{"ID":2844498,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.05778","arxiv_id":"2511.05778","title":"TOPSIS-like metaheuristic for LABS problem","abstract":"This paper presents the application of socio-cognitive mutation operators inspired by the TOPSIS method to the Low Autocorrelation Binary Sequence (LABS) problem. Traditional evolutionary algorithms, while effective, often suffer from premature convergence and poor exploration-exploitation balance. To address these challenges, we introduce socio-cognitive mutation mechanisms that integrate strategies of following the best solutions and avoiding the worst. By guiding search agents to imitate high-performing solutions and avoid poor ones, these operators enhance both solution diversity and convergence efficiency. Experimental results demonstrate that TOPSIS-inspired mutation outperforms the base algorithm in optimizing LABS sequences. The study highlights the potential of socio-cognitive learning principles in evolutionary computation and suggests directions for further refinement.","short_abstract":"This paper presents the application of socio-cognitive mutation operators inspired by the TOPSIS method to the Low Autocorrelation Binary Sequence (LABS) problem. Traditional evolutionary algorithms, while effective, often suffer from premature convergence and poor exploration-exploitation balance. To address these cha...","url_abs":"https://arxiv.org/abs/2511.05778","url_pdf":"https://arxiv.org/pdf/2511.05778v1","authors":"[\"Aleksandra Urbańczyk\",\"Bogumiła Papiernik\",\"Piotr Magiera\",\"Piotr Urbańczyk\",\"Aleksander Byrski\"]","published":"2025-11-08T00:47:37Z","proceeding":"cs.NE","tasks":"[\"cs.NE\",\"math.OC\"]","methods":"[\"LoRA\"]","has_code":false}