This paper presents an extensive study of a new metaheuristics algorithm called Smell Agent Optimization (SAO) on some CEC numerical optimization benchmark functions and Hybrid Renewable Energy System (HRES) engineering problems. The SAO implements the relationships between a smell agent and an object evaporating a smell molecule. These relationships are modelled into three separate modes called the sniffing, trailing and random modes. The sniffing mode simulates the smell perception capability of the agent as the smell molecules diffuse from a smell source towards the agent. The trailing mode simulates the capability of the agent to track the part of the smell molecules until its source is identified. Whereas, the random mode is a strategy employed by the agent to avoid getting stuck in local minima. Thirty-seven commonly used CEC benchmark functions, and HRES engineering problem are tested, and results are compared with six other metaheuristics methods. Experimental results revealed that the SAO can find the global optimum in 76% of the benchmark functions. Similarly, statistical results showed that the SAO also obtained the most cost effective HRES design compared to the benchmark algorithm.

本文针对一些 CEC 数值优化基准函数和混合可再生能源系统 (HRES) 工程问题，对称为气味代理优化 (SAO) 的新元启发式算法进行了广泛研究。SAO 实现了气味代理和蒸发气味分子的对象之间的关系。这些关系被建模为三种独立的模式，称为嗅探、尾随和随机模式。当气味分子从气味源向代理扩散时，嗅探模式模拟代理的气味感知能力。尾随模式模拟代理跟踪气味分子部分直到其来源被识别的能力。而随机模式是代理采用的一种策略，以避免陷入局部最小值。测试了 37 个常用的 CEC 基准函数和 HRES 工程问题，并将结果与​​其他六种元启发式方法进行了比较。实验结果表明，SAO 可以在 76% 的基准函数中找到全局最优值。同样，统计结果表明，与基准算法相比，SAO 也获得了最具成本效益的 HRES 设计。