Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice - ImAppNIO

Prosiect: Ymchwil a ariannwyd yn allanol

Manylion y Prosiect


Nature-inspired search and optimisation heuristics are easy to implement and apply to new problems. However, in order to achieve good performance it is usually necessary to adjust them to the problem at hand. Theoretical foundations for the understanding of such approaches have been built very successfully in the past 20 years but there is a huge disconnect between the theoretical basis and practical applications. The development of powerful analytical tools, significant insights in general limitations of different types of nature-inspired optimisation methods and the development of more practically relevant perspectives for theoretical analysis have brought impressive advances to the theory-side of the field. However, so far impact on the application-side has been limited and few people in the diverse potential application areas have benefitted from these advances.
The main objective of the COST Action is to bridge this gap and improve the applicability of all kinds of nature-inspired optimisation methods. It aims at making theoretical insights more accessible and practical by creating a platform where theoreticians and practitioners can meet and exchange insights, ideas and needs; by developing robust guidelines and practical support for application development based on theoretical insights; by developing theoretical frameworks driven by actual needs arising from practical applications; by training Early Career Investigators in a theory of nature-inspired optimisation methods that clearly aims at practical applications; by broadening participation in the ongoing research of how to develop and apply robust nature-inspired optimisation methods in different application areas.
StatwsWedi gorffen
Dyddiad cychwyn/gorffen dod i rym09 Maw 201608 Maw 2020


  • Horizon 2020 -European Commission (CA15140): £10,774.05

Ôl bys

Archwilio’r pynciau ymchwil mae a wnelo'r prosiect hwn â nhw. Mae’r labelau hyn yn cael eu cynhyrchu’n seiliedig ar y dyfarniadau/grantiau sylfaenol. Gyda’i gilydd maen nhw’n ffurfio ôl bys unigryw.
  • Wales Randomised Optimisation Algorithms Network-WIN

    Zarges, C. (Prif Ymchwilydd), Bazargani, M. (Cyd-ymchwilydd), Caraffini, F. (Cyd-ymchwilydd), Jansen, T. (Cyd-ymchwilydd) & Rahat, A. (Cyd-ymchwilydd)

    01 Ebr 202430 Medi 2024

    Prosiect: Ymchwil a ariannwyd yn allanol