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The coverage path planning of UAVs is a complex optimization problem in practice, especially for those involving multiple target areas. It is challenging to comprehensively plan the inter-area visiting order and the intra-area coverage paths simultaneously. Due to the battery limitation, usually the task can hardly be finished by a single UAV, and instead a fleet of UAVs are required. In this paper, we first formulate an energy-aware multi-UAV multi-area coverage path planning (EM2CPP) model, in order to characterize the practical path planning requirements of UAVs in complex conditions. Subsequently, to accomplish the optimization task, we propose a bipartite cooperative coevolution (BiCC) algorithm that coevolves an inter-area path planning and an intra-area path planning components to obtain good solutions. The basic operators in BiCC, such as the initialization and the reproduction operators, are tailored for the task of EM2CPP. Besides, we also develop a fast heuristic algorithm for EM2CPP, which is able to produce approximate solutions in a short time. Simulations on real-world datasets validate the good performance of the proposed methods.

Sensor activity scheduling is critical for prolonging the lifetime of wireless sensor networks (WSNs). However, most existing methods assume sensors to have one fixed sensing range. Prevalence of sensors with adjustable sensing ranges posts two new challenges to the topic: 1) expanded search space, due to the rise in the number of possible activation modes and 2) more complex energy allocation, as the sensors differ in the energy consumption rate when using different sensing ranges. These two challenges make it hard to directly solve the lifetime maximization problem of WSNs with range-adjustable sensors (LM-RASs). This article proposes a neighborhood-based estimation of distribution algorithm (NEDA) to address it in a recursive manner. In NEDA, each individual represents a coverage scheme in which the sensors are selectively activated to monitor all the targets. A linear programming (LP) model is built to assign activation time to the schemes in the population so that their sum, the network lifetime, can be maximized conditioned on the current population. Using the activation time derived from LP as individual fitness, the NEDA is driven to seek coverage schemes promising for prolonging the network lifetime. The network lifetime is thus optimized by repeating the steps of the coverage scheme evolution and LP model solving. To encourage the search for diverse coverage schemes, a neighborhood sampling strategy is introduced. Besides, a heuristic repair strategy is designed to fine-tune the existing schemes for further improving the search efficiency. Experimental results on WSNs of different scales show that NEDA outperforms state-of-the-art approaches. It is also expected that NEDA can serve as a potential framework for solving other flexible LP problems that share the same structure with LM-RAS.

Real-time taxi–passenger matching plays a critical role in modern taxi dispatch systems. Currently, the greedy strategy is widely adopted, which limits the quality of the service (QoS) and the profit of the entire system. There are two crucial tasks in this system: 1) the pairwise prioritization and 2) the matching of taxi–passenger pairs. In this paper, we develop a two-stage taxi–passenger matching system to deal with these two tasks. In the first stage, we design a fuzzy controller to assign a priority score to each taxi–passenger pair in real time. To ensure its performance on providing good QoS and profit, the fuzzy controller is optimized by an offline differential evolution algorithm. New individual representation is designed to optimize the membership functions and fuzzy rule base simultaneously. To accelerate the optimization process, the algorithm is implemented in a parallel way. Then, in the second stage, considering the priority scores as weights in the bipartite graph of taxi and passenger sets, we further apply a polynomial Kuhn–Munkres algorithm to find the maximum weight perfect matching in the bipartite graph. Simulated results validate the effectiveness of the proposed algorithm, which is able to enhance the QoS provided by the taxi system and improve the profit gained by the taxi service company.

As the cornerstone for joint dimension reduction and feature extraction, extensive linear projection algorithms were proposed to fit various requirements. When being applied to image data, however, existing methods suffer from representation deficiency since the multi-way structure of the data is (partially) neglected. To solve this problem, we propose a novel Low-Rank Preserving t-Linear Projection (LRP-tP) model that preserves the intrinsic structure of the image data using t-product-based operations. The proposed model advances in four aspects: 1) LRP-tP learns the t-linear projection directly from the tensorial dataset so as to exploit the correlation among the multi-way data structure simultaneously; 2) to cope with the widely spread data errors, e.g., noise and corruptions, the robustness of LRP-tP is enhanced via self-representation learning; 3) LRP-tP is endowed with good discriminative ability by integrating the empirical classification error into the learning procedure; 4) an adaptive graph considering the similarity and locality of the data is jointly learned to precisely portray the data affinity. We devise an efficient algorithm to solve the proposed LRP-tP model using the alternating direction method of multipliers. Extensive experiments on image feature extraction have demonstrated the superiority of LRP-tP compared to the state-of-the-arts.

Automatic itinerary planning is a crucial and challenging issue in tourism. This paper proposes a novel automatic planning method to suggest multiple itineraries that satisfy the specific demands of tourists. First, a multiple-itinerary planning model is developed, which provides three customized goals for a tourist to choose and supports generating multiple $D$ -day trips. The model makes fewer assumptions than the literature works did, while it provides more flexibility to the tourists. Then, based on the multiple-itinerary planning model, we design a niching genetic evolution approach to accomplish the automatic itinerary planning task. The genetic evolution approach guarantees a high search efficiency, while the niching strategy facilitates maintaining the population diversity. Consequently, the resultant algorithm can finally provide a number of diverse and superior solutions. Experimental results on real-world datasets show that our proposed algorithm not only outperforms state-of-the-art methods in considering different user-specified goals, but it is also capable of generating a set of diverse itineraries for the tourist to select. Additional experiments further verify the scalability of the proposed algorithm in terms of the problem size and the optimization objective.

Neighborhood information plays an important role in improving the performance of evolutionary computation in various optimization scenarios, particularly in the context of multimodal optimization. Several neighborhood concepts, i.e., index-based neighborhood, nearest neighborhood, and fuzzy neighborhood, have been studied and engaged in the design of niching methods. However, the use of these neighborhood concepts requires the specification of some problem-related parameters, which is difficult to determine without a prior knowledge. In this paper, we introduce a new neighborhood concept based on a geometrical construction called Voronoi diagram. The new concept offers two advantages at the expense of increasing the computational complexity to a higher level. It eliminates the need of additional parameters and it is more informative than the existing ones. The information provided by the Voronoi neighbors of an individual can be exploited to estimate the evolutionary state. Based on the information, we divide the population into three groups and assign each group a different reproduction strategy to support the exploration and exploitation of the search space. We show the use of the concept in the design of an effective evolutionary algorithm for multimodal optimization. The experiments have been conducted to investigate the performance of the algorithm. The results reveal that the proposed algorithm compare favorably with the state-of-the-art algorithms designed based on other types of neighborhood concepts.

Linear discriminant analysis has been incorporated with various representations and measurements for dimension reduction and feature extraction. In this paper, we propose two-dimensional quaternion sparse discriminant analysis (2D-QSDA) that meets the requirements of representing RGB and RGB-D images. 2D-QSDA advances in three aspects: 1) including sparse regularization, 2D-QSDA relies only on the important variables, and thus shows good generalization ability to the out-of-sample data which are unseen during the training phase; 2) benefited from quaternion representation, 2D-QSDA well preserves the high order correlation among different image channels and provides a unified approach to extract features from RGB and RGB-D images; 3) the spatial structure of the input images is also retained via the matrix-based processing. We tackle the constrained trace ratio problem of 2D-QSDA by solving a corresponding constrained trace difference problem, which is then transformed into a quaternion sparse regression (QSR) model. Afterward, we reformulate the QSR model to an equivalent complex form to avoid the processing of the complicated structure of quaternions. A nested iterative algorithm is designed to learn the solution of 2D-QSDA in the complex space and then we convert this solution back to the quaternion domain. To improve the separability of 2D-QSDA, we further propose 2D-QSDA w using the weighted pairwise between-class distances. Extensive experiments on RGB and RGB-D databases demonstrate the effectiveness of 2D-QSDA and 2D-QSDA w compared with peer competitors.

As the mutation strategy and algorithmic parameters in differential evolution (DE) are sensitive to the problems being solved, a hot research topic is to adaptively control the strategy and parameters according to the requirements of the problem. In the literature, most adaptive DE use either historical experiences of the population or heuristic information of the individuals to promote adaptation. In this paper, we develop a novel variant of adaptive DE, utilizing both the historical experience and heuristic information for the adaptation. In this novel historical and heuristic DE (HHDE), each individual dynamically adjusts its mutation strategy and associated parameters not only by learning from previous successful experience of the whole population, but also according to heuristic information related with its own current state. These help the algorithm select a more suitable mutation strategy and determinate better parameters for each individual in different evolutionary stages. The performance of the proposed HHDE is extensively evaluated on 30 benchmark functions with different dimensions. Experimental results confirm the competitiveness of the proposed algorithm to a number of DE variants.

The cooperative coevolution (CC) framework achieves a promising performance in solving large scale global optimization problems. The framework encounters difficulties on nonseparable problems, where variables interact with each other. Using the static grouping methods, variables will be theoretically grouped into one big subcomponent, whereas the random grouping strategy endures low efficiency. In this paper, a dynamic CC framework is proposed to tackle the challenge. The proposed framework works in a computationally efficient manner, in which the computational resources are allocated to a series of elitist subcomponents consisting of superior variables. First, a novel estimation method is proposed to evaluate the contribution of variables using the historical information of the best overall fitness. Based on the contribution and the interaction information, a dynamic grouping strategy is conducted to construct the dynamic subcomponent that evolves in the next evolutionary period. The constructed subcomponents are different from each other, and therefore the required parameters to control the optimization of each subcomponent vary a lot in each evolutionary period. A stage-by-stage parameter adaptation strategy is proposed to adapt the optimizer to the dynamic optimization environment. Experimental results indicate that the proposed framework achieves competitive results compared with the state-of-the-art CC frameworks.

Cross-docks serve as distribution centers where shipments from different vendors are first consolidated according to their destinations, and then delivered to the retailers directly, with little or no storage in between. A critical problem encountered in the operation of cross-docks is the assignment of receiving and shipping doors, which greatly influences the labor or machinery cost of transferring the shipments between inbound and outbound transports. We show that the cross-dock door assignment problem (CDAP) is strictly non-deterministic polynomial-time complete. Although some deterministic algorithms have been reported to handle small-scale problems, the solutions to the middle- and large-scale CDAPs progressed at a slow pace. In this paper, we develop a nature-inspired dual-colony ant algorithm for CDAP, in which the two colonies of ants cooperatively search the optimal assignments of receiving and shipping doors to minimize the transferring costs of shipments. A collaborative local search strategy is designed and incorporated into the algorithm to enhance the search efficiency. Experiments have been conducted on a number of problem instances with different cross-dock sizes and freight flow patterns. The results show that the proposed algorithm is very competitive and can provide better solutions than the state-of-the-art heuristic algorithms.

Mobile crowdsourcing is an emerging crowdsourcing paradigm, which generates large-scale sensing tasks and sensing data. One of the major issues in mobile crowdsourcing is how to maximize social welfare through selecting appropriate sensing tasks for crowd workers and selecting appropriate workers for sensing tasks such that it can improve the effectiveness and efficiency of mobile crowdsourcing. This paper proposes an incentive mechanism to maximize social welfare for mobile crowdsourcing and, respectively, investigates worker-centric task selection and platform-centric worker selection. This paper applies an optimization algorithm in task selection for mobile crowdsourcing systems. A discrete particle swarm optimization (DPSO) algorithm for worker-centric task selection is designed to maximize the utilities of workers. In addition, a platform-centric worker selection method, which integrates multiattribute auction and two-stage auction, is proposed to maximize the utility of the platform. The performance of the proposed incentive mechanism is evaluated through experiments. The experimental results show that the proposed incentive mechanism can improve the efficiency and truthfulness of mobile crowdsourcing effectively.

Recent studies have shown the effectiveness of using depth information in salient object detection. However, the most commonly seen images so far are still RGB images that do not contain the depth data. Meanwhile, the human brain can extract the geometric model of a scene from an RGB-only image and hence provides a 3D perception of the scene. Inspired by this observation, we propose a new concept named RGB-'D' saliency detection, which derives pseudo depth from the RGB images and then performs 3D saliency detection. The pseudo depth can be utilized as image features, prior knowledge, an additional image channel, or independent depth-induced models to boost the performance of traditional RGB saliency models. As an illustration, we develop a new salient object detection algorithm that uses the pseudo depth to derive a depth-driven background prior and a depth contrast feature. Extensive experiments on several standard databases validate the promising performance of the proposed algorithm. In addition, we also adapt two supervised RGB saliency models to our RGB-'D' saliency framework for performance enhancement. The results further demonstrate the generalization ability of the proposed RGB-'D' saliency framework.