Electricity is one of the most important resources for the growth and sustainability of the population. This paper assesses the energy consumption and user satisfaction of a simulated air conditioning system controlled with two different optimization algorithms. The algorithms are a genetic algorithm (GA), implemented from the state of the art, and a non-dominated sorting genetic algorithm II (NSGA II) proposed in this paper; these algorithms control an air conditioning system considering user preferences. It is worth noting that we made several modifications to the objective function’s definition to make it more robust. The energy-saving optimization is essential to reduce CO2 emissions and economic costs; on the other hand, it is desirable for the user to feel comfortable, yet it will entail a higher energy consumption. Thus, we integrate user preferences with energy-saving on a single weighted function and a Pareto bi-objective problem to increase user satisfaction and decrease electrical energy consumption. To assess the experimentation, we constructed a simulator by training a backpropagation neural network with real data from a laboratory’s air conditioning system. According to the results, we conclude that NSGA II provides better results than the state of the art (GA) regarding user preferences and energy-saving.

Forecasting sales trends is a valuable activity for companies of all types and sizes, as it enables more efficient decision making to avoid unnecessary expenses from excess inventory or, conversely, losses due to insufficient inventory to meet demand. In this paper, we designed a personalized cost function to reduce economic losses caused by the excessive acquisition of products or derived from their scarcity when needed. Moreover, we designed an LSTM network integrated with Glorot and Orthogonal initializers and dropout to forecast sales trends in a lumber mill in Tamaulipas, Mexico. To generalize and appropriately forecast the sales of the lumber mill products, we optimized the LSTM network’s hyperparameters through a genetic algorithm, which was essential to explore the solution space. We evaluated our proposal in instances obtained from the historical sales of the five main products sold by the lumber mill. According to the results, we concluded that for our case study the proposed function cost and the hyperparameters optimization allowed the LSTM to forecast the direction and trend of the lumber mill’s product sales despite the variability of the products.

The optimization of Artificial Neural Networks (ANNs) remains a significant challenge in machine learning, particularly in overcoming local-optima limitations during training. Traditional classification algorithms, such as k-Nearest Neighbors (KNN), decision trees, Support Vector Machines (SVMs), and ANNs, often suffer from convergence to suboptimal solutions due to their training methods. This research proposes a hybrid neuroevolutionary approach that integrates a genetic algorithm with a NEAT-based structure to enhance ANN performance. Additionally, a Cellular Processing Algorithm (PCELL) is employed to expand the search space and improve solution quality. The methodology involves designing an initial neural network trained via backpropagation, followed by the application of genetic operators to evolve network structures. Experimental results from diverse benchmark datasets demonstrate that the proposed algorithm outperforms conventional ANN training methods and achieves performance levels comparable to evolutive solutions. The results suggest that integrating evolutionary strategies with cellular processing enhances classification accuracy and contributes to the advancement of neuroevolutionary learning techniques.

In this work, we investigate the variant of the Internet Shopping Optimization Problem (ISHOP) that considers different item units. This variant is more challenging than the original problem. The original ISHOP is already known as a combinatorial NP-hard problem. In this work, we present a formal proof that the ISHOP variant considering different item units belongs to the NP-Hard complexity class. The abovementioned variant is familiar to companies and consumers who need to purchase more than one unit of a specific product to satisfy their requirements. For example, companies buy different quantities of construction materials, medical equipment, office supplies, or chemical components. We propose two new evolutionary operators (crossover and mutation) and an unfeasible solution repair method for the studied ISHOP variant. Furthermore, we produce a new benchmark of 15 synthetic instances where item prices follow a random uniform distribution. Finally, to assess our evolutionary operators, we implemented two Evolutionary Algorithms, a Genetic Algorithm (GA) and a Cellular Genetic Algorithm (CGA), and an experimental evaluation against a Water Cycle Algorithm (WCA) from the state-of-the-art. Experimental results show that our proposed GA performs well with statistical significance.

Recent advances in cloud computing, large language models, and deep learning have started a race to create massive High-Performance Computing (HPC) centers worldwide. These centers increase in energy consumption proportionally to their computing capabilities; for example, according to the top 500 organization, the HPC centers Frontier, Aurora, and Super Computer Fugaku report energy consumptions of 22,786 kW, 38,698 kW, and 29,899 kW, respectively. Currently, energy-aware scheduling is a topic of interest to many researchers. However, as far as we know, this work is the first approach considering the idle energy consumption by the HPC units and the possibility of turning off unused units entirely, driven by a quantitative objective function. We found that even when turning off unused machines, the objectives of makespan and energy consumption still conflict and, therefore, their multi-objective optimization nature. This work presents empirical results for AGEMOEA, AGEMOEA2, GWASFGA, MOCell, MOMBI, MOMBI2, NSGA2, and SMS-EMOA. The best-performing algorithm is MOCell for the 400 real scheduling problem tests. In contrast, the best-performing algorithm is GWASFGA for a small-instance synthetic testbed.