Application of Genetic Algorithms for the Optimisation of Multi-Pollutant Multi-Effect Problems

摘要: In this paper, crucial aspects of the implications and complexity interconnected multipollutant multi-effect assessments both air pollution control strategies closely related reduction Greenhouse Gas (GHG) emissions will be discussed. The main aims work described here are to identify core problems which occur when trying apply current state-of-the-art methodology conduct integrated – in context, cost-benefit assessment (CBA) as well cost-effectiveness (CEA) using sophisticated computer models propose solutions identified. approaches display use databases, efficient Genetic Algorithms (GA) already existing software tools a unified model framework. first part paper discusses need for new developments one particular field Integrated Assessment Models (IAMs), (typically) country-specific single pollutant abatement cost curves, have been applied large number modelling with aim find effective given quality targets. However, research conducted such non-linear problem tropospheric ozone (dealing two primary pollutants their rather complex relationship form ozone) identified basic cost-curve based even two-pollutant case. approach discussed solves key identified, making extensive databases order provide fast high input CEA CBA. addition that, application means address extremely complex, vast solution spaces typical tasks IAMs set solve nowadays. final diversity increasing operators methods increase performance GA optima results runs