Model-based multiobjective evolutionary algorithm optimization for HCCI engines

Modern engines feature a considerable number of adjustable control parameters. With this increasing number of degrees of freedom (DoFs) for engines and the consequent considerable calibration effort required to optimize engine performance, traditional manual engine calibration or optimization methods are reaching their limits. An automated and efficient engine optimization approach is desired. In this paper, interdisciplinary research on a multiobjective evolutionary algorithm (MOEA)-based global optimization approach is developed for a homogeneous charge compression ignition (HCCI) engine. The performance of the HCCI engine optimizer is demonstrated by the cosimulation between an HCCI engine Simulink model and a Strength Pareto Evolutionary Algorithm 2 (SPEA2)-based multiobjective optimizer Java code. The HCCI engine model is developed by Simulink and validated with different engine speeds (1500-2250 r/min) and indicated mean effective pressures (IMEPs) (3-4.5 bar). The model can simulate the HCCI engine's indicated specific fuel consumption (ISFC) and indicated specific hydrocarbon (ISHC) emissions with good accuracy. The introduced MOEA optimization is an approach to efficiently optimize the engine ISFC and ISHC simultaneously by adjusting the settings of the engine's actuators automatically through the SPEA2. In this paper, the settings of the HCCI engine's actuators are intake valve opening (IVO) timing, exhaust valve closing (EVC) timing, and relative air-to-fuel ratio $lambda$. The cosimulation study and experimental validation results show that the MOEA engine optimizer can find the optimal HCCI engine actuators' settings with satisfactory accuracy and a much lower time consumption than usual

An effective risk management approach to prevent bee damage due to the emission of abraded seed treatment particles during sowing of seeds treated with bee toxic insecticides

contribution to session V Honey bee poisoning incidents and monitoring schemes In spring of 2008, a bee incident occurred in the Upper Rhine Valley (Germany) during drilling of corn: bees were exposed to dust from abraded particles of the seed-coating containing the insecticide clothianidin. An inspection of drilled seed batches for resistance to abrasion and a geographical correlation analysis between specified seed batches and reported bee damages revealed that the incident was caused by improperly dressed batches of corn seeds with excessive abrasion of seed treatment particles which were subsequently emitted via the outlet air stream of the pneumatic drilling machines. Concerns raised by local beekeepers regarding effects on bees from foraging in seed-treated corn fields during bloom could be dispelled by a large-scale survey of clothianidin residues in pollen from the treated crop and an accompanying monitoring of bee hives exposed to flowering corn fields. In order to ensure the bee safety of seed-dressing products, technical improvements of seed treatment quality and drilling technology were developed resulting in a minimization of formation and emission of dust from abraded seed treatment particles. The efficacy of these improvements was proven in field trials. Keywords: seed treatment, drilling machines, corn, clothianidin, dust, honey bee

Using Multiple Representations in Evolutionary Algorithms

Although evolutionary algorithms are very different from other artificial intelligence search algorithms, they face similar fundamental issues --- representation and search. There have been a large amount of work done in evolutionary computation on search, such as recombination operators, mutation operators, selection schemes and various specialised operators. In comparison, research in different representations has not been as active. Most of such research has been focused on a single representation, e.g., bit strings, real-valued vectors using cartesian coordinates, etc. This paper proposes and studies multiple representations in an evolutionary algorithm and shows empirically how multiple representations can benefit search as much as a good search operator could