Parametric Optimisation is an important problem that can be tackled with a range of bio-inspired problem space search algorithms. We show how a simplified Particle Swarm Optimiser (PSO) can efficiently exploit advanced space exploration with Lévy flights, Rayleigh flights and Cauchy flights, and we discuss hybrid variations of these. We present implementations of these methods and compare algorithmic convergence on several multi-modal and unimodal test functions. Random flights considerably enhance the efficient simplified PSO and the Lévy flight gives good balance between local space exploration and local minima avoidance. We discuss computational tradeoffs involved in generating such flights. In summary, these modifications show varying success between themselves for problem solving, but outperforms the uniform random exploration technique in most cases.
Photobioreactors offer an efficient and controllable environment from which many biomaterial-based products can be manufactured naturally. For example, the cultivation of the algae H. pluvialis, results in a carotenoid known as Astaxanthin, which is a highly valuable pigment and powerful antioxidant. The demand for naturally-produced Astaxanthin is much higher than synthetic, and the process by which this algae is cultivated is therefore important. We develop a discrete agent-based model that can be used to test the control processes for growing and harvesting such a bio product. We experiment with a variant of the Photosynthetic Factory model to improve cell division and hence growth kinetics. We employ parallel computing techniques to implement simulations of large numbers of control agents on graphical processing units. We discuss the implications of stochasticity, and propose a metric with which to balance cultivation time against fill rates of a stochastic data-parallel, bubble-column photobioreactor simulation.
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