Human-robot interactive target-oriented grasping in unstructured environments, guided by natural language, is crucial for enabling intelligent robotic arms to perform tasks safely and efficiently. However, it remains a challenge for robotic arms to comprehend human instructions and execute corresponding grasping actions. In this paper, we propose QwenGrasp, a novel system that uses a large vision-language model to align workspace images with textual instructions. This alignment enables QwenGrasp to perform accurate 6DoF grasping on the specified target object. Additionally, we introduce Masked REGNet, which incorporates target-object location information into the network to generate precise grasp poses and ensure high grasp quality. Through extensive real-world experiments, QwenGrasp achieves over 90% success across six diverse instruction types. The results highlight QwenGrasp’s ability to understand human intent and execute precise grasping actions. Notably, it outperforms other target-oriented methods in both performance and instruction comprehension. Even when given vague descriptions, directional cues, or complex instructions, QwenGrasp reliably identifies and grasps the correct object. An ablation study further confirms the importance of each component, with all contributing significantly to robust and high-quality grasping.

Automated design of metaheuristic algorithms offers an attractive avenue to reduce human effort and gain enhanced performance beyond human intuition. Current automated methods design algorithms within a fixed structure and operate from scratch. This poses a clear gap toward fully discovering potentials over the metaheuristic family and fertilizing from prior design experience. To bridge the gap, this article proposes an autoregressive learning-based designer for automated design of metaheuristic algorithms. Our designer formulates metaheuristic algorithm design as a sequence generation task, and harnesses an autoregressive generative network to handle the task. This offers two advances. First, through autoregressive inference, the designer generates algorithms with diverse lengths and structures, enabling to fully discover potentials over the metaheuristic family. Second, prior design knowledge learned and accumulated in neurons of the designer can be retrieved for designing algorithms for future problems, paving the way to continual design of algorithms for open-ended problem solving. Extensive experiments on numeral benchmarks and real-world problems reveal that the proposed designer generates algorithms that outperform all human-created baselines on 24 out of 25 test problems. The generated algorithms display various structures and behaviors, reasonably fitting for different problem-solving contexts. Code is available athttps://github.com/auto4opt/ALDes.

In this paper, we present an open-source pure-Python library called PyPop7 for black-box optimization (BBO). As population-based methods (e.g., evolutionary algorithms, swarm intelligence, and pattern search) become increasingly popular for BBO, the design goal of PyPop7 is to provide a unified API and elegant implementations for them, particularly in challenging high-dimensional scenarios. Since these population-based methods easily suffer from the notorious curse of dimensionality owing to random sampling as one of core operations for most of them, recently various improvements and enhancements have been proposed to alleviate this issue more or less mainly via exploiting possible problem structures: such as, decomposition of search distribution or space, low-memory approximation, low-rank metric learning, variance reduction, ensemble of random subspaces, model self-adaptation, and fitness smoothing. These novel sampling strategies could better exploit different problem structures in high-dimensional search space and therefore they often result in faster rates of convergence and/or better qualities of solution for large-scale BBO. Now PyPop7 has covered many of these important advances on a set of well-established BBO algorithm families and also provided an open-access interface to adding the latest or missed black-box optimizers for further functionality extensions. Its well-designed source code (under GPL-3.0 license) and full-fledged online documents (under CC-BY 4.0 license) have been freely available at https://github.com/Evolutionary-Intelligence/pypop and https://pypop.readthedocs.io, respectively.

Gridless methods show great superiority in line spectral estimation. These methods need to solve an atomic$l_{0}$norm (i.e., the continuous analog of$l_{0}$norm) minimization problem to estimate frequencies and model order. Since this problem is NP-hard to compute, relaxations of the atomic$l_{0}$norm, such as the nuclear norm and reweighted atomic norm, have been employed for promoting sparsity. However, the relaxations give rise to a resolution limit, subsequently leading to biased model order and convergence error. To overcome the above shortcomings of relaxation, we propose a novel idea of simultaneously estimating the frequencies and model order using the atomic$l_{0}$norm. To accomplish this idea, we build a multiobjective optimization model. The measurement error and the atomic$l_{0}$norm are taken as the two optimization objectives. The proposed model directly exploits the model order via the atomic$l_{0}$norm, thus breaking the resolution limit. We further design a variable-length evolutionary algorithm to solve the proposed model, which includes two innovations. One is a variable-length coding and search strategy. It flexibly codes and interactively searches diverse solutions with different model orders. These solutions act as steppingstones that helpfully exploring the variable and open-ended frequency search space and provide extensive potentials toward the optima. Another innovation is a model-order pruning mechanism, which heuristically prunes less contributive frequencies within the solutions, thus significantly enhancing convergence and diversity. Simulation results confirm the superiority of our approach in both frequency estimation and model-order selection.

In the post-Moore era, main performance gains of black-box optimizers are increasingly depending on parallelism, especially for large-scale optimization (LSO). Here we propose to parallelize the well-established covariance matrix adaptation evolution strategy (CMA-ES) and in particular its one latest LSO variant called limited-memory CMA-ES (LM-CMA). To achieve efficiency while approximating its powerful invariance property, we present a multilevel learning-based meta-framework for distributed LM-CMA. Owing to its hierarchically organized structure, Meta-ES is well-suited to implement our distributed meta-framework, wherein the outer-ES controls strategy parameters while all parallel inner-ESs run the serial LM-CMA with different settings. For the distribution mean update of the outer-ES, both the elitist and multi-recombination strategy are used in parallel to avoid stagnation and regression, respectively. To exploit spatiotemporal information, the global step-size adaptation combines Meta-ES with the parallel cumulative step-size adaptation. After each isolation time, our meta-framework employs both the structure and parameter learning strategy to combine aligned evolution paths for CMA reconstruction. Experiments on a set of large-scale benchmarking functions with memory-intensive evaluations, arguably reflecting many data-driven optimization problems, validate the benefits (e.g., effectiveness w.r.t. solution quality, and adaptability w.r.t. second-order learning) and costs of our meta-framework.

In many clustering scenes, data samples' attribute values change over time. For such data, we are often interested in obtaining a partition for each time step and tracking the dynamic change of partitions. Normally, a smooth change is assumed for data to have a temporal smooth nature. Existing algorithms consider the temporal smoothness as an a priori preference and bias the search toward the preferred direction. This a priori manner leads to a risk of converging to an unexpected region because it is not always the case that a reasonable preference can be elicited given the little prior knowledge about the data. To address this issue, this article proposes a new clustering framework called evolutionary robust clustering over time. One significant innovation of the proposed framework is processing the temporal smoothness in an a posteriori manner, which avoids unexpected convergence that occurs in existing algorithms. Furthermore, the proposed framework automatically infers the a posteriori preference to temporal smoothness without data's affinity matrix and predefined parameters, which holds better applicability and efficiency. The effectiveness and efficiency of the proposed framework are confirmed by comparing with state-of-the-art algorithms on both synthetic and real datasets.

Unmanned Aerial Vehicles (UAVs), or drones, have the potential to be applied to delivery services, which are expected to bring economic benefits. One of the key issues is planning routes for vehicles and drones with specific constraints and objectives, known as Vehicle Routing Problem with Drones (VRPD). This paper considers a scenario involving multiple trucks, multiple UAV stations, and UAVs within each station to serve the customers. A bi-level approach that combines the Brain Storm optimization algorithm and Adaptive Large Neighborhood Search is proposed by designing the solution representation, new solution generation mechanism, and other operations. The experimental results show that the proposed method has the ability to solve the problem and deserves further development.

Dynamic multiobjective optimization problems are challenging due to their fast convergence and diversity maintenance requirements. Prediction-based evolutionary algorithms currently gain much attention for meeting these requirements. However, it is not always the case that an elaborate predictor is suitable for different problems and the quality of historical solutions is sufficient to support prediction, which limits the availability of prediction-based methods over various problems. Faced with these issues, this article proposes a knowledge learning strategy for change response in the dynamic multiobjective optimization. Unlike prediction approaches that estimate the future optima from previously obtained solutions, in the proposed strategy, we react to changes via learning from the historical search process. We introduce a method to extract the knowledge within the previous search experience. The extracted knowledge can accelerate convergence as well as introduce diversity for the optimization of the future environment. We conduct a comprehensive experiment on comparing the proposed strategy with the state-of-the-art algorithms. Results demonstrate the better performance of the proposed strategy in terms of solution quality and computational efficiency.

The Vehicle Routing Problem with Time Windows (VRPTW) is NP-hard which has many real-world applications in logistics and transportation. The traditional VRPTW is defined on a complete graph with customers as nodes, but in the real-world, VRPTWs are more based on road networks. To better simulate the real-world scenarios, this paper studies the VRPTW on road networks. Most researchers solve the VRPTW on road networks by utilizing exact algorithms which can not deal with large size problem. In this paper, a hybrid BSO-ACS algorithm, which combines Brain Storm optimization (BSO), Ant Colony System (ACS) and Local Search (LS), is proposed to solve the VRPTW on road networks. A set of instances based on the road network of southwest Shenzhen, China are generated as benchmark problems. The computational experiments demonstrate the effectiveness of the proposed algorithm.

Evolutionary algorithms have shown their promise in coping with many-objective optimization problems. However, the strategies of balancing convergence and diversity and the effectiveness of handling problems with irregular Pareto fronts (PFs) are still far from perfect. To address these issues, this paper proposes an adaptive sorting-based evolutionary algorithm based on the idea of decomposition. First, we propose an adaptive sorting-based environmental selection strategy. Solutions in each subpopulation (partitioned by reference vectors) are sorted based on their convergence. Those with better convergence are further sorted based on their diversity, then being selected according to their sorting levels. Second, we provide an adaptive promising subpopulation sorting-based environmental selection strategy for problems which may have irregular PFs. This strategy provides additional sorting-based selection effort on promising subpopulations after the general environmental selection process. Third, we extend the algorithm to handle constraints. Finally, we conduct an extensive experimental study on the proposed algorithm by comparing with start-of-the-state algorithms. Results demonstrate the superiority of the proposed algorithm.

High-dimensional data clustering is of great importance in the big data era. Multi-objective evolutionary soft subspace clustering (SSC) algorithms have shown promise in handling such datasets, but the objective functions and local search strategies used have not yet been well investigated. To consider these issues, this paper proposes an improved multiobjective evolutionary approach with new objective function and local search operator for clustering high-dimensional data. First, a new objective function is provided, which optimizes the clustering validity indexes and additional item simultaneously to overcome the difficulty of coefficient settings in the objective functions of existing SSC approaches. Second, an improved local search operator is introduced, which updates the weights of features by considering both the within-class compactness and between-class separation to capture a more comprehensive data structure. An experimental study with comparison with state-of-the-art SSC methods demonstrates the efficiency of the proposed approach.