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

TL;DR

This paper presents Trust-Based Optimization (TBO), an innovative agent-driven extension to the island model in evolutionary algorithms that leverages trust and reputation to enhance optimization performance across diverse problems.

Contribution

The paper introduces TBO, a novel trust-based interaction mechanism replacing traditional migrations in the island model, offering a flexible and adaptive approach to evolutionary optimization.

Findings

TBO generally outperforms standard island models.

Performance varies with problem type and configuration.

Trust mechanisms improve solution quality in many cases.

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.