This study explores the application of reinforcement learning (RL) algorithms to optimize lift control strategies. By developing a versatile lift simulator enriched with real-world traffic data from an intelligent building system, we systematically compare RL-based strategies against well-established heuristic solutions. The research evaluates their performance using predefined metrics to improve our understanding of RL’s effectiveness in solving complex decision problems, such as the lift control algorithm. The results of the experiments show that all trained agents developed strategies that outperform the heuristic algorithms in every metric. Furthermore, the study conducts a comprehensive exploration of three Experience Replay mechanisms, aiming to enhance the performance of the chosen RL algorithm, Deep Q-Learning.

Socio-cognitive computing is a paradigm developed for the last several years in our research group. It consists of introducing mechanisms inspired by inter-individual learning and cognition into metaheuristics. Different versions of the paradigm have been successfully applied in hybridizing Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithms, Differential Evolution, and Evolutionary Multi-agent System (EMAS) metaheuristics. In this paper, we have followed our previous experiences in order to propose a novel mutation based on sociocognitive mechanism and test it based on Evolution Strategy (ES). The newly constructed versions were applied to popular benchmarks and compared with their reference versions.

This paper introduces a novel Balanced Binary Whale Optimization Algorithm (BB-WOA) designed specifically for dynamic feature selection in Green Cloud Computing (GCC). Traditional feature selection methods used in cloud resource forecasting often suffer from either suboptimal predictive performance or excessive computational complexity. To address this, we propose significant algorithmic enhancements over the standard Binary Whale Optimization Algorithm (B-WOA), including dynamic binary transition functions, progressive scaling, diversified population initialization via Sobol sequences, balanced exploration-exploitation strategies, and an activation-based recovery mechanism. Our comprehensive experimental evaluation using real-world cloud resource data demonstrates that BB-WOA outperforms existing methods. Specifically, BB-WOA reduces predictive error (RMSE) by up to 7.45% compared to B-WOA and 1.55% compared to Genetic Algorithm (GA). Moreover, BB-WOA achieves computational improvements, reducing execution time by approximately 38.30%, 64.13%, and 78.53% over B-WOA, Random Search (RS), and GA, respectively. Environmentally, the proposed method reduces energy consumption by 38.48% relative to B-WOA, 64.18% compared to RS, and 52.55% relative to GA, while simultaneously selecting significantly fewer features (a reduction of up to 70.77%). These results underscore the effectiveness and sustainability of BB-WOA, positioning it as a highly competitive and environmentally friendly solution.