# Plum Tree Algorithm and Weighted Aggregated Ensembles for Energy Efficiency Estimation
## 1 Introduction  
The nature-inspired optimization research field evolved a lot in recent years, and its sources of inspiration are various, ranging from swarm intelligence [1] to the laws of physics [2]. Nature-inspired optimization algorithms are often used as an alternative to mathematical methods to solve global optimization problems in various domains, such as finance [3], engineering [4], data mining [5], and other areas. Their main advantage is that they lead to good optimal solutions using reasonable computational resources. Several representative classes that can be distinguished among nature-inspired algorithms [6] are swarm intelligence methods [7], evolutionary computation approaches [8], and the algorithms which have the laws of physics and biology as their main source of inspiration [9]. Methods in the swarm intelligence class represent solutions as swarm members that exchange information during the search process. The exchanged information is related to the properties of the swarm leaders, which are either global leaders or local leaders. This class includes algorithms such as the Particle Swarm Optimization (PSO) algorithm [10], the Elephant Herding Optimization (EHO) algorithm [11], the Whale Optimization Algorithm (WOA) [12], the Spotted Hyena Optimizer (SHO) [13], and the Horse Herd Optimization Algorithm (HHOA) [14]. Approaches in the evolutionary computation class associate the search process to the evolution of a set of solutions called the population. Representative algorithms of this class are the Genetic Algorithms (GA) [15], the Differential Evolution (DE) algorithm [16], the Scatter Search (SS) algorithm [17], and the Invasive Weed Optimization (IWO) algorithm [18]. The algorithms inspired by the laws of biology and physics include a selection of algorithms which imitate the biological and physical processes of the nature. This class is represented by algorithms such as Harmony Search (HS) [19], the Gravitational Search Algorithm (GSA) [20], the Fireworks Algorithms (FA) [21], and the Spiral Dynamics Algorithm (SDA) [22]. Table 1 presents a selection of representative nature-inspired algorithms for each year from the period 2005-2022. As shown in the table, the sources of inspiration are various, ranging from the behaviors of specific birds or mammals to processes related to flower pollination or to trees' competition for light. Moreover, the big number of nature-inspired algorithms proposed in recent years demonstrates that the nature-inspired optimization research field is still an open research field with a variety of challenges. The applications of nature-inspired algorithms are various. The authors of [41] considered an algorithm based on the Glowworm Swarm Optimization (GSO) algorithm [42] and the Bacterial Foraging Optimization (BFO) algorithm [43] for multi-objective optimization. The approach presented in [44] introduced a novel algorithm named the Pelican Optimization Algorithm (POA), which was applied in engineering problems such as the pressure vessel design problem and the welded beam design problem. Another approach, the one presented in [45], considered a binary version of the HOA with applications in feature selection for classification problems. Nature-inspired algorithms were also considered in research fields such as image processing [46], pattern recognition [47], data mining [48], and video compression [49]. The application of these machine learning models in real systems is crucial to achieve smart building control and performant energy efficiency, as demonstrated in [50], in which the authors considered current practical issues related to the application of machine learning models to building energy efficiency. Moreover, the application of ensemble learning received a lot of attention in building energy prediction recently [51] because it returned predictions which were more stable and accurate than the ones returned by using conventional methods based on a single predictor.