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arXiv · 2511.05778

TOPSIS-like metaheuristic for LABS problem

Aleksandra Urbańczyk·Bogumiła Papiernik·Piotr Magiera·Piotr Urbańczyk·Aleksander Byrski
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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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Aleksandra Urbańczyk, Bogumiła Papiernik, Piotr Magiera, Piotr Urbańczyk, Aleksander Byrski. 2025-11-08. TOPSIS-like metaheuristic for LABS problem. https://doi.org/10.1007/978-3-032-03705-3_24

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