability without accounting for uncertainties of their model parameters. Unlike other studies, the present techno-economic assessment examines and quantifies the effect of uncertainty of key parameters on the fuel production costs. The results of this analysis show that there is a realistic chance (8–14%) of concepts based on Fischer-Tropsch synthesis meeting the cost of conventional fuels; that this probability could be increased to 50% with moderate tax incentives (an 8% reduction in the tax rate); but that deterministic estimates may be systematically underestimating likely production costs. The overall energy efficiency and production costs of the BTL designs evaluated range from 37.9% to 47.6% LHV and €17.88–25.41 per GJ of produced fuels, respectively. The BTL concept with the lowest production costs incorporates CFB gasification and FT synthesis. The model deterministic estimates of production costs of this design indicate that a BTL process is not yet competitive with conventional refineries since the biofuel production costs are approximately 8% higher than current market prices. Large scale biofuel production may be possible in the long term through subsidies, crude oil price rises and legislation