A Novel Smell Agent Optimization (SAO): An extensive CEC study and engineering application

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 benchmarked algorithms. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper presents an extensive study of a new metaheuristics algorithm called Smell Agent Optimization (SAO) on CEC numerical optimization benchmark functions and Hybrid Renewable Energy System (HRES) engineering problems. Results show that SAO excels in finding global optimum solutions and cost-effective designs, outperforming benchmarked algorithms.