Greedy Strategy Research Articles

A Greedy Cooperative Co-Evolutionary Algorithm With Problem-Specific Knowledge for Multiobjective Flowshop Group Scheduling Problems

The flowshop sequence-dependent group scheduling problem (FSDGSP) with the production efficiency measures has been extensively studied due to its wide industrial applications. However, energy efficiency indicators are often ignored in the literature. This paper considers the FSDGSP to minimize makespan, total flow time and total energy consumption, simultaneously. After the problem-specific knowledge is extracted, a mixed-integer linear programming model and a critical path-based accelerated evaluation method are proposed. Since the FSDGSP includes multiple coupled sub-problems, a greedy cooperative co-evolutionary algorithm (GCCEA) is designed to explore the solution space in depth. Meanwhile, a random mutation operator and a greedy energy-saving strategy are employed to adjust the processing speeds of machines to obtain a potential non-dominated solution. A large number of experimental results show that the proposed algorithm significantly outperforms the existing classic multi-objective optimization algorithms, which is due to the usage of problem-related knowledge.

Positive Evaluation Maximization in Social Networks: Model and Algorithm

Influence maximization (IM) is a classical and heated issue in online social networks. Although some works have studied multifeature IM, they do not consider this problem from the perspective of users&#x2019; preferences. In this article, we construct a novel multifeature spreading model that considers users&#x2019; different preferences, namely, the MFP-independent cascade (IC) model, which uses the IC model as the basic propagation model. Some features are positive or negative depending on users&#x2019; preferences. According to this spreading model, we consider a novel problem that focuses on the influence gap between positive features and negative features, namely, positive evaluation maximization (PEM). This problem is NP-hard and nonsubmodular. Fortunately, PEM can be expressed as DS decomposition because the positive influence and the negative influence are both monotone submodular. Based on DS decomposition, we design some special greedy strategies, namely, the parametric conditioned greedy (PCG). To reduce the computational cost of our method, we design fast PCG (FPCG) algorithm using the sampling technique. In addition, the approximation ratio of the PCG and FPCG approaches is only the gap between their <inline-formula> <tex-math notation="LaTeX">$O(\epsilon)$</tex-math> </inline-formula> values. Finally, we evaluate our algorithms by performing massive experiments on real datasets.

Physical Black-Box Adversarial Attacks Through Transformations

Deep learning has shown impressive performance in numerous applications. However, recent studies have found that deep learning models are vulnerable to adversarial attacks, where the attacker adds imperceptible perturbations into benign samples to induce misclassifications. Adversarial attacks in the digital domain focus on constructing imperceptible perturbations. However, they are always less effective in the physical world because the perturbations may be destroyed when captured by the camera. Most physical adversarial attacks require adding invisible adversarial features (e.g., a sticker or a laser) to the target object, which may be noticed by human eyes. In this work, we propose to employ image transformation to generate more natural adversarial samples in the physical world. Concretely, we propose two attack algorithms to satisfy different attack goals: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Efficient-AATR</i> employs a greedy strategy to generate adversarial samples with fewer queries; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Effective-AATR</i> employs an adaptive particle swarm optimization algorithm to search for the most effective adversarial samples within the given the number of queries. Extensive experiments demonstrate the superiority of our attacks compared with state-of-the-art adversarial attacks under mainstream defenses.

Potential-Field-RRT: A Path-Planning Algorithm for UAVs Based on Potential-Field-Oriented Greedy Strategy to Extend Random Tree

This paper proposes a random tree algorithm based on a potential field oriented greedy strategy for the path planning of unmanned aerial vehicles (UAVs). Potential-field-RRT (PF-RRT) discards the defect of traditional artificial potential field (APF) algorithms that are prone to fall into local errors, and introduces potential fields as an aid to the expansion process of random trees. It reasonably triggers a greedy strategy based on the principle of field strength descending gradient optimization, accelerating the process of random tree expansion to a better region and reducing path search time. Compared with other optimization algorithms that improve the sampling method to reduce the search time of the random tree, PF-RRT takes full advantage of the potential field without limiting the arbitrariness of random tree expansion. Secondly, the path construction process is based on the principle of triangle inequality for the root node of the new node to improve the quality of the path in one iteration. Simulation experiments of the algorithm comparison show that the algorithm has the advantages of fast acquisition of high-quality initial path solutions and fast optimal convergence in the path search process. Compared with the original algorithm, obtaining the initial solution using PF-RRT can reduce the time loss by 20% to 70% and improve the path quality by about 25%. In addition, the feasibility of PF-RRT for UAV path planning is demonstrated by actual flight test experiments at the end of the experiment.

A Quantum States Preparation Method Based on Difference-Driven Reinforcement Learning

Due to the large state space of the two-qubit system, and the adoption of ladder reward function in the existing quantum state preparation methods, the convergence speed is slow and it is difficult to prepare the desired target quantum state with high fidelity under limited conditions. To solve the above problems, a difference-driven reinforcement learning (RL) algorithm for quantum state preparation of two-qubit system is proposed by improving the reward function and action selection strategy. Firstly, a model is constructed for the problem of preparing quantum states of a two-qubit system, with restrictions on the type of quantum gates and the time for quantum state evolution. In the preparation process, a weighted differential dynamic reward function is designed to assist the algorithm quickly obtain the maximum expected cumulative reward. Then, an adaptive [Formula: see text]-greedy action selection strategy is adopted to achieve a balance between exploration and utilization to a certain extent, thereby improving the fidelity of the final quantum state. The simulation results show that the proposed algorithm can prepare quantum state with high fidelity under limited conditions. Compared with other algorithms, it has different degrees of improvement in convergence speed and fidelity of the final quantum state.

An Analysis of Optimization for Car PBS Scheduling Based on Greedy Strategy State Transition Algorithm

The differences in constraints between an automotive painting workshop and a final assembly workshop can lead to production scheduling disruptions, and using the Painted Body Store (PBS) in automotive manufacturing can optimize production scheduling to meet the production requirements of the final assembly workshop. This is achieved by establishing a multi-objective mixed-integer optimization scheduling model for PBS and solving the model using a state transition algorithm based on the greedy strategy; finally, a comparison was made with the classical genetic algorithm. The results show that the optimal objective function is achieved when the body sequence movement iterations for data sets N and R are 34 and 54, respectively. At this point, the highest objective function scores are 23.46 and 49.79, and vehicles entering the PBS scheduling system from the painting-to-assembly line require 31 and 38 sequence adjustments, respectively. Compared with the classical genetic algorithm, the simulation results show that the state transition algorithm based on the greedy strategy is significantly better; it can also perform well in discrete sequence movement operations, demonstrating strong global search capabilities and fast convergence. It provides a new approach for optimizing vehicle scheduling in the automotive painting-to-assembly line.

Optimizing Comprehensive Cost of Charger Deployment in Multi-hop Wireless Charging

The multi-hop wireless charging technology has attracted a lot of attention, as it largely extends the charging range of chargers. Different from the existing work with single cost optimization, the objective of this article is to optimize the comprehensive cost, which is the combination of energy cost and deployment cost. We decompose the target problem into two sub-problems. The first sub-problem aims to minimize the deployment cost with energy capacity constraints. The proposed algorithm follows the greedy strategy, where the subset of sensor nodes for any charger is determined by finding the capacitated minimum spanning tree. The second sub-problem, which aims to maximize the reduction of comprehensive cost by adding chargers to the solution of the first sub-problem, is proved to be an unconstrained submodular set function maximization problem and can be solved by a 1/2-approximation randomized linear time algorithm for its equivalent problem. Through extensive simulations, we demonstrate that the proposed solution can reduce the comprehensive cost by 57.55% comparing with the benchmark algorithms.

Energy- and Cost-Efficient Transmission Strategy for UAV Trajectory Tracking Control: A Deep Reinforcement Learning Approach

In this paper, we consider a networked control system (NCS) with network-induced delay, in which the control center needs to control the remote unmanned aerial vehicle (UAV) to complete the trajectory tracking task. The sensor of the controlled UAV adopts the event-triggered mechanism, while the control center uses the adaptive dynamic programming (ADP) based tracking control method to generate control actions. The application of ADP method brings new transmission options, i.e., the control center can choose to transmit control action or neural network model. Considering the fundamental tradeoff between these two transmission options with different transmission energy consumption and tracking cost, we formulate the joint optimization problem as a markov decision process (MDP). Due to the continuous value of state in MDP, we propose the DQN-based strategy, which uses the reinforcement learning (RL) algorithm, specifically deep Q-network (DQN). Besides, we further propose a greedy strategy by calculating the instantaneous expected cost. Simulation results show that DQN-based strategy has better performance but depends on the training process, while greedy strategy is suboptimal but easy to compute. Besides, compared with the benchmark strategies, the proposed strategies can achieve a better compromise in the long-term average energy consumption and tracking cost by adjusting the value of the weighted factor. Furthermore, by comparing the difference of transmission decisions in the proposed strategies, we show that the proper transmission sequence in DQN-based strategy can reduce the tracking cost and transmission energy at the same time.

Identifying influential spreaders in social networks: A two-stage quantum-behaved particle swarm optimization with Lévy flight

The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework, first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.

Joint decision-making of virtual module formation and scheduling considering queuing time

Formation and scheduling are the most important decisions in the virtual modular manufacturing system; however, the global performance optimization of the system may be sacrificed via the superposition of two independent decision-making results. The joint decision of formation and scheduling is very important for system design. Complex and discrete manufacturing enterprises such as shipbuilding and aerospace often comprise multiple tasks, processes, and parallel machines, resulting in complex routes. The queuing time of parts in front of machines may account for 90% of the production cycle time. This study established a weighted allocation model of a formation-scheduling joint decision problem considering queuing time in system. To solve this nondeterministic polynomial (NP) problem, an adaptive differential evolution-simulated annealing (ADE-SA) algorithm is proposed. Compared with the standard differential evolution (DE) algorithm, the adaptive mutation factor overcomes the disadvantage that the scale of DE’s differential vector is difficult to control. The selection strategy of the SA algorithm compensates for the deficiency that DE’s greedy strategy may fall into a local optimal solution. The comparison results of four algorithms of a series of random examples demonstrate that the overall performance of ADE-SA is superior to the genetic algorithm, and average iteration, maximum completion time, and move time are 24%, 11%, and 7% lower than the average of other three algorithms, respectively. The method can generate the joint decision-making scheme with better overall performance, and effectively identify production bottlenecks through quantitative analysis of queuing time.

A Hyperheuristic With Q-Learning for the Multiobjective Energy-Efficient Distributed Blocking Flow Shop Scheduling Problem.

Carbon peaking and carbon neutrality, which are the significant national strategy for sustainable development, have attracted considerable attention from production enterprises. In this study, the energy consumption is considered in the distributed blocking flow shop scheduling problem (DBFSP). A hyperheuristic with Q -learning (HHQL) is presented to address the energy-efficient DBFSP (EEDBFSP). Q -learning is employed to select an appropriate low-level heuristic (LLH) from a predesigned LLH set according to historical information fed back by LLH. An initialization method, which considers both total tardiness (TTD) and total energy consumption (TEC), is proposed to construct the initial population. The ε -greedy strategy is introduced to utilize the learned knowledge while retaining a certain degree of exploration in the process of selecting LLH. The acceleration operation of the job on the critical path is designed to optimize TTD. The deceleration operation of the job on the noncritical path is designed to optimize TEC. The statistical and computational experimentation in an extensive benchmark testified that the HHQL outperforms the other comparison algorithm regarding efficiency and significance in solving EEDBFSP.

Data-Driven Wind Farm Control via Multiplayer Deep Reinforcement Learning

This brief proposes a novel data-driven control scheme to maximize the total power output of wind farms subject to strong aerodynamic interactions among wind turbines. The proposed method is model-free and has strong robustness, adaptability, and applicability. Particularly, distinct from the state-of-the-art data-driven wind farm control methods that commonly use the steady-state or time-averaged data (such as turbines’ power outputs under steady wind conditions or from steady-state models) to carry out learning, the proposed method directly mines in-depth the time-series data measured at turbine rotors under time-varying wind conditions to achieve farm-level power maximization. The control scheme is built on a novel multiplayer deep reinforcement learning method (MPDRL), in which a special critic–actor–distractor structure, along with deep neural networks (DNNs), is designed to handle the stochastic feature of wind speeds and learn optimal control policies subject to a user-defined performance metric. The effectiveness, robustness, and scalability of the proposed MPDRL-based wind farm control method are tested by prototypical case studies with a dynamic wind farm simulator (WFSim). Compared with the commonly used greedy strategy, the proposed method leads to clear increases in farm-level power generation in case studies.

On the performance of the helix wind farm control approach in the conventionally neutral atmospheric boundary layer

The performance of wind farms can substantially increase when their individual turbines deviate from their own greedy control strategy and instead also take into account downstream turbines operating in the wake. The helix approach is a recently introduced dynamic wind farm control strategy that tackles this issue by leveraging individual pitch control to accelerate wake recovery. Its effective implementation requires detailed knowledge about the scaling between control input and the resulting power gain and turbine loading across the farm. In the present work this scaling is explored by means of large-eddy simulation of a two-turbine farm in the conventionally neutral atmospheric boundary layer. A parameter sweep for the amplitude of the helix is performed showing monotonous increase of the farm’s power output with increasing pitch amplitude within the considered range of zero to six degrees. The scaling of the power gain suggests that a threshold amplitude should be exceeded for effective speed-up of the wake recovery, whereas the damage equivalent loads computed for the turbines indicate an upper limit for the amplitude despite increasing power gains.

Multi-Strategy Improved Flamingo Search Algorithm for Global Optimization

To overcome the limitations of the Flamingo Search Algorithm (FSA), such as a tendency to converge on local optima and improve solution accuracy, we present an improved algorithm known as the Multi-Strategy Improved Flamingo Search Algorithm (IFSA). The IFSA utilizes a cube chaotic mapping strategy to generate initial populations, which enhances the quality of the initial solution set. Moreover, the information feedback model strategy is improved to dynamically adjust the model based on the current fitness value, which enhances the information exchange between populations and the search capability of the algorithm itself. In addition, we introduce the Random Opposition Learning and Elite Position Greedy Selection strategies to constantly retain superior individuals while also reducing the probability of the algorithm falling into a local optimum, thereby further enhancing the convergence of the algorithm. We evaluate the performance of the IFSA using 23 benchmark functions and verify its optimization using the Wilcoxon rank-sum test. The compared experiment results indicate that the proposed IFSA can obtain higher convergence accuracy and better exploration abilities. It also provides a new optimization algorithm for solving complex optimization problems.

An Improved NSGA-II for Energy-oriented Hot Rolling Production Scheduling Problems

With the sharply increasing energy demand and the shortage of energy, the scheduling goal of hot rolling production (HRP) is to select and sequence slabs to reduce cost and energy consumption. A multi-objective HRP optimal scheduling model is formulated and transferred as a multi-objective prize collecting vehicle routing problem (MO-PCVRP) with special constraints that simultaneously minimizes the jump cost of width, thickness, and hardness between adjacent slabs, the total length of unselected slabs, and the energy cost. Furthermore, this paper introduces a greedy strategy to intensify the local search of the standard NSGA-II algorithm, and names it local-intensification NSGA-II (LI-NSGA-II). The comparison experiments show that the LI-NSGA-II algorithm obtains better comprehensive performance than other compared algorithms in solving multi-objective scheduling problems of HRP.

Improved (In-)Approximability Bounds for d-Scattered Set

In the $d$-${\rm S{\small CATTERED}\;S{\small ET}}$ problem we are asked to select at least $k$ vertices of a given graph, so that the distance between any pair is at least $d$. We study the problem's (in-)approximability and offer improvements and extensions of known results for ${\rm I{\small NDEPENDENT}\;S{\small ET}}$, of which it is a generalization. Specifically, we show: A lower bound of $\Delta^{\lfloor d/2\rfloor-\epsilon}$ on the approximation ratio of any polynomial-time algorithm for graphs of maximum degree $\Delta$ and an improved upper bound of $O(\Delta^{\lfloor d/2\rfloor})$ on the approximation ratio of any greedy scheme for this problem. A polynomial-time $2\sqrt{n}$-approximation for bipartite graphs and even values of $d$, that matches the known lower bound by considering the only remaining case. A lower bound on the complexity of any $\rho$-approximation algorithm of (roughly) $2^{\frac{n^{1-\epsilon}}{\rho d}}$ for even $d$ and $2^{\frac{n^{1-\epsilon}}{\rho(d+\rho)}}$ for odd $d$ (under the randomized ETH), complemented by $\rho$-approximation algorithms of running-times that (almost) match these bounds.

A Multistep Iterative Ranking Learning Method for Optimal Project Portfolio Planning of Smart Grid

Optimal project portfolio planning is a typical nonconvex, multiobjective, highly constrained, multitemporal coupling, and combinatorial optimization problem. This paper proposes a novel multistep iterative ranking learning method (MIRL) to solve this complex combinatorial optimization problem from massive infrastructure projects of smart grid. The optimal project portfolio planning problem of power grid is formulated as the optimization process of massive project priority sorting with an improved knapsack model. The proposed method dynamically optimizes the best infrastructure project combination for each round to maximize the economic, social, and security benefits without exceeding the annual investment limit. A pairwise-based ranking learning algorithm is used to mine the priority sorting law from massive historical combination data of power grid to initialize candidate project portfolio. In order to approach the optimal portfolio planning solution with the constraint satisfactions of project construction duration and electric load supplies, a heuristic greedy strategy is designed to search the solution dynamically for selecting the project having highest construction benefits iteratively. The effectiveness of the proposed method is proved by experiments with real-world project data of Hunan power grid in China, and experimental results show that the proposed MIRL can outperform other methods on investment efficiency, calculation time, and rationality of project construction period schedule.

An empirical analysis for software robustness vulnerability in terms of modularity quality

AbstractModern software architectures such as microservices provide a high degree of scalability, changeability, and maintainability in application development. Furthermore, enabling controlled failure of microservices can provide abstract‐level solutions to design more resilient applications. In this paper, we introduce modularity vulnerability to analyze the vulnerability of a modular software design model under the failure of m top‐rank modules by the proposed structural metrics. The study analyzes the modularity quality coefficient (MQC) under the failure of the critical modules identified using the proposed parameter‐based greedy strategy. We conduct a comprehensive analysis of the software design generated by well‐known models and online datasets and provide a perspective for reasoning about the correlation between modularity metrics. The results show that the failure of the modules with the highest cluster factor (CF) value leads to a maximum decrease in the software modularity quality. Finally, we show a linear correlation between CF and the variations of the MQC, implying stability in the software modularity analysis (SMA) problem.

Anonymous Methods Based on Multi-Attribute Clustering and Generalization Constraints

The dissemination and sharing of data sheets in IoT applications presents privacy and security challenges that can be addressed using the k-anonymization algorithm. However, this method needs improvement, for example, in areas related to its overgeneralization and its insufficient attribute diversity constraints during the anonymization process. To address these issues, this study proposes a multi-attribute clustering and generalization constraints (k,l)-anonymization method that can be applied to multidimensional data tables. The algorithm first used a greedy strategy to rank the attributes by width first, derived the division into dimensions to construct a multidimensional generalization hierarchy, and then selected the attributes with the most significant width values as the priority generalization attributes. Next, the k-nearest neighbor (KNN) clustering method was introduced to determine the initial clustering center by the width-first results, divide the quasi-identifier attributes into KNN clusters according to a distance metric, and generalize the quasi-identifier attributes in the equivalence class using a hierarchical generalization structure. Then, the proposed method re-evaluated the attributes to be generalized before each generalization operation. Finally, the algorithm employed an improved frequency–diversity constraint to generalize sensitive attributes in order to ensure that there were at least l records that were mutually dissimilar and closest in the equivalence class. While limiting the frequency threshold for the occurrence of sensitive attributes, the sensitive attribute values remained similar within the group, thus achieving protection of anonymity for all the attributes.

A task scheduling algorithm with deadline constraints for distributed clouds in smart cities.

Computing technologies and 5G are helpful for the development of smart cities. Cloud computing has become an essential smart city technology. With artificial intelligence technologies, it can be used to integrate data from various devices, such as sensors and cameras, over the network in a smart city for management of the infrastructure and processing of Internet of Things (IoT) data. Cloud computing platforms provide services to users. Task scheduling in the cloud environment is an important technology to shorten computing time and reduce user cost, and thus has many important applications. Recently, a hierarchical distributed cloud service network model for the smart city has been proposed where distributed (micro) clouds, and core clouds are considered to achieve a better network architecture. Task scheduling in the model has attracted many researchers. In this article, we study a task scheduling problem with deadline constraints in the distributed cloud model and aim to reduce the communication network's data load and provide low-latency services from the cloud server in the local area, hence promoting the efficiency of cloud computing services for local users. To solve the task scheduling problem efficiently, we present an efficient local search algorithm to solve the problem. In the algorithm, a greedy search strategy is proposed to improve the current solutions iteratively. Moreover, randomized methods are used in selecting tasks and virtual machines for reassigning tasks. We carried out extensive computational experiments to evaluate the performance of our algorithm and compared experimental results with Swarm-based approaches, such as GA and PSO. The comparative results show that the proposed local search algorithm performs better than the comparative algorithms on the task scheduling problem.