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Applying Social Choice Theory to Solve Engineering Multi-objective Optimization Problems

  • Vinicius Renan de Carvalho
  • Kate Larson
  • Anarosa Alves Franco Brandão
  • Jaime Simão SichmanEmail author
Article
  • 16 Downloads

Abstract

Multi-objective optimization problems usually do not have a single unique optimal solution, for either discrete or continuous domains. Furthermore, there are usually many possible available algorithms for solving these problems, and one typically does not know in advance which of these will be the most effective for solving a particular problem instance. Hyper-heuristics (HHs) are often used as a means to make this choice. In particular, the underlying idea of HHs is to run several algorithms or heuristics and dynamically decide, based on different criteria, which problem or part of the problem should be solved by which algorithm or heuristic. On the other hand, the domain of social choice theory studies how to design collective decision processes by aggregating individual inputs into collective ones. In this paper, we explore the use of social choice theory in creating HHs. By using HHs based on different voting methods, like Borda, Copeland and Kemeny–Young, we show how we can solve both continuous and discrete engineering multi-objective optimization problems and discuss the results obtained by each of these methods. Our obtained results show that our strategy has found solutions that are at least equals to the ones generated by the best algorithm among the studied ones, and sometimes even overcomes these results.

Keywords

Hyper-heuristics Multi-objective evolutionary algorithms Voting methods Crashworthiness Car side impact Machining Water resource planning Multi-objective travel salesperson problem 

Notes

Acknowledgements

Vinicius Renan de Carvalho was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)-Grant Code 001, and by CNPq, Brazil, Grant Agreement No. 140974/2016-4.

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Copyright information

© Brazilian Society for Automatics--SBA 2019

Authors and Affiliations

  1. 1.Laboratório de Técnicas Inteligentes (LTI), Escola Politécnica (EP)Universidade de São Paulo (USP)São PauloBrazil
  2. 2.Cheriton School of Computer ScienceUniversity of WaterlooWaterlooCanada

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