The capacitated electric vehicle routing problem (CEVRP) requires a set of shortest delivery routes for capacitated electric vehicles from a depot to multiple customer locations. In this paper, a Q-learning-based hyper-heuristic (QHH) is proposed to solve the CEVRP. First, a novel initialization method is proposed to generate capacity-feasible solutions with potentially shorter routes. Second, a repair method is devised to insert proper charging stations into routes, generate feasible solutions, optimize visits to charging stations, and subsequently improve the solutions. Then, a diverse set of six common low-level heuristics (LLHs) and several local search methods are employed as fundamental components of the QHH algorithm. Meanwhile, Q-learning serves as a guiding high-level heuristic, dynamically selecting the appropriate LLHs in response to the evolutionary process. Additionally, the Design of Experiments methodology is employed to investigate the performance of various parameter settings. Finally, the proposed QHH is evaluated on a large set of 60 CEVRP instances, demonstrating its superiority over four state-of-the-art algorithms.

As cloud computing undergoes rapid development, cloud workflow scheduling has gained significant attention as a critical area of research. However, the substantial energy consumption of cloud data centers in handling diverse workflow applications highlights the need for effective energy optimization strategies to promote green computing. This research investigates an energy-efficient cloud workflow scheduling problem that explores the inherent relationship between scheduling schemes, host load, and energy consumption. To tackle this problem, a reinforcement learning-based population hyper-heuristic algorithm is proposed to provide an effective solution. Specifically, six low-level heuristics (LLHs) tailored to the characteristics of the problem are developed and refined. Meanwhile, Q-learning is employed as a high-level strategy for the intelligent selection of LLHs. Moreover, an adaptive local search strategy is introduced to improve inferior individuals. Additionally, an experience-driven task-resource mapping approach is employed to realize efficient resource allocation. To mitigate the challenge of infeasible solutions, a region-constrained insertion mechanism is devised to effectively minimize their occurrence. Subsequently, a solution feasibility reconstruction strategy is introduced to adjust and optimize any remaining infeasible solutions. Extensive experiments on various workflow applications of different scales and types demonstrate the superiority of the proposed algorithm over existing approaches in terms of energy optimization and exhibit significant practical application potential.

In this study, we use Genetic Programming (GP) to compose new optimization benchmark functions. Optimization benchmarks have the important role of showing the differences between evolutionary algorithms, making it possible for further analysis and comparisons. We show that the benchmarks generated by GP are able to differentiate algorithms better than human-made benchmark functions. The fitness measure of the GP is the Wasserstein distance of the solutions found by a pair of optimizers. Additionally, we use MAP-Elites to both enhance the search power of the GP and also illustrate how the difference between optimizers changes by various landscape features. Our approach provides a novel way to automate the design of benchmark functions and to compare evolutionary algorithms.