A genetics algorithms for optimizing a function over the integer efficient set

Authors

  • Ali Zaidi Laboratory of Multiple Criteria Decision and Operations Research (AMCD and RO), Faculty of Mathematics, USTHB University, Bab-Ezzouar, BP32 El-Alia, 16122, Algiers, Algeria
  • Djamal Chaabane Laboratory of Multiple Criteria Decision and Operations Research (AMCD and RO), Faculty of Mathematics, USTHB University, Bab-Ezzouar, BP32 El-Alia, 16122, Algiers, Algeria
  • Larbi Asli LaMOS Laboratory, Faculty of Exact Sciences, University of Bejaia, Algeria
  • Lamine Idir Centre for Research in Amazigh Language and Culture (CRLCA), Algeria
  • Saida Matoub Centre for Research in Amazigh Language and Culture (CRLCA), Algeria

Abstract

In this paper, we propose an algorithm called Directional Exploration Genetic Algorithm (DEGA) to resolve a function $\Phi$ over the efficient set of a multi-objective integer linear programming problem (MOILP). DEGA algorithm belongs to evolutionary algorithms, which operate on the decision space by choosing the fastest improving directions that improve the objectives functions and $\Phi$ function. Two variants of this algorithm and a basic version of the genetic algorithm (BVGA) are performed and implemented in Python. Several benchmarks are carried out to evaluate the algorithm's performances and interesting results are obtained and discussed.

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Published

2024-05-27

Issue

Vol. 15 No. 1 (2024): CRORR

Section

CRORR Journal Regular Issue

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