A Micro Dynamic Multi-objective Evolutionary Algorithm for Small-scale Smart Greenhouse with Low-power Microprocessor.

Smart greenhouse is a modern agricultural facility that integrates smart control systems to regulate the plant growth environment through advanced intelligent technology and devices. In recent years, smart greenhouses have received widespread attention and have been applied in agriculture. Due to their high energy demands and costs, current smart greenhouses are often impractical for applications with limited resources. Nevertheless, small-scale smart greenhouse with low-power microprocessor, are more suitable for homes, offices, and other fields. Therefore, this paper proposes a micro dynamic multi-objective evolutionary algorithm ( mu DMOEA) for small-scale smart greenhouse with low-power microprocessor, which applies chaotic mapping to select dynamic response strategies based on the fitness of dominant relationships and k-nearest neighbor environmental selection. mu DMOEA performs well in the simulation of small-scale smart greenhouses. It not only outperforms SGEA, DNSGA-II, and RVCP in IGD indicator but also plays a good role in adjusting environmental parameters. It demonstrates the feasibility and effectiveness of micro dynamic multi-objective optimization on small-scale smart greenhouse with low-power microprocessor.