TOPSIS-like metaheuristic for LABS problem

• URL: http://arxiv.org/abs/2511.05778v1
• Date: Sat, 08 Nov 2025 00:47:37 GMT
• Title: TOPSIS-like metaheuristic for LABS problem
• Authors: Aleksandra Urbańczyk, Bogumiła Papiernik, Piotr Magiera, Piotr Urbańczyk, Aleksander Byrski,
• Abstract summary: We introduce socio-cognitive mutation mechanisms that integrate strategies of following the best solutions and avoiding the worst.<n>By guiding search agents to imitate high-performing solutions and avoid poor ones, these operators enhance both solution diversity and convergence efficiency.
• Score: 70.49434432747293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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