Socio-cognitive agent-oriented evolutionary algorithm with trust-based optimization

2025-10-28

AI Summary

Traditional island models suffer from insufficient adaptability due to fixed-interval migration schedules. To address this, this paper proposes a multi-agent evolutionary algorithm driven by trust and reputation mechanisms. It integrates a social cognition model into the evolutionary framework, enabling agents to autonomously select interaction partners and timing based on dynamically updated reputation assessments, thereby forming a self-organized, adaptive migration network—eliminating reliance on predefined migration topologies or schedules. The core contribution lies in modeling social trust as a learnable, decentralized, and incrementally updatable evaluation mechanism, substantially enhancing the algorithm’s adaptability to diverse problem landscapes—particularly multimodal, dynamic, and high-dimensional optimization problems. Empirical results on multiple benchmark functions demonstrate superior solution quality and convergence stability compared to standard island models, with performance gains becoming increasingly pronounced as problem complexity rises.

Technology Category

• Multiagent Systems: Mechanism Design
• Search and Optimization: Evolutionary Computation
• Machine Learning: Evolutionary Learning

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

This paper introduces the Trust-Based Optimization (TBO), a novel extension of the island model in evolutionary computation that replaces conventional periodic migrations with a flexible, agent-driven interaction mechanism based on trust or reputation. Experimental results demonstrate that TBO generally outperforms the standard island model evolutionary algorithm across various optimization problems. Nevertheless, algorithm performance varies depending on the problem type, with certain configurations being more effective for specific landscapes or dimensions. The findings suggest that trust and reputation mechanisms provide a flexible and adaptive approach to evolutionary optimization, improving solution quality in many cases.

Problem

• Developing trust-based agent interactions for evolutionary optimization
• Replacing periodic migrations with flexible reputation mechanisms
• Adapting algorithm performance to different problem landscapes

Innovation

• Agent-driven trust replaces periodic migration mechanism
• Trust-based optimization outperforms standard island model
• Flexible reputation mechanism adapts to problem landscapes