Balancing Pareto Front exploration of Non-dominated Tournament Genetic Algorithm (B-NTGA) in solving multi-objective NP-hard problems with constraints
<|reference_start|>Balancing Pareto Front exploration of Non-dominated Tournament Genetic Algorithm (B-NTGA) in solving multi-objective NP-hard problems with constraints: The paper presents a new balanced selection operator applied to the proposed Balanced Non-dominated Tournament Genetic Algorithm (B-NTGA) that actively uses archive to solve multi- and many-objective NP-hard combinatorial optimization problems with constraints. The primary motivation is to make B-NTGA more efficient in exploring Pareto Front Approximation (PFa), focusing on 'gaps' and reducing some PFa regions' sampling too frequently. Such a balancing mechanism allows B-NTGA to be more adaptive and focus on less explored PFa regions. The proposed B-NTGA is investigated on two benchmark multi- and many-objective optimization real-world problems, like Thief Traveling Problem and Multi-Skill Resource-Constrained Project Scheduling Problem. The results of experiments show that B-NTGA has a higher efficiency and better performance than state-of-the-art methods.<|reference_end|>
@article{antkiewicz2024balancing,
    title={Balancing Pareto Front exploration of Non-dominated Tournament Genetic
  Algorithm (B-NTGA) in solving multi-objective NP-hard problems with
  constraints},
    author={Micha{l} Antkiewicz, Pawe{l} B. Myszkowski},
    journal={Information Sciences, 2024, 667: 120400},
    year={2024},
    doi={10.1016/j.ins.2024.120400},
    archivePrefix={arXiv},
    eprint={2410.05701},
    primaryClass={cs.NE}
}