Spatial crowdsourcing (SC), as an effective paradigm for accomplishing spatiotemporal tasks, has gradually attracted widespread attention from both industry and academia. With the advancement of mobile technology, the service modes of SC have become more diversified and flexible, aiming to better meet the variable requirements of users. However, most research has focused on homogeneous task allocation problems under a single service model, without considering the individual differences among task requirements and workers. Consequently, many of these studies fail to achieve satisfactory outcomes in real scenarios. Based on real service scenarios, in this study, we investigate a heterogeneous multi-task allocation (HMTA) problem for hybrid scenarios and provide a formal description and definition of the problem. To solve the problem, we propose a role division approach embedded with an individual sorting model (RD-ISM). This approach is implemented based on a batch-based mode (BBM) and consists of two parts. First, an individual sorting model is introduced to determine the sequence of objects based on spatiotemporal attributes, prioritizing tasks and workers. Second, a role division model is designed based on an attraction–repulsion mechanism to match tasks and workers. Following several iterations over multiple batches, the approach obtains the final matching results. The effectiveness of the approach is verified using real and synthetic datasets and its performance is demonstrated through comparisons with other algorithms. Additionally, the impact of different parameters within the approach is investigated, confirming its scalability.

The new generation of artificial intelligence (AI) research initiated by Chinese scholars conforms to the needs of a new information environment changes, and strives to advance traditional artificial intelligence (AI 1.0) to a new stage of AI 2.0. As one of the important components of AI, collective intelligence (CI 1.0), i.e., swarm intelligence, is developing to the stage of CI 2.0 (crowd intelligence). Through in-depth analysis and informative argumentation, it is found that an incompatibility exists between CI 1.0 and CI 2.0. Therefore, CI 1.5 is introduced to build a bridge between the above two stages, which is based on bio-collaborative behavioral mimicry. CI 1.5 is the transition from CI 1.0 to CI 2.0, which contributes to the compatibility of the two stages. Then, a new interpretation of the meta-synthesis of wisdom proposed by Qian Xuesen is given. The meta-synthesis of wisdom, as an improvement of crowd intelligence, is an advanced stage of bionic intelligence, i.e., CI 3.0. It is pointed out that the dual-wheel drive of large language models and big data with deep uncertainty is an evolutionary path from CI 2.0 to CI 3.0, and some elaboration is made. As a result, we propose four development stages (CI 1.0, CI 1.5, CI 2.0, and CI 3.0), which form a complete framework for the development of CI. These different stages are progressively improved and have good compatibility. Due to the dominant role of cooperation in the development stages of CI, three types of cooperation in CI are discussed: indirect regulatory cooperation in lower organisms, direct communicative cooperation in higher organisms, and shared intention based collaboration in humans. Labor division is the main form of achieving cooperation and, for this reason, this paper investigates the relationship between the complexity of behavior and types of labor division. Finally, based on the overall understanding of the four development stages of CI, the future development direction and research issues of CI are explored.

This paper presents an effective perturbation-based thresholding search for two popular and challenging packing problems with minimal containers: packing N identical circles in a square and packing N identical spheres in a cube. Following the penalty function approach, we handle these constrained optimization problems by solving a series of unconstrained optimization subproblems with fixed containers. The proposed algorithm relies on a two-phase search strategy that combines a thresholding search method reinforced by two general-purpose perturbation operators and a container adjustment method. The performance of the algorithm is assessed relative to a large number of benchmark instances widely studied in the literature. Computational results show a high performance of the algorithm on both problems compared with the state-of-the-art results. For circle packing, the algorithm improves 156 best-known results (new upper bounds) in the range of [Formula: see text] and matches 242 other best-known results. For sphere packing, the algorithm improves 66 best-known results in the range of [Formula: see text], whereas matching the best-known results for 124 other instances. Experimental analyses are conducted to shed light on the main search ingredients of the proposed algorithm consisting of the two-phase search strategy, the mixed perturbation and the parameters. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was supported by the National Natural Science Foundation of China [Grants 61703213 and 61933005]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1290 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0004 ) at ( http://dx.doi.org/10.5281/zenodo.7579558 ).

We investigate a kind of vehicle routing problem with constraints (VRPC) in the car-sharing mobility environment, where the problem is based on user orders, and each order has a reservation time limit and two location point transitions, origin and destination. It is a typical extended vehicle routing problem (VRP) with both time and space constraints. We consider the VRPC problem characteristics and establish a vehicle scheduling model to minimize operating costs and maximize user (or passenger) experience. To solve the scheduling model more accurately, a spatiotemporal distance representation function is defined based on the temporal and spatial properties of the customer, and a spatiotemporal distance embedded hybrid ant colony algorithm (HACA-ST) is proposed. The algorithm can be divided into two stages. First, through spatiotemporal clustering, the spatiotemporal distance between users is the main measure used to classify customers in categories, which helps provide heuristic information for problem solving. Second, an improved ant colony algorithm (ACO) is proposed to optimize the solution by combining a labor division strategy and the spatiotemporal distance function to obtain the final scheduling route. Computational analysis is carried out based on existing data sets and simulated urban instances. Compared with other heuristic algorithms, HACA-ST reduces the length of the shortest route by 2%–14% in benchmark instances. In VRPC testing instances, concerning the combined cost, HACA-ST has competitive cost compared to existing VRP-related algorithms. Finally, we provide two actual urban scenarios to further verify the effectiveness of the proposed algorithm.

A single strategy used in the firefly algorithm (FA) cannot effectively solve the complex optimal scheduling problem. Thus, we propose the FA with division of roles (DRFA). Herein, fireflies are divided into leaders, developers, and followers, while a learning strategy is assigned to each role: the leader chooses the greedy Cauchy mutation; the developer chooses two leaders randomly and uses the elite neighborhood search strategy for local development; the follower randomly selects two excellent particles for global exploration. To improve the efficiency of the fixed step size used in FA, a stepped variable step size strategy is proposed to meet different requirements of the algorithm for the step size at different stages. Role division can balance the development and exploration ability of the algorithm. The use of multiple strategies can greatly improve the versatility of the algorithm for complex optimization problems. The optimal performance of the proposed algorithm has been verified by three sets of test functions and a simulation of optimal scheduling of cascade reservoirs.

To address indeterminism in the bilevel knapsack problem, an uncertain bilevel knapsack problem (UBKP) model is proposed. Then, an uncertain solution for UBKP is proposed by defining the $${\mathcal{P}_E}$$ Nash equilibrium and $${\mathcal{P}_E}$$ Stackelberg-Nash equilibrium. To improve the computational efficiency of the uncertain solution, an evolutionary algorithm, the improved binary wolf pack algorithm, is constructed with one rule (wolf leader regulation), two operators (invert operator and move operator), and three intelligent behaviors (scouting behavior, intelligent hunting behavior, and upgrading). The UBKP model and the $${\mathcal{P}_E}$$ uncertain solution are applied to an armament transportation problem as a case study.

In this paper we present a new algorithm for the Layout Optimization Problem: this concerns the placement of circular, weighted objects inside a circular container, the two objectives being to minimize imbalance of mass and to minimize the radius of the container. This problem carries real practical significance in industrial applications (such as the design of satellites), as well as being of significant theoretical interest. We present a genetic algorithm solution and compare it with two existing nature-inspired methods, one of which is the best published algorithm for this problem. Experimental results show that our approach out-performs these existing methods in terms of both solution quality and execution time.

Mechanism path synthesis can be viewed as a pattern-matching problem in essence. Some key techniques related to its implementation such as the generation of the training path curves, the digital description of mechanism path and the clustering of path curves are discussed in detail. After generating amounts of training curves, the moment invariants of them are computed, which are defined as the attributes of the curves. By modifying the distance metric in LF model, these moment invariants are clustered based on ant clustering method. When the mechanism corresponding to the expected curve is needed, all the curves are discretized and the problem of pattern matching is solved. All these operations are integrated into a pattern-matching framework for mechanism path synthesis. A practical example of four-bar linkage path synthesis demonstrates that the ant-clustering algorithm is efficient and the proposed method is effective.