# Plum Tree Algorithm and Weighted Aggregated Ensembles for Energy Efficiency Estimation
## 1 Introduction
Using the building energy prediction data from an institutional building located at the University of Florida as experimental support, the authors of [51] demonstrated the feasibility of a method based on an exhaustive search when identifying the optimal ensemble learning model. The building environment sector is responsible for approximately one-third of the world's energy consumption; therefore, seeking solutions that aim to reduce the demands of the building energy is an important research direction in the context of the mitigation of adverse environmental impacts [52]. In addition to the application of machine learning techniques to improve energy efficiency estimation, the research community also considered approaches to improve energy storage devices. In [53], the authors proposed a solution based on a mechanically \begin{table}
\begin{tabular}{c l l} \hline \hline
**Year** & **Nature-Inspired Algorithm** & **Source of Inspiration** \\ \hline
2005 & Artificial Bee Colony (ABC) [23] & honeybee foraging behavior \\
2006 & Cat Swarm Optimization (CSO) [24] & seeking and tracing mode behaviors \\
2007 & Monkey Search (MS) [25] & behavior of monkeys looking for food \\
2008 & Fast Bacterial Swarming Algorithm (FBSA) [26] & foraging mechanism of E-coli bacterium \\
2009 & Cuckoo Search (CS) [27] & brood parasitic and Levy flight behaviors \\
2010 & Bat Algorithm (BA) [28] & echolocation behavior \\
2011 & Cuckoo Optimization Algorithm (COA) [29] & egg laying and breeding behaviors \\
2012 & Flower Pollination Algorithm (FPA) [30] & flower pollination process \\
2013 & Dolphin Enolocation (DE) [31] & echolocation ability of dolphins \\
2014 & Forest Optimization Algorithm (POA) [32] & tree survival durations \\
2015 & Runner-Rot Algorithm (RRA) [33] & plants propagating through runners \\
2016 & Crowd Search Algorithm (CSA) [34] & crows’ intelligent behavior \\
2017 & Salp Swarm Algorithm (SSA) [35] & salp fish swarming \\
2018 & Tree Growth Algorithm (TGA) [36] & trees’ competition for light and foods \\
2019 & Salifish Optimizer (SO) [37] & sailfish group hunting \\
2020 & Horse Optimization Algorithm (HOA) [38] & horse herds hierarchical organization \\
2021 & African Voltures Optimization Algorithm (AVOA) [39] & African vultures’ behavior \\
2022 & Snake Optimizer (SO) [40] & snake mating behavior \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Representative Nature-Inspired Algorithms for 2005–2022. interlocked network (MIN) in a Li anode, which promises future success in battery fields that suffer from volume variations. As shown in [54], the lithium metal batteries which use solid electrolytes are expected to become the next generation of lithium batteries. Another challenge related to lithium-ion batteries is the accurate prediction of their lifetime in the context of the acceleration of their technological applications and advancements [55]. The main objective of this paper is the proposal of a novel nature-inspired algorithm with applications in a wide range of engineering problems. The energy efficiency estimation problem was selected as a representative use-case for the application of the proposed algorithm because of the importance of energy research in the context of climate change's near-future impact as well as because the application of ensemble learning to building energy efficiency prediction attracted much attention in the research community in recent years. The main contributions of the article are:  
1. A critical review of the nature-inspired approaches applied to machine learning ensemble optimization;
2. An overview of the application of machine learning approaches to energy efficiency estimation;
3. The proposal of a novel nature-inspired algorithm called the Plum Tree Algorithm (PTA), which has the biology of the plum tree as its main source of inspiration;
4.