Agent-based modeling is a relatively new approach to model complex systems composed of agents whose behavior is described using simple rules. As a consequence of the agent interactions emerges a complex global behavioral pattern not explicitly programmed. In the last decade, an increasing number of metaheuristic techniques have been reported in the literature where authors claim their novelty and their abilities to perform as powerful optimization methods. Although these schemes emulate very different processes or systems, the rules used to model individual behavior are very similar. The idea behind the design of many metaheuristic methods is to configure a recycled set of rules that has demonstrated to be successful in previous approaches for producing new optimization schemes. Such common rules have been designed without considering the final global result obtained by the individual interactions. On the other hand, agent-based systems provide a solid theory and a set of consistent models that allow characterizing global behavioral patterns produced by the collective interaction of the individuals from a set of simple rules. Under this perspective, several agent-based concepts and models that generate very complex global search behaviors can be used to produce or improve efficient optimization algorithms. In this paper, a new metaheuristic algorithm based on agent systems principles is presented. The proposed method is based on the agent-based model known as “Heroes and Cowards”. This model involves a small set of rules to produce two emergent global patterns that can be considered in terms of the metaheuristic literature as exploration and exploitation stages. To evaluate its performance, the proposed algorithm has been tested in a set of representative benchmark functions, including multimodal, unimodal, and hybrid benchmark formulations. The competitive results demonstrate the promising association between both paradigms.

基于代理的建模是一种相对较新的方法，可以对由代理组成的复杂系统建模，这些代理的行为使用简单的规则进行描述。代理交互的结果是出现了未明确编程的复杂全局行为模式。在过去的十年中，文献中报道了越来越多的元启发式技术，作者声称它们的新颖性和作为强大的优化方法执行的能力。尽管这些方案模拟的过程或系统非常不同，但是用于对单个行为进行建模的规则却非常相似。许多元启发式方法设计背后的思想是配置一组可循环使用的规则，这些规则在产生新优化方案的先前方法中已证明是成功的。设计此类通用规则时，并未考虑各个交互所获得的最终全局结果。另一方面，基于代理的系统提供了扎实的理论和一组一致的模型，这些模型允许表征由一组简单规则中的个体的集体互动所产生的全局行为模式。在这种情况下，可以使用几种基于代理的概念和模型来生成非常复杂的全局搜索行为，以生成或改进有效的优化算法。本文提出了一种新的基于代理系统原理的元启发式算法。所提出的方法基于称为“英雄和Co夫”的基于代理的模型。该模型涉及一小组规则，以产生两个新出现的全局模式，这些模式可以在元启发式文献中视为探索和开发阶段。为了评估其性能，已在一组代表性基准功能（包括多峰，单峰和混合基准公式）中对提出的算法进行了测试。竞争结果证明了两种范例之间有希望的关联。