Greedy Strategy Research Articles

Physics-Based Greedy Algorithm for Reliable Fast Frequency Sweep in Electromagnetics via the Reduced-Basis Method

A reliable fast frequency sweep model order reduction (MOR) process is detailed. A reduced-basis approximation is taken into account to accurately and efficiently describe the electromagnetic field in a frequency band of analysis. The reduced-basis space is made up of snapshots of the electric field adaptively chosen on a physics-based greedy algorithm. Following a theoretical analysis starting from time-harmonic Maxwell’s equations, in- band eigenresonances are shown to significantly contribute to the electric field in the band of analysis. An impedance matrix transfer function description of the microwave circuit is used and a pole-residue expansion of this matrix-valued impedance transfer function is detailed. A fundamental physical property is identified, from which a straightforward a posteriori error estimator arises, namely, matrix residues in the pole-residue expansion of the impedance transfer function must be rank-1. This cheap computation is well-suited for fast greedy algorithm strategies in adaptive construction of the reduced-basis approximation. Contrary to what has been previously done, a straightforward physics-based greedy algorithm that does not need to scan the whole frequency band of interest to enrich the reduced-basis approximation is proposed. Finally, actual microwave applications illustrate the capabilities and efficiency of the new physics-based a posteriori error estimator in the proposed methodology.

Cost effective hybrid genetic algorithm for workflow scheduling in cloud

Cloud computing plays a significant role in everyone’s lifestyle by snugly linking communities, information, and trades across the globe. Due to its NP-hard nature, recognizing the optimal solution for workflow scheduling in the cloud is a challenging area. We proposed a hybrid meta-heuristic cost-effective load-balanced approach to schedule workflow in a heterogeneous environment. Our model is based on a genetic algorithm integrated with predict earliest finish time (PEFT) to minimize makespan. Instead of assigning the task randomly to a virtual machine, we apply a greedy strategy that assigns the task to the lowest-loaded virtual machine. After completing the mutation operation, we verify the dependency constraint instead of each crossover operation, which yields a better outcome. The proposed model incorporates the virtual machine’s performance variance as well as acquisition delay, which concedes the minimum makespan and computing cost. One of the most astounding aspects of our cost-effective hybrid genetic algorithm (CHGA) is its capacity to anticipate by creating an optimistic cost table (OCT) while maintaining quadratic time complexity. Based on the results of our meticulous experiments on some real-world workflow benchmarks and comprehensive analysis of some recently successful scheduling algorithms, we concluded that the performance of our CHGA is melodious. CHGA is 14.58188%, 11.40224%, 11.75306%, and 9.78841% cheaper than standard Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Cost Effective Genetic Algorithm(CEGA), and Cost-Effective Load-balanced Genetic Algorithm (CLGA), respectively.

Energy-Aware Cloud-Edge Collaborative Task Offloading with Adjustable Base Station Radii in Smart Cities

In smart cities, the computing power and battery life of terminal devices (TDs) can be effectively enhanced by offloading tasks to nearby base stations (BSs) with richer resources. With the goal of TDs being fully served and achieving low-carbon energy savings for the system, this paper investigates task offloading in cloud-edge collaborative heterogeneous scenarios with multiple BSs and TDs. According to the proportional relationship between the energy and coverage radii of BSs, a complete coverage task offloading model with adjustable BS radii is proposed. The task offloading problem is formulated as an integer linear program with multidimensional resource constraints to minimize the sum of energy consumption of BS coverage, offloading tasks to BSs and the cloud data center (CC). Since this task offloading problem is NP-hard, two approximate algorithms with polynomial time complexity are designed based on the greedy strategy of seeking the most energy-effective disk and the primal–dual method of constructing primal feasible solutions according to dual feasible solutions. Experimental results show that both the greedy and primal–dual algorithms can achieve good approximation performance, but each of them has its own advantages due to different design principles. The former is superior in execution time and energy consumption, while the latter has advantages in balancing loads among BSs and alleviating core network bandwidth pressure.

Harris hawks optimization based on global cross-variation and tent mapping.

Harris hawks optimization (HHO) is a new meta-heuristic algorithm that builds a model by imitating the predation process of Harris hawks. In order to solve the problems of poor convergence speed caused by uniform choice position update formula in the exploration stage of basic HHO and falling into local optimization caused by insufficient population richness in the later stage of the algorithm, a Harris hawks optimization based on global cross-variation and tent mapping (CRTHHO) is proposed in this paper. Firstly, the tent mapping is introduced in the exploration stage to optimize random parameter q to speed up the convergence in the early stage. Secondly, the crossover mutation operator is introduced to cross and mutate the global optimal position in each iteration process. The greedy strategy is used to select, which prevents the algorithm from falling into local optimal because of skipping the optimal solution and improves the convergence accuracy of the algorithm. In order to investigate the performance of CRTHHO, experiments are carried out on ten benchmark functions and the CEC2017 test set. Experimental results show that the CRTHHO algorithm performs better than the HHO algorithm and is competitive with five advanced meta-heuristic algorithms.

Two stochastic optimization methods for shift design with uncertain demand

The purpose of this paper is to investigate the shift design problem with a probability constraint on demand satisfaction and to design the corresponding stochastic model so that the staffing for each shift can cope with stochastic demand. To solve this stochastic model, we propose two solution methods: a method involving average sample approximation and a two-stage heuristic algorithm based on statistics with a greedy strategy. Numerical results show that both methods can solve the stochastic model of this paper well, and the sample average approximation method outperforms the two-stage heuristic algorithm in terms of cost optimization. However, as the number of scenarios used to approximate realistic situations increases, the superiority of the two-stage heuristic algorithm in terms of solution speed becomes progressively more significant.

Spatio-temporal nonconvex penalty adaptive chirp mode decomposition for signal decomposition of cross-frequency coupled sources in seafloor dynamic engineering

Electromagnetic field noise and clutter generated from the motion of ocean waves are the main obstacles in the research of magnetotelluric dynamic analysis, and it is difficult to extract the crossed instantaneous frequencies (IFs) of underwater electromagnetic detected (UEMD) data due to the limited resolution of the current time-frequency techniques. To alleviate this bottleneck issue, a new spatio-temporal nonconvex penalty adaptive chirp mode decomposition (STNP-ACMD) is originally proposed for separating each mono-component individually from a complicated multi-component with severely crossed IFs or overlapped components, in this paper. Specifically, the idea of a nonconvex penalty greedy strategy is incorporated into the vanilla ACMD method by using a recursive mode extraction scheme, and the fractional-order characteristic of the observation signal is also considered. Meanwhile, the spatio-temporal matrices were constructed elaborately and then applied to capture coupling characteristics and spatio-temporal relationships among all estimated mono-components. Eventually, a high-resolution adaptive time-frequency spectrum is obtained according to the IFs and instantaneous amplitudes (IAs) of each estimated mono-component. The effectiveness and practicability of the proposed algorithm were verified via simulated scenarios and velocity dynamic data of the seafloor from the South China Sea, compared with four state-of-the-art benchmarks.

Risk-Aware Travel Path Planning Algorithm Based on Reinforcement Learning during COVID-19

The outbreak of COVID-19 brought great inconvenience to people’s daily travel. In order to provide people with a path planning scheme that takes into account both safety and travel distance, a risk aversion path planning model in urban traffic scenarios was established through reinforcement learning. We have designed a state and action space of agents in a “point-to-point” way. Moreover, we have extracted the road network model and impedance matrix through SUMO simulation, and have designed a Restricted Reinforcement Learning-Artificial Potential Field (RRL-APF) algorithm, which can optimize the Q-table initialization operation before the agent learning and the action selection strategy during learning. The greedy coefficient is dynamically adjusted through the improved greedy strategy. Finally, according to different scenarios, our algorithm is verified by the road network model and epidemic historical data in the surrounding areas of Xinfadi, Beijing, China, and comparisons are made with common Q-Learning, the Sarsa algorithm and the artificial potential field-based reinforcement learning (RLAFP) algorithm. The results indicate that our algorithm improves convergence speed by 35% on average and the travel distance is reduced by 4.3% on average, while avoiding risk areas, compared with the other three algorithms.

Container-based task scheduling in cloud-edge collaborative environment using priority-aware greedy strategy

Container-based task scheduling in cloud-edge collaborative environment using priority-aware greedy strategy

An Intersection-Based Routing Scheme Using Q-Learning in Vehicular Ad Hoc Networks for Traffic Management in the Intelligent Transportation System

Vehicular ad hoc networks (VANETs) create an advanced framework to support the intelligent transportation system and increase road safety by managing traffic flow and avoiding accidents. These networks have specific characteristics, including the high mobility of vehicles, dynamic topology, and frequent link failures. For this reason, providing an efficient and stable routing approach for VANET is a challenging issue. Reinforcement learning (RL) can solve the various challenges and issues of vehicular ad hoc networks, including routing. Most of the existing reinforcement learning-based routing methods are incompatible with the dynamic network environment and cannot prevent congestion in the network. Network congestion can be controlled by managing traffic flow. For this purpose, roadside units (RSUs) must monitor the road status to be informed about traffic conditions. In this paper, an intersection-based routing method using Q-learning (IRQ) is presented for VANETs. IRQ uses both global and local views in the routing process. For this reason, a dissemination mechanism of traffic information is introduced to create these global and local views. According to the global view, a Q-learning-based routing technique is designed for discovering the best routes between intersections. The central server continuously evaluates the created paths between intersections to penalize road segments with high congestion and improve the packet delivery rate. Finally, IRQ uses a greedy strategy based on a local view to find the best next-hop node in each road segment. NS2 software is used for analyzing the performance of the proposed routing approach. Then, IRQ is compared with three methods, including IV2XQ, QGrid, and GPSR. The simulation results demonstrate that IRQ has an acceptable performance in terms of packet delivery rate and delay. However, its communication overhead is higher than IV2XQ.

An enhanced adaptive Bi-clustering algorithm through building a shielding complex sub-matrix.

Bi-clustering refers to the task of finding sub-matrices (indexed by a group of columns and a group of rows) within a matrix of data such that the elements of each sub-matrix (data and features) are related in a particular way, for instance, that they are similar with respect to some metric. In this paper, after analyzing the well-known Cheng and Church bi-clustering algorithm which has been proved to be an effective tool for mining co-expressed genes. However, Cheng and Church bi-clustering algorithm and summarizing its limitations (such as interference of random numbers in the greedy strategy; ignoring overlapping bi-clusters), we propose a novel enhancement of the adaptive bi-clustering algorithm, where a shielding complex sub-matrix is constructed to shield the bi-clusters that have been obtained and to discover the overlapping bi-clusters. In the shielding complex sub-matrix, the imaginary and the real parts are used to shield and extend the new bi-clusters, respectively, and to form a series of optimal bi-clusters. To assure that the obtained bi-clusters have no effect on the bi-clusters already produced, a unit impulse signal is introduced to adaptively detect and shield the constructed bi-clusters. Meanwhile, to effectively shield the null data (zero-size data), another unit impulse signal is set for adaptive detecting and shielding. In addition, we add a shielding factor to adjust the mean squared residue score of the rows (or columns), which contains the shielded data of the sub-matrix, to decide whether to retain them or not. We offer a thorough analysis of the developed scheme. The experimental results are in agreement with the theoretical analysis. The results obtained on a publicly available real microarray dataset show the enhancement of the bi-clusters performance thanks to the proposed method.

Genome sequence assembly algorithms and misassembly identification methods.

The sequence assembly algorithms have rapidly evolved with the vigorous growth of genome sequencing technology over the past two decades. Assembly mainly uses the iterative expansion of overlap relationships between sequences to construct the target genome. The assembly algorithms can be typically classified into several categories, such as the Greedy strategy, Overlap-Layout-Consensus (OLC) strategy, and de Bruijn graph (DBG) strategy. In particular, due to the rapid development of third-generation sequencing (TGS) technology, some prevalent assembly algorithms have been proposed to generate high-quality chromosome-level assemblies. However, due to the genome complexity, the length of short reads, and the high error rate of long reads, contigs produced by assembly may contain misassemblies adversely affecting downstream data analysis. Therefore, several read-based and reference-based methods for misassembly identification have been developed to improve assembly quality. This work primarily reviewed the development of DNA sequencing technologies and summarized sequencing data simulation methods, sequencing error correction methods, various mainstream sequence assembly algorithms, and misassembly identification methods. A large amount of computation makes the sequence assembly problem more challenging, and therefore, it is necessary to develop more efficient and accurate assembly algorithms and alternative algorithms.

Time‐cost efficient memory configuration for serverless workflow applications

SummaryRecently, workflow applications are increasingly migrated to Function‐as‐a‐Service platforms which are easy to manage, highly‐scalable, and pay‐as‐you‐go. Meanwhile, users face challenges in migration of serverless applications because of the lack of efficient algorithm for workflow memory configuration to optimize the performance. To this end, this article proposes a heuristic urgency‐based algorithm UWC and a meta‐heuristic hybrid algorithm BPSO to tackle the time‐cost tradeoff. UWC sorts functions and allocates each function an appropriate memory size by greedy strategy. BPSO hybridizes particle swarm optimization as well as beetle antennae search algorithm to guide particles to search directionally and utilizes nonlinear inertia weight to avoid local premature convergence. Extensive experiments with classical serverless application demonstrate that UWC and BPSO are very competitive in comparison with existing algorithms as they can find the optimal workflow memory configuration.

A novel Hybrid Harris hawk sine cosine optimization algorithm for reactive power optimization problem

ABSTRACT Reactive power optimisation can effectively reduce active power loss and improve voltage quality, which is a great significance for power system planning. When the reactive power optimisation problem is solved by Harris Hawk optimisation (HHO) algorithm, there are slow convergence and falling into local optima. This is caused by the multiple random parameters in HHO’s exploration phase. To solve this problem, the Improved Logistic Chaotic mapping, Sine and Cosine Algorithm (SCA), the dynamic adaptive inertia weights and greedy strategy are introduced; the aim is to speed up convergence, reduce blind spots and improve the search capability. The improved algorithm was tested on the classical 23 benchmark functions; Wilcoxon’s signed-rank test and Friedman test were tested, the results show that the improved algorithm can obtain better performance. The improved algorithm is applied to the reactive power optimisation problem in distribution networks with distributed generators (DG). When the reactive power optimisation problem is solved by the improved algorithms HHO, WOA, CSO, CS and PSO, respectively, the improved algorithm can obtain the lowest active power loss. Compared with no optimisation, active power loss is reduced by 33.19%. Finally, the node voltage quality ensures the safe operation of the system.

Optimization Design of Multi-UAV Communication Network Based on Reinforcement Learning

In recent years, due to the application of high-definition video codec technology, high-precision satellite navigation technology, mobile base station positioning technology, and broadband technology, the performance of UAVs has been greatly improved. In the military field, drones have become an important weapon alongside missiles on the battlefield. In the future, military drones will perform strategic missions such as battlefield reconnaissance and long-range destruction. Outside the military field, DJI’s Zenmuse series drones are used for filming, MG series drones are used for pesticide spraying, and Beijing Zhonghangzhi unmanned helicopters are used for geological surveys, precise inspection of power lines, and maritime law enforcement. With the continuous improvement of technical specifications, UAV communication technology requires further research and development. This paper has conducted research experiments on the optimization of multi-UAV communication network based on reinforcement learning. The experimental data show that it is marked as the AoI value corresponding to the completion of a certain self-task. It can be seen that the final AoI of the communication trajectory of reinforcement learning is 115, and the AoI greedy strategy finally obtains AoI of 140 seconds, achieving about 18% of the total AoI reduce, which effectively improve the performance of the system. From the above data, the research of reinforcement learning method has great benefits for the development of UAV communication.

Hierarchical framework for mobile robots to effectively and autonomously explore unknown environments

Achieving efficient and safe autonomous exploration in unknown environments is an urgent challenge to be overcome in the field of robotics. Existing exploration methods based on random and greedy strategies cannot ensure that the robot moves to the unknown area as much as possible, and the exploration efficiency is not high. In addition, because the robot is located in an unknown environment, the robot cannot obtain enough information to process the surrounding environment and cannot guarantee absolute safety. To improve the efficiency and safety of exploring unknown environments, we propose an autonomous exploration motion planning framework that is divided into the exploration and obstacle avoidance levels. The two levels are independent and interconnected. The exploration level finds the optimal frontier target point in the global scope based on the forward filtering angle and cost function, attracting the robot to move to the unknown area as much as possible, and improving the exploration efficiency; the obstacle avoidance level establishes a scenario-speed conversion mechanism, and the target point and obstacle information are weighed to realise dynamic motion planning and completes obstacle avoidance control, and ensures the safety of exploration. Experiments in different simulation scenarios and real environments verify the superiority of the method. Results show that our method is superior to the existing methods.

Effective submodularity of influence maximization on temporal networks.

We study influence maximization on temporal networks. This is a special setting where the influence function is not submodular, and there is no optimality guarantee for solutions achieved via greedy optimization. We perform an exhaustive analysis on both real and synthetic networks. We show that the influence function of randomly sampled sets of seeds often violates the necessary conditions for submodularity. However, when sets of seeds are selected according to the greedy optimization strategy, the influence function behaves effectively as a submodular function. Specifically, violations of the necessary conditions for submodularity are never observed in real networks, and only rarely in synthetic ones. The direct comparison with exact solutions obtained via brute-force search indicates that the greedy strategy provides approximate solutions that are well within the optimality gap guaranteed for strictly submodular functions. Greedy optimization appears, therefore, to be an effective strategy for the maximization of influence on temporal networks.

An improved Pareto local search for solving bi-objective scheduling problems in hot rolling mills

Hot rolling scheduling is a complex decision-making optimization problem in the production management of the iron and steel industry. Comprehensively considering temporal and technical constraints of hot rolling production, we formulate a prize-collecting capacitated vehicle routing problem with time windows (PC-CVRPTW) involving special constraints and multiple objectives. Furthermore, this paper develops a Pareto local search (PLS)-based solving algorithm via leveraging problem-specific properties and implements three improvement mechanisms: (1) solution initialization with a greedy strategy; (2) promising solution selection with optimistic hypervolume contribution; (3) hybrid neighborhood exploration integrated variable neighborhood search with constraint programming. To validate the proposed method, computational experiments are conducted on a set of randomly synthetic and realistic industrial instances. The results compared with other variants of PLS and state-of-art algorithms demonstrate that the improved PLS algorithm is effective and can provide promising solutions for bi-objective hot rolling scheduling problems.

The UAV Path Coverage Algorithm Based on the Greedy Strategy and Ant Colony Optimization

Today, the development of unmanned aerial vehicles (UAVs) has attracted significant attention in both civil and military fields due to their flight flexibility in complex and dangerous environments. However, due to energy constraints, UAVs can only finish a few tasks in a limited time. The problem of finding the best flight path while balancing the task completion time and the coverage rate needs to be resolved urgently. Therefore, this paper proposes a UAV path coverage algorithm base on the greedy strategy and ant colony optimization. Firstly, this paper introduces a secondary advantage judgment and optimizes it using an ant colony optimization algorithm to reach the goal of minimum time and maximum coverage. Simulations are performed for different numbers of mission points and UAVs, respectively. The results illustrate that the proposed algorithm achieves a 2.8% reduction in task completion time while achieving a 4.4% improvement in coverage rate compared to several previous works.

Solve routing problems with a residual edge-graph attention neural network

For NP-hard combinatorial optimization problems, it is usually challenging to find high-quality solutions in polynomial time. Designing either an exact algorithm or an approximate algorithm for these problems often requires significantly specialized knowledge. Recently, deep learning methods have provided new directions to solve such problems. In this paper, an end-to-end deep reinforcement learning framework is proposed to solve this type of combinatorial optimization problems. This framework can be applied to different problems with only slight changes of input, masks, and decoder context vectors. The proposed framework aims to improve the models in literacy in terms of the neural network model and the training algorithm. The solution quality of TSP and the CVRP up to 100 nodes are significantly improved via our framework. Compared with the best results of the state-of-the-art methods, the average optimality gap is reduced from 4.53% to 3.67% for TSP with 100 nodes and from 7.34% to 6.68% for CVRP with 100 nodes when using the greedy decoding strategy. Besides, the proposed framework can be used to solve a multi-depot CVRP case without any structural modification. Furthermore, our framework uses about 1/3∼3/4 training samples compared with other existing learning methods while achieving better results. The results performed on randomly generated instances, and the benchmark instances from TSPLIB and CVRPLIB confirm that our framework has a linear running time on the problem size (number of nodes) during training and testing phases and has a good generalization performance from random instance training to real-world instance testing.

A parallel based evolutionary algorithm with primary-auxiliary knowledge

The development of hybrid algorithms or the application of multiple strategies is one of the focal points for research on improving evolutionary algorithms. However, since most of the evolutionary equations of different algorithms can be transformed into each other, it is difficult to change the established properties of the given algorithm through a hybrid algorithm based on a mixture of evolutionary equations. In addition, multi-strategy methods tend to adopt the best strategy for the current local domain through greedy strategies in the solution process, which does not ensure validity in the global domain. Recently, Federated Learning has achieved remarkable results in machine learning, where the idea of model independence, parallelism and data sharing can essentially compensate for the weaknesses of hybrid and multi-strategy algorithms. Inspired by the idea of Federated Learning, this paper proposes an evolutionary algorithm named as parallel based Evolutionary Algorithm with primary-auxiliary knowledge. Specifically, a Spark-based primary-auxiliary knowledge model is developed, with different evolutionary algorithms used on each parallel sub-model. Then, an effective topological knowledge (individual) migration method is devised, which enables the primary knowledge model to learn the best knowledge from different auxiliary knowledge models through a topological structure. In this way, the best knowledge on the auxiliary knowledge models can be transferred to the primary knowledge model. Through a test conducted on the CEC2013 test set, it can be found out that the proposed algorithm clearly outperforms the 10 algorithms compared, which demonstrates the excellent performance of our proposed parallel based evolutionary algorithm with primary-auxiliary knowledge.