Integral Global Optimality Conditions and an Algorithm for Multiobjective Problems

Everton J. Silva, Elizabeth W. Karas, Lucelina B. Santos

Research output: Contribution to journalArticlepeer-review


In this work, we propose integral global optimality conditions for multiobjective problems not necessarily differentiable. The integral characterization, already known for single objective problems, are extended to multiobjective problems by weighted sum and Chebyshev weighted scalarizations. Using this last scalarization, we propose an algorithm for obtaining an approximation of the weak Pareto front whose effectiveness is illustrated by solving a collection of multiobjective test problems.

Original languageEnglish
Pages (from-to)1265-1288
Number of pages24
JournalNumerical Functional Analysis and Optimization
Issue number10
Publication statusPublished - 2022


  • Chebyshev weighted scalarization
  • integral global optimality conditions
  • multiobjective optimization
  • Pareto front
  • weighted sum scalarization


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