TOPSIS-like metaheuristic for LABS problem

TL;DR

This paper introduces a TOPSIS-inspired socio-cognitive mutation approach to improve evolutionary algorithms for the LABS problem, enhancing solution diversity and convergence efficiency.

Contribution

It presents a novel mutation mechanism based on socio-cognitive principles inspired by TOPSIS, addressing exploration-exploitation issues in LABS optimization.

Findings

Outperforms baseline algorithms in LABS sequence optimization

Enhances solution diversity and convergence speed

Demonstrates effectiveness of socio-cognitive mutation operators

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.