Urban customized bus (UCB) has become an important mode of urban transportation in recent years. To further enhance the applicability of UCB, we develop a UCB model that accommodates flexible pick-ups and drop-offs in this article. In our model, when a bus passes through a station, it can drop off passengers whose destination is exactly this station and pick up new passengers as long as constraints are satisfied. By eliminating fixed boarding and alighting regions that were commonly adopted in prior studies, we can provide travel services for both prebooked and real-time orders across broader temporal and spatial ranges. Under such a scenario, it is more challenging to decide when and where to pick up which passenger with which bus, and which route the bus chooses. To solve these intractable problems, we proposed an approach including passenger assignment and route design. For passenger assignment, we devise an ant colony system (ACS) method and a greedy method to assign prebooked and real-time passengers to buses, respectively. ACS represents the correlation among different passengers numerically through a pheromone matrix and updates it iteratively based on the good results. Therefore, prebooked passengers with higher correlations are more likely to be assigned to the same buses. The greedy method always selects passengers with the highest heuristic value to have a fast response in real-time passenger assignment. For route design, we adapt a Dijkstra-based greedy method to efficiently design routes for each bus in both static and dynamic situations. At last, experiments based on both generated data and real data demonstrate the superiority of the proposed method.

Recent years have witnessed the increasing adoption of unmanned aerial vehicles (UAVs) for power grid inspection, as they gradually replace conventional hazardous manual operations. However, conventional metaheuristics and operations research-based path planning algorithms suffer from long computation times for large-scale problems, thus making them unsuitable for real-time multi-UAV scheduling under flight time and energy consumption uncertainty. To solve the multi-UAV online path planning problem, this paper proposes a multi-agent deep reinforcement learning (MADRL) algorithm that performs online path planning based on real-time environmental and UAV state information, with the goal of minimizing the total travel distance. We employ resource preservation and decision sharing to handle real-time cooperation under travel time uncertainty while preventing resource conflicts. We design a Safety Mask mechanism that constrains dangerous UAV actions to address energy consumption uncertainty. Experiments on instances with 40-400 towers show that our algorithm requires minimal computation time and generates higher-quality solutions compared to other baseline algorithms in large-scale tower scenarios.

Many real-world combinatorial optimization problems are defined on networks, such as road networks and social networks, etc. Due to the connectivity nature of networks, decision variables in such problems are usually coupled with each other, and the variables are also closely related to the characteristics of the local subnetwork to which they belong. These features pose new challenges to the design of cooperative coevolutionary (CC) algorithms for the large-scale network-based optimization. To improve the scalability, efficiency, and effectiveness of CC, we propose a new approach called parallel cooperative coevolution with overlapping decomposition and local evaluation (CCOL) for large-scale network-based combinatorial optimization. First, CCOL devises the overlapping decomposition to divide a large-scale network-based problem into some overlapping subproblems with lower dimensions. Second, subproblems are optimized in parallel since they can be evaluated by defined local objectives without the context of other subproblems. Meanwhile, an overlapping cooperation strategy is employed to achieve consensus toward the global objective. Finally, as different subproblems may have different scales, a Huffman-tree-based resources assignment strategy is devised. This strategy is able to utilize computing resources in a better way and thus further improve the scalability of the algorithm. To better demonstrate the proposed CCOL, we implement a set-based particle swarm optimization CCOL (CCOL-SPSO) to solve the multidepot vehicle routing problem with time windows as an example. Experimental results in medium and large scale benchmark problems indicate that CCOL is efficient and promising.

Identifying influential nodes in social networks is an important problem called the influence maximization (IM) problem. So far, a large number of IM algorithms have been proposed. Among these algorithms, meta-heuristic approaches such as evolutionary algorithms (EAs) can obtain high-quality solutions. But in general, they usually suffer from time efficiency problems and are designed only for a few diffusion models. In this paper, we propose a novel EA combined with the reverse influence sampling (RIS) to solve the IM problem. By introducing the RIS technique, we can evaluate influence spreading efficiently under various diffusion models using hypergraphs. Moreover, the hypergraphs are also used as a kind of high-level heuristic information. To combine the RIS technique with EA, we exploit the idea of RIS to design a surrogate model and decide to address the single-objective IM problem in a multi-objective way. Then we modify the classical NSGA-II algorithm and apply it to this strategy. Our experimental results on million-scale social networks validate the good performance of the proposed approach.

Influence maximization (IM) is a problem of selecting the most influential vertices with a limited budget under a given propagation model. A significant challenge faced by many existing algorithms pertains to their inability to reconcile the competing goals of solution quality and computational efficiency, rendering them unsuitable in large-scale social networks. In this paper, we propose an adaptive community-based influence maximization algorithm, named AComA, to solve the IM problem with a balance of effectiveness and efficiency. First, we introduce a community detection method to divide a large-scale network into several communities. An adaptive indicator is then defined to identify vertices with high propagation values in divided community networks. Based on community detection and the adaptive influence indicator, the number of candidate vertices is reduced, which significantly reduces the search space of the problem. Second, to select the final seed set from these candidate vertices, a genetic algorithm (GA) is introduced. The crossover and mutation operations are modified explicitly to adapt to the IM problem. By extracting information from the local neighborhood and the global community structure, AComA achieves a more accurate measurement of the influence spread for each vertex. The method proposed in this paper is tested on several real-world datasets. The experimental results show that AComA is promising.