Showing papers on "Heuristic published in 2021"

AoI-Minimal Trajectory Planning and Data Collection in UAV-Assisted Wireless Powered IoT Networks

Abstract: This article investigates the unmanned aerial vehicle (UAV)-assisted wireless powered Internet-of-Things system, where a UAV takes off from a data center, flies to each of the ground sensor nodes (SNs) in order to transfer energy and collect data from the SNs, and then returns to the data center. For such a system, an optimization problem is formulated to minimize the average Age of Information (AoI) of the data collected from all ground SNs. Since the average AoI depends on the UAV’s trajectory, the time required for energy harvesting (EH) and data collection for each SN, these factors need to be optimized jointly. Moreover, instead of the traditional linear EH model, we employ a nonlinear model because the behavior of the EH circuits is nonlinear by nature. To solve this nonconvex problem, we propose to decompose it into two subproblems, i.e., a joint energy transfer and data collection time allocation problem and a UAV’s trajectory planning problem. For the first subproblem, we prove that it is convex and give an optimal solution by using Karush–Kuhn–Tucker (KKT) conditions. This solution is used as the input for the second subproblem, and we solve optimally it by designing dynamic programming (DP) and ant colony (AC) heuristic algorithms. The simulation results show that the DP-based algorithm obtains the minimal average AoI of the system, and the AC-based heuristic finds solutions with near-optimal average AoI. The results also reveal that the average AoI increases as the flying altitude of the UAV increases and linearly with the size of the collected data at each ground SN.

A rigorous and robust quantum speed-up in supervised machine learning

Abstract: Recently, several quantum machine learning algorithms have been proposed that may offer quantum speed-ups over their classical counterparts. Most of these algorithms are either heuristic or assume that data can be accessed quantum-mechanically, making it unclear whether a quantum advantage can be proven without resorting to strong assumptions. Here we construct a classification problem with which we can rigorously show that heuristic quantum kernel methods can provide an end-to-end quantum speed-up with only classical access to data. To prove the quantum speed-up, we construct a family of datasets and show that no classical learner can classify the data inverse-polynomially better than random guessing, assuming the widely believed hardness of the discrete logarithm problem. Furthermore, we construct a family of parameterized unitary circuits, which can be efficiently implemented on a fault-tolerant quantum computer, and use them to map the data samples to a quantum feature space and estimate the kernel entries. The resulting quantum classifier achieves high accuracy and is robust against additive errors in the kernel entries that arise from finite sampling statistics. Many quantum machine learning algorithms have been proposed, but it is typically unknown whether they would outperform classical methods on practical devices. A specially constructed algorithm shows that a formal quantum advantage is possible.

Pose Recognition with Cascade Transformers

Abstract: In this paper, we present a regression-based pose recognition method using cascade Transformers. One way to categorize the existing approaches in this domain is to separate them into 1). heatmap-based and 2). regression-based. In general, heatmap-based methods achieve higher accuracy but are subject to various heuristic designs (not end-to-end mostly), whereas regression-based approaches attain relatively lower accuracy but they have less intermediate non-differentiable steps. Here we utilize the encoder-decoder structure in Transformers to perform regression-based person and keypoint detection that is general-purpose and requires less heuristic design compared with the existing approaches. We demonstrate the keypoint hypothesis (query) refinement process across different self-attention layers to reveal the recursive self-attention mechanism in Transformers. In the experiments, we report competitive results for pose recognition when compared with the competing regression-based methods.

Physics-Based Generative Adversarial Models for Image Restoration and Beyond

Abstract: We present an algorithm to directly solve numerous image restoration problems (e.g., image deblurring, image dehazing, and image deraining). These problems are ill-posed, and the common assumptions for existing methods are usually based on heuristic image priors. In this paper, we show that these problems can be solved by generative models with adversarial learning. However, a straightforward formulation based on a straightforward generative adversarial network (GAN) does not perform well in these tasks, and some structures of the estimated images are usually not preserved well. Motivated by an interesting observation that the estimated results should be consistent with the observed inputs under the physics models, we propose an algorithm that guides the estimation process of a specific task within the GAN framework. The proposed model is trained in an end-to-end fashion and can be applied to a variety of image restoration and low-level vision problems. Extensive experiments demonstrate that the proposed method performs favorably against state-of-the-art algorithms.

An effective multi-start iterated greedy algorithm to minimize makespan for the distributed permutation flowshop scheduling problem with preventive maintenance

Abstract: In recent years, distributed scheduling problems have been well studied for their close connection with multi-factory production networks. However, the maintenance operations that are commonly carried out on a system to restore it to a specific state are seldom taken into consideration. In this paper, we study a distributed permutation flowshop scheduling problem with preventive maintenance operation (PM/DPFSP). A multi-start iterated greedy (MSIG) algorithm is proposed to minimize the makespan. An improved heuristic is presented for the initialization and re-initialization by adding a dropout operation to NEH2 to generate solutions with a high level of quality and disperstiveness. A destruction phase with the tournament selection and a construction phase with an enhanced strategy are introduced to avoid local optima. A local search based on three effective operators is integrated into the MSIG to reinforce local neighborhood solution exploitation. In addition, a restart strategy is adpoted if a solution has not been improved in a certain number of consecutive iterations. We conducted extensive experiments to test the performance of the presented MSIG. The computational results indicate that the presented MSIG has many promising advantages in solving the PM/DPFSP under consideration.

User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization

Abstract: Federated learning (FL), as a type of collaborative machine learning framework, is capable of preserving private data from mobile terminals (MTs) while training the data into useful models. Nevertheless, it is still possible for a curious server to infer private information from the shared models uploaded by MTs. To address this problem, we first make use of the concept of local differential privacy (LDP), and propose a user-level differential privacy (UDP) algorithm by adding artificial noise to the shared models before uploading them to servers. According to our analysis, the UDP framework can realize $(\epsilon_{i}, \delta_{i})$ -LDP for the i-th MT with adjustable privacy protection levels by varying the variances of the artificial noise processes. We then derive a theoretical convergence upper-bound for the UDP algorithm. It reveals that there exists an optimal number of communication rounds to achieve the best learning performance. More importantly, we propose a communication rounds discounting (CRD) method, which can achieve a much better trade-off between the computational complexity of searching and the convergence performance compared with the heuristic search method. Extensive experiments indicate that our UDP algorithm using the proposed CRD method can effectively improve both the training efficiency and model quality for the given privacy protection levels.

Socially Fair k-Means Clustering

Abstract: We show that the popular k-means clustering algorithm (Lloyd's heuristic), used for a variety of scientific data, can result in outcomes that are unfavorable to subgroups of data (e.g., demographic groups). Such biased clusterings can have deleterious implications for human-centric applications such as resource allocation. We present a fair k-means objective and algorithm to choose cluster centers that provide equitable costs for different groups. The algorithm, Fair-Lloyd, is a modification of Lloyd's heuristic for k-means, inheriting its simplicity, efficiency, and stability. In comparison with standard Lloyd's, we find that on benchmark datasets, Fair-Lloyd exhibits unbiased performance by ensuring that all groups have equal costs in the output k-clustering, while incurring a negligible increase in running time, thus making it a viable fair option wherever k-means is currently used.

Coverage Path Planning of Heterogeneous Unmanned Aerial Vehicles Based on Ant Colony System

Abstract: Unmanned aerial vehicle (UAV) has been extensively studied and widely adopted in practical systems owing to its effectiveness and flexibility. Although heterogeneous UAVs have an enormous advantage in improving performance and conserving energy with respect to homogeneous ones, they give rise to a complex path planning problem. Especially in large-scale cooperative search systems with multiple separated regions, coverage path planning which seeks optimal paths for UAVs to completely visit and search all of regions of interest, has a NP-hard computation complexity and is difficult to settle. In this work, we focus on the coverage path planning problem of heterogeneous UAVs, and present an ant colony system (ACS)-based algorithm to obtain good enough paths for UAVs and fully cover all regions efficiently. First, models of UAVs and regions are built, and a linear programming-based formulation is presented to exactly provide the best point-to-point flight path for each UAV. Then, inspired by the foraging behaviour of ants that they can obtain the shortest path between their nest and food, an ACS-based heuristic is presented to seek approximately optimal solutions and minimize the time consumption of tasks in the cooperative search system. Experiments on randomly generated regions have been organized to evaluate the performance of the new heuristic in terms of execution time, task completion time and deviation ratio.

Hierarchical Multiobjective Heuristic for PCB Assembly Optimization in a Beam-Head Surface Mounter.

Abstract: This article proposes a hierarchical multiobjective heuristic (HMOH) to optimize printed-circuit board assembly (PCBA) in a single beam-head surface mounter. The beam-head surface mounter is the core facility in a high-mix and low-volume PCBA line. However, as a large-scale, complex, and multiobjective combinatorial optimization problem, the PCBA optimization of the beam-head surface mounter is still a challenge. This article provides a framework for optimizing all the interrelated objectives, which has not been achieved in the existing studies. A novel decomposition strategy is applied. This helps to closely model the real-world problem as the head task assignment problem (HTAP) and the pickup-and-place sequencing problem (PAPSP). These two models consider all the factors affecting the assembly time, including the number of pickup-and-place (PAP) cycles, nozzle changes, simultaneous pickups, and the PAP distances. Specifically, HTAP consists of the nozzle assignment and component allocation, while PAPSP comprises place allocation, feeder set assignment, and place sequencing problems. Adhering strictly to the lexicographic method, the HMOH solves these subproblems in a descending order of importance of their involved objectives. Exploiting the expert knowledge, each subproblem is solved by an elaborately designed heuristic. Finally, the proposed HMOH realizes the complete and optimal PCBA decision making in real time. Using industrial PCB datasets, the superiority of HMOH is elucidated through comparison with the built-in optimizer of the widely used Samsung SM482.

Integrated Task Assignment and Path Planning for Capacitated Multi-Agent Pickup and Delivery

Abstract: Multi-agent Pickup and Delivery (MAPD) is a challenging industrial problem where a team of robots is tasked with transporting a set of tasks, each from an initial location and each to a specified target location. Appearing in the context of automated warehouse logistics and automated mail sortation, MAPD requires first deciding which robot is assigned what task (i.e., Task Assignment or TA) followed by a subsequent coordination problem where each robot must be assigned collision-free paths so as to successfully complete its assignment (i.e., Multi-Agent Path Finding or MAPF). Leading methods in this area solve MAPD sequentially: first assigning tasks, then assigning paths. In this work we propose a new coupled method where task assignment choices are informed by actual delivery costs instead of by lower-bound estimates. The main ingredients of our approach are a marginal-cost assignment heuristic and a meta-heuristic improvement strategy based on Large Neighbourhood Search. As a further contribution, we also consider a variant of the MAPD problem where each robot can carry multiple tasks instead of just one. Numerical simulations show that our approach yields efficient and timely solutions and we report significant improvement compared with other recent methods from the literature.

The Psychological Foundations of Management in Family Firms:Values, Biases, and Heuristics

Abstract: Considering the heterogeneity of family firm behaviors as reflecting the values, biases, and heuristics of individuals, we discuss the implications of the psychological foundations of management in...

A Deep Learning Trained by Genetic Algorithm to Improve the Efficiency of Path Planning for Data Collection With Multi-UAV

Abstract: To collect data of distributed sensors located at different areas in challenging scenarios through artificial way is obviously inefficient, due to the numerous labor and time. Unmanned Aerial Vehicle (UAV) emerges as a promising solution, which enables multi-UAV collect data automatically with the preassigned path. However, without a well-planned path, the required number and consumed energy of UAVs will increase dramatically. Thus, minimizing the required number and optimizing the path of UAVs, referred as multi-UAV path planning, are essential to achieve the efficient data collection. Therefore, some heuristic algorithms such as Genetic Algorithm (GA) and Ant Colony Algorithm (ACA) which works well for multi-UAV path planning have been proposed. Nevertheless, in challenging scenarios with high requirement for timeliness, the performance of convergence speed of above algorithms is imperfect, which will lead to an inefficient optimization process and delay the data collection. Deep learning (DL), once trained by enough datasets, has high solving speed without worries about convergence problems. Thus, in this paper, we propose an algorithm called Deep Learning Trained by Genetic Algorithm (DL-GA), which combines the advantages of DL and GA. GA will collect states and paths from various scenarios and then use them to train the deep neural network so that while facing the familiar scenarios, it can rapidly give the optimized path, which can satisfy high timeliness requirements. Numerous experiments demonstrate that the solving speed of DL-GA is much faster than GA almost without loss of optimization capacity and even can outperform GA under some specific conditions.

Dynamic Type Matching

Abstract: Problem definition: We consider an intermediary’s problem of dynamically matching demand and supply of heterogeneous types in a periodic-review fashion. Specifically, there are two disjoint sets of...

Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification

Abstract: Band selection refers to the process of choosing the most relevant bands in a hyperspectral image. By selecting a limited number of optimal bands, we aim at speeding up model training, improving accuracy, or both. It reduces redundancy among spectral bands while trying to preserve the original information of the image. By now, many efforts have been made to develop unsupervised band selection approaches, of which the majorities are heuristic algorithms devised by trial and error. In this article, we are interested in training an intelligent agent that, given a hyperspectral image, is capable of automatically learning policy to select an optimal band subset without any hand-engineered reasoning. To this end, we frame the problem of unsupervised band selection as a Markov decision process, propose an effective method to parameterize it, and finally solve the problem by deep reinforcement learning. Once the agent is trained, it learns a band-selection policy that guides the agent to sequentially select bands by fully exploiting the hyperspectral image and previously picked bands. Furthermore, we propose two different reward schemes for the environment simulation of deep reinforcement learning and compare them in experiments. This, to the best of our knowledge, is the first study that explores a deep reinforcement learning model for hyperspectral image analysis, thus opening a new door for future research and showcasing the great potential of deep reinforcement learning in remote sensing applications. Extensive experiments are carried out on four hyperspectral data sets, and experimental results demonstrate the effectiveness of the proposed method. The code is publicly available.

DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets

Abstract: Due to the stochasticity of human behaviors, predicting the future trajectories of road agents is challenging for autonomous driving. Recently, goal-based multi-trajectory prediction methods are proved to be effective, where they first score over-sampled goal candidates and then select a final set from them. However, these methods usually involve goal predictions based on sparse pre-defined anchors and heuristic goal selection algorithms. In this work, we propose an anchor-free and end-to-end trajectory prediction model, named DenseTNT, that directly outputs a set of trajectories from dense goal candidates. In addition, we introduce an offline optimization-based technique to provide multi-future pseudo-labels for our final online model. Experiments show that DenseTNT achieves state-of-the-art performance, ranking 1st on the Argoverse motion forecasting benchmark and being the 1st place winner of the 2021 Waymo Open Dataset Motion Prediction Challenge.

A Bilevel Ant Colony Optimization Algorithm for Capacitated Electric Vehicle Routing Problem.

Abstract: The development of electric vehicle (EV) techniques has led to a new vehicle routing problem (VRP) called the capacitated EV routing problem (CEVRP). Because of the limited number of charging stations and the limited cruising range of EVs, not only the service order of customers but also the recharging schedules of EVs should be considered. However, solving these two aspects of the problem together is very difficult. To address the above issue, we treat CEVRP as a bilevel optimization problem and propose a novel bilevel ant colony optimization algorithm in this article, which divides CEVRP into two levels of subproblem: 1) capacitated VRP and 2) fixed route vehicle charging problem. For the upper level subproblem, the electricity constraint is ignored and an order-first split-second max-min ant system algorithm is designed to generate routes that fulfill the demands of customers. For the lower level subproblem, a new effective heuristic is designed to decide the charging schedule in the generated routes to satisfy the electricity constraint. The objective values of the resultant solutions are used to update the pheromone information for the ant system algorithm in the upper level. Through good orchestration of the two components, the proposed algorithm can significantly outperform state-of-the-art algorithms on a wide range of benchmark instances.

Multitask Genetic Programming-Based Generative Hyperheuristics: A Case Study in Dynamic Scheduling.

Abstract: Evolutionary multitask learning has achieved great success due to its ability to handle multiple tasks simultaneously. However, it is rarely used in the hyperheuristic domain, which aims at generating a heuristic for a class of problems rather than solving one specific problem. The existing multitask hyperheuristic studies only focus on heuristic selection, which is not applicable to heuristic generation. To fill the gap, we propose a novel multitask generative hyperheuristic approach based on genetic programming (GP) in this article. Specifically, we introduce the idea in evolutionary multitask learning to GP hyperheuristics with a suitable evolutionary framework and individual selection pressure. In addition, an origin-based offspring reservation strategy is developed to maintain the quality of individuals for each task. To verify the effectiveness of the proposed approach, comprehensive empirical studies have been conducted on the homogeneous and heterogeneous multitask dynamic flexible job shop scheduling. The results show that the proposed algorithm can significantly improve the quality of scheduling heuristics for each task in all the examined scenarios. In addition, the evolved scheduling heuristics verify the mutual help among the tasks in a multitask scenario.

DCHG-TS: a deadline-constrained and cost-effective hybrid genetic algorithm for scientific workflow scheduling in cloud computing

Abstract: Cloud infrastructures are suitable environments for processing large scientific workflows. Nowadays, new challenges are emerging in the field of optimizing workflows such that it can meet user’s service quality requirements. The key to workflow optimization is the scheduling of workflow tasks, which is a famous NP-hard problem. Although several methods have been proposed based on the genetic algorithm for task scheduling in clouds, our proposed method is more efficient than other proposed methods due to the use of new genetic operators as well as modified genetic operators and the use of load balancing routine. Moreover, a solution obtained from a heuristic used as one of the initial population chromosomes and an efficient routine also used for generating the rest of the primary population chromosomes. An adaptive fitness function is used that takes into account both cost and makespan. The algorithm introduced in this paper utilizes a load balancing routine to maximize resources’ efficiency at execution time. The performance of the proposed algorithm is evaluated by comparing the results with state of the art algorithms of this field, and the results indicate that the proposed algorithm has remarkable superiority in comparison to other algorithms and performs task scheduling with the least makespan and cost.

A Generalized Voronoi Diagram based Efficient Heuristic Path Planning Method for RRTs in Mobile Robots

Abstract: In this paper, we present an efficient heuristic path planning algorithm incorporating the Rapidly-exploring Random Tree and the Generalized Voronoi Diagram. Different from other heuristic algorithms that only work in certain environments or depend on specified parameter setting, the proposed algorithm can automatically identify the environment feature and provide a reasonable heuristic path. First, the given environment is initialized with a lightweight feature extraction from the GVD, which guarantees that any state in the free space can be connected to the feature graph without any collision. Second, a feature matrix is proposed to represent the connection among feature nodes and a corresponding feature node fusion technique is utilized to delete the redundant nodes. Third, based on the GVD feature matrix, a heuristic path planning algorithm is presented. This heuristic path is used to guide and achieve real-time motion planning. As a meta algorithm, the proposed GVD feature matrix can be also applied into other RRT methods to further improve the performance. Through a series of simulation studies and real-world implementations, it is confirmed that the proposed algorithm achieves better performance in heuristic path planning, feature extraction of free space and real-time motion planning.

Antecedents of Trust and Adoption Intention toward Artificially Intelligent Recommendation Systems in Travel Planning: A Heuristic–Systematic Model:

Abstract: Drawing on the dual process theory, this study investigates the impacts of systematic and heuristic cues on travelers’ cognitive trust, emotional trust, and adoption intention toward artificial int...

Personalized Cross-Silo Federated Learning on Non-IID Data.

Abstract: Non-IID data present a tough challenge for federated learning. In this paper, we explore a novel idea of facilitating pairwise collaborations between clients with similar data. We propose FedAMP, a new method employing federated attentive message passing to facilitate similar clients to collaborate more. We establish the convergence of FedAMP for both convex and non-convex models, and propose a heuristic method to further improve the performance of FedAMP when clients adopt deep neural networks as personalized models. Our extensive experiments on benchmark data sets demonstrate the superior performance of the proposed methods.

Ant-TD: Ant colony optimization plus temporal difference reinforcement learning for multi-label feature selection

Abstract: In recent years, multi-label learning becomes a trending topic in machine learning and data mining. This type of learning deals with data that each instance is associated with more than one label. Feature selection is a pre-processing method, which can significantly improve the performance of the multi-label classification. In this paper, we propose a new multi-label feature selection method based on Ant Colony Optimization (ACO). The proposed method makes a significant difference among all ACO-based feature selection methods so that instead of using a static heuristic function, it uses a heuristic learning approach. Because heuristic functions influence the decision-making process of the ACO during the search process, using a heuristic learning approach can help the algorithm to search better in the search space. Here we propose a heuristic learning approach for the ACO heuristic function, which learns from experiences directly by using the Temporal Difference (TD) reinforcement learning algorithm. For heuristic learning, we need to model the ACO problem into a reinforcement learning problem. Thus, we model the feature selection search space into a Markovian Decision Process (MDP) where features represent the states (S), and selecting the unvisited features by each ant represents a set of actions (A). Reward signals (R) are composed of two criteria when ants take action. The ACO state transition rule, which is a combination of both probabilistic and greedy rules, forms the transition function (T) in MDP. In addition to the pheromone that is updated by the “global updating rule” in the ACO, the state-value function (V) is directly updated by the temporal difference formulation to form a learned heuristic function. We conduct various experiments on nine benchmark data and compare the classification performance over three multi-label evaluation measures against nine state-of-the-art multi-label feature selection methods. The results show that the proposed method outperforms competing methods significantly.

A genetic programming hyper-heuristic for the distributed assembly permutation flow-shop scheduling problem with sequence dependent setup times

Abstract: In this paper, a genetic programming hyper heuristic (GP-HH) algorithm is proposed to solve the distributed assembly permutation flow-shop scheduling problem with sequence dependent setup times (DAPFSP-SDST) and the objective of makespan minimization. The main idea is to use genetic programming (GP) as the high level strategy to generate heuristic sequences from a pre-designed low-level heuristics (LLHs) set. In each generation, the heuristic sequences are evolved by GP and then successively operated on the solution space for better solutions. Additionally, simulated annealing is embedded into each LLH to improve the local search ability. An effective encoding and decoding pair is also presented for the algorithm to obtain feasible schedules. Finally, computational simulation and comparison are both carried out on a benchmark set and the results demonstrate the effectiveness of the proposed GP-HH. The best-known solutions are updated for 333 out of the 540 benchmark instances.

Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem.

Abstract: Existing deep reinforcement learning (DRL)-based methods for solving the capacitated vehicle routing problem (CVRP) intrinsically cope with a homogeneous vehicle fleet, in which the fleet is assumed as repetitions of a single vehicle. Hence, their key to construct a solution solely lies in the selection of the next node (customer) to visit excluding the selection of vehicle. However, vehicles in real-world scenarios are likely to be heterogeneous with different characteristics that affect their capacity (or travel speed), rendering existing DRL methods less effective. In this article, we tackle heterogeneous CVRP (HCVRP), where vehicles are mainly characterized by different capacities. We consider both min-max and min-sum objectives for HCVRP, which aim to minimize the longest or total travel time of the vehicle(s) in the fleet. To solve those problems, we propose a DRL method based on the attention mechanism with a vehicle selection decoder accounting for the heterogeneous fleet constraint and a node selection decoder accounting for the route construction, which learns to construct a solution by automatically selecting both a vehicle and a node for this vehicle at each step. Experimental results based on randomly generated instances show that, with desirable generalization to various problem sizes, our method outperforms the state-of-the-art DRL method and most of the conventional heuristics, and also delivers competitive performance against the state-of-the-art heuristic method, that is, slack induction by string removal. In addition, the results of extended experiments demonstrate that our method is also able to solve CVRPLib instances with satisfactory performance.

Integrated Task Assignment and Path Planning for Capacitated Multi-Agent Pickup and Delivery

Abstract: Multi-agent Pickup and Delivery (MAPD) is a challenging industrial problem where a team of robots is tasked with transporting a set of tasks, each from an initial location and each to a specified target location. Appearing in the context of automated warehouse logistics and automated mail sortation, MAPD requires first deciding which robot is assigned what task (i.e., Task Assignment or TA) followed by a subsequent coordination problem where each robot must be assigned collision-free paths so as to successfully complete its assignment (i.e., Multi-Agent Path Finding or MAPF). Leading methods in this area solve MAPD sequentially: first assigning tasks, then assigning paths. In this work we propose a new coupled method where task assignment choices are informed by actual delivery costs instead of by lower-bound estimates. The main ingredients of our approach are a marginal-cost assignment heuristic and a meta-heuristic improvement strategy based on Large Neighbourhood Search. As a further contribution, we also consider a variant of the MAPD problem where each robot can carry multiple tasks instead of just one. Numerical simulations show that our approach yields efficient and timely solutions and we report significant improvement compared with other recent methods from the literature.