Titlebook: Metaheuristics; 15th International C Marc Sevaux,Alexandru-Liviu Olteanu,Salma Makboul Conference proceedings 2024 The Editor(s) (if applic

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A Simulated Annealing Algorithm to Learn an RMP Preference Modeln by the DM. Here, we use Simulated Annealing (SA) to learn the parameters of the Ranking based on Multiple Reference Profiles (RMP) model and its simpler variant SRMP. Extensive experiments demonstrate that our proposal outperforms existing methods in terms of both calculation time and accuracy.

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0302-9743 nce focuses on artificial intelligence, combinatorial optimization, computer science, graph theory, evolutionary algorithms, genetic algorithms, simulated annealing, optimization, optimization problems..978-3-031-62911-2978-3-031-62912-9Series ISSN 0302-9743 Series E-ISSN 1611-3349

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0302-9743 ing June 4–7, 2024...The 36 full papers presented together with 34 short papers were carefully reviewed and selected from 127 submissions. The conference focuses on artificial intelligence, combinatorial optimization, computer science, graph theory, evolutionary algorithms, genetic algorithms, simul

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Breakout Local Search for Heaviest Subgraph Problemry to obtain the most appropriate degree of diversification. Computational experiments are performed on a number of large random graphs. The experimental evaluations show that the results obtained by BLS are comparable to, and in most cases superior to, those of the current state-of-the-art approaches.

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A Biased Random Key Genetic Algorithm for Solving the ,-Neighbor p-Center Problem and the goal is to minimize this maximum distance. The proposed algorithm is compared with the best method found in the literature. The performance of the algorithm is evaluated over a large set of instances showing the robustness of the proposal.

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A Continuous-GRASP Random-Key Optimizertested on five NP-hard problems: Traveling salesman problem (TSP); Tree hub location problem in graphs (THLP); Steiner triple set covering problem (STCP); Node capacitated graph partitioning problem (NCGPP); and Job sequencing and tool switching problem (SSP).

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