Abstract—In this paper, we solve the bi-objective obnoxious with a population-based method. The designed algorithm first determines a starting archive set by applying an iterative search on the equivalent problem, where an aggregate function is considered. Second, an adaptation of the dominating local search, combined with exchange operators, is considered for generating a series of new non-dominated solutions that enrich the reference archive set. Third, a drop and rebuild strategy is incorporated to the algorithm for iteratively highlighting the final Pareto front. An experimental part is given, where the performance of the method is evaluated on a set of benchmark instances of the literature. Its provided results are compared to those achieved by the more recent methods available in the literature. Encouraging results are reached.
Index Terms—Bi-objective, heuristics, obnoxious, optimization.
M. Aïder and A-I. Azzi are with LaROMaD, Fac. Maths, USTHB, BP 32, Bab Ezzouar, 16111, Algeria.
Mhand Hifi is with EPROAD UR 4669, UPJV, Amiens, France.
Cite: Méziane Aïder, Aida-Ilham Azzi, and Mhand Hifi, "Effect of Drop and Rebuilt Operator for Solving the Biobjective Obnoxious p-Median Problem," International Journal of Machine Learning vol. 13, no. 1, pp. 1-6, 2023.Copyright @ 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).