TOPSIS-Like Metaheuristic for LABS Problem

2025-11-08

International Conference on Artificial Intelligence and Soft Computing

AI Summary

To address premature convergence and imbalance between exploration and exploitation in optimizing Low-Autocorrelation Binary Sequences (LABS), this paper proposes a social-cognitive mutation mechanism inspired by the TOPSIS multi-criteria decision-making method. The mechanism quantifies each individual’s relative superiority within the population to guide mutation operations—simultaneously attracting toward elite solutions and repelling from inferior ones—thereby enabling directed diversification in the solution space. The proposed socio-cognitive mutation operator is seamlessly integrated into standard evolutionary frameworks without requiring additional parameter tuning. Experimental evaluation on canonical LABS benchmark instances demonstrates substantial improvements in both solution quality (Merit value) and convergence speed. On average, the method outperforms classical genetic algorithms (GA), differential evolution (DE), and state-of-the-art metaheuristics. Results confirm its effectiveness and robustness in balancing global exploration and local exploitation.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Evolutionary Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Applying TOPSIS-inspired mutation to LABS problem optimization
• Addressing premature convergence in evolutionary algorithms
• Enhancing solution diversity and convergence efficiency

Innovation

• TOPSIS-like socio-cognitive mutation operators
• Following best solutions and avoiding worst
• Enhancing solution diversity and convergence efficiency