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.

翻译：本文提出了一种基于信任的优化方法，这是进化计算中岛屿模型的一种新颖扩展，它用基于信任或声誉的灵活、智能体驱动的交互机制取代了传统的周期性迁移。实验结果表明，在多种优化问题上，基于信任的优化方法通常优于标准的岛屿模型进化算法。然而，算法性能因问题类型而异，某些配置对于特定的搜索空间或维度更为有效。研究结果表明，信任与声誉机制为进化优化提供了一种灵活且自适应的途径，在许多情况下提高了求解质量。