Large-scale multi-objective optimization

The improvement in computing power in the last decade has made it possible to develop computational intelligence and AI methodologies for complex problems in various applications in science and industry. Additionally, large available data sets from simulations can contribute to unlock the power of these methodologies such as artificial neural networks and evolutionary algorithms. One of the advantages of working on evolutionary algorithms in comparison to neural networks (such as deep learning) is the explainability of the developed solutions and additionally the capability to deal with problems with large search spaces. In the recent years, there is a growing interest in so-called large-scale multi-objective optimization that deal with problems with a large number of decision variables. The performance of classic metaheuristic algorithms often deteriorates when the dimensionality of the decision space increases. We have published several works on this topic. Our proposed WOF framework can find very good solutions in a relatively short time in comparison to existing approaches:



  • Heiner Zille, Hisao Ishibuchi, Sanaz Mostaghim and Yusuke Nojima
  • A Framework for Large-scale Multi-objective Optimization based on Problem Transformation (Download) (Supplement Material) (Sourcecode
  • IEEE Transactions on Evolutionary Computation, Vol. 22, Issue 2, pp. 260-275, April 2018. --> Link


  • Heiner Zille and Sanaz Mostaghim
  • Comparison Study of Large-scale Optimisation Techniques on the LSMOP Benchmark Functions (Download) (Sourcecode)
  • IEEE Symposium Series on Computational Intelligence (SSCI), IEEE SSCI 2017, Honolulu, Hawaii, November 2017 --> Link


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