Resource allocation and levelling remain critical challenges in construction management, where project tasks must be synchronized under strict time and resource constraints. Traditional scheduling techniques, such as the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT), efficiently define timelines but fail to handle fluctuations in resource usage. These fluctuations cause inefficiencies, idle periods, and cost overruns. This paper introduces the World Hyper-Heuristic (WHH) algorithm, an adaptive optimization framework that dynamically selects among multiple metaheuristics using reinforcement learning. WHH integrates Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA), and Imperialist Competitive Algorithm (ICA) into a unified decision-making model. The proposed method is implemented in MATLAB and tested on a real construction project dataset involving 20 activities. Experimental results demonstrate that WHH achieves superior performance in minimizing resource fluctuation and improving levelling smoothness. Compared to GA, PSO, ACO, SA, and ICA, the proposed algorithm exhibits faster convergence, higher stability, and better adaptability to constraint complexity. These findings suggest that WHH provides a practical, intelligent framework for optimizing resource allocation and levelling in modern construction projects.