Deep neural networks have become foundational in various applications but remain vulnerable to adversarial patch attacks. Crafting effective adversarial patches is inherently challenging due to the combinatorial complexity involved in jointly optimizing critical factors such as patch shape, location, number, and content. Existing approaches often simplify this optimization by addressing each factor independently, which limits their effectiveness. To tackle this significant challenge, we introduce a novel and flexible adversarial attack framework termed IMPACT (Irregular Multi-Patch Adversarial Composition based on Two-phase optimization). IMPACT uniquely enables comprehensive optimization of all essential patch factors using gradient-free methods. Specifically, we propose a novel dimensionality reduction encoding scheme that substantially lowers computational complexity while preserving expressive power. Leveraging this encoding, we further develop a two-phase optimization framework: phase 1 employs differential evolution for joint optimization of patch mask and content, while phase 2 refines patch content using an evolutionary strategy for enhanced precision. Additionally, we introduce a new aggregation algorithm explicitly designed to produce contiguous, irregular patches by merging localized regions, ensuring physical applicability. Extensive experiments demonstrate that our method significantly outperforms several state-of-the-art approaches, highlighting the critical benefit of jointly optimizing all patch factors in adversarial patch attacks.

With the rapid advancement of cloud computing, cloud services have been widely adopted for managing large-scale and complex IoT workflow applications due to their robust computational capabilities. However, efficiently scheduling and deploying these workflows while ensuring quality-of-service (QoS) for diverse users remains a significant challenge for cloud service providers. In this study, we propose a novel multi-stage workflow scheduling algorithm (RE-ACO) for energy-efficient management of reliability-constrained IoT applications in cloud environments. The algorithm operates in three key stages: Task ordering by ACO, reliability constraint distribution with feedback information and energy-aware task assignment. The RE-ACO leverages ACO and an energy-aware task assignment strategy to optimize energy usage without compromising workflow reliability. First, the ACO algorithm determines the optimal task execution sequence. Next, a feedback-based reliability distribution method dynamically assigns sub-reliability constraints to individual tasks. Finally, each task is allocated to a virtual machine (VM) that minimizes energy consumption while meeting its sub-reliability requirement. Simulation results demonstrate that RE-ACO outperforms existing approaches, achieving the lowest energy consumption for reliability-constrained workflow scheduling compared to three benchmark algorithms.

In the rapidly evolving landscape of cloud computing, scheduling complex scientific workflows poses significant challenges, particularly with constraints like budget considerations. While recent years have seen considerable research focus on budget-constrained cloud workflow scheduling, existing studies primarily concentrate on constructing solutions without delving into potential enhancements. To address this gap, our paper introduces PACP-HEFT, a modified HEFT algorithm integrating priority adjustment and task optimization to minimize makespan. Leveraging the Heterogeneous Earliest-Finish-Time (HEFT) algorithm as its foundation, PACP-HEFT incorporates two priority adjusters informed by task characteristics gleaned from task dependency topology and real-time scheduling data. Additionally, a critical task optimizer conducts thorough analyses and optimizations to reduce overall makespan. Extensive experimentation with real-world workflows underscores PACP-HEFT’s superior performance compared to contemporary algorithms. Note to Practitioners—This paper presents a deterministic approach to budget-constrained scheduling problem, with the objective of minimizing the makespan. The proposed PACP-HEFT algorithm exhibits robust solution enhancement capabilities through the utilization of two priority adjusters and a critical task optimizer. The two priority adjusters fine-tune task priorities based on characteristics extracted from task dependency topology and real-time scheduling data, while the critical task optimizer performs an in-depth analysis of scheduling solutions and optimizes critical tasks to reduce overall makespan. Real-world workflow experiments clearly highlight the superior performance of PACP-HEFT compared to state-of-the-art algorithms. Validation experiments also underscore the effectiveness of each priority adjuster and the critical task optimizer. Moreover, PACP-HEFT’s ability to rapidly reach solutions further enhances its practicality, ensuring timely scheduling outcomes. Furthermore, we assess the adaptability and transferability of these components, emphasizing their potential applicability in enhancing the performance of other scheduling algorithms. For practical applications, this algorithm can be directly applied to rapidly minimize the makespan of budget-constrained scheduling problems in the cloud. Moreover, it can be integrated into other schedulers as a solution enhancement component, offering versatility and improved scheduling efficiency.

Cloud computing is a potent platform for delivering high-quality computational services to intricate IoT applications. However, effective scheduling approaches are essential to meet application demands while maximizing cloud computing’s potential. In this study, we propose an innovative workflow scheduling method for addressing the cost-effective, deadline-constrained scheduling challenge of IoT applications in cloud computing systems. Our solution, the F-ACO algorithm, leverages a hybrid intelligence approach that combines Ant Colony Optimization (ACO) with a cost-driven heuristic strategy. The primary goal is to minimize workflow scheduling costs while ensuring that workflow deadlines are met. F-ACO introduces a deadline distribution method to derive task sub-deadlines, enabling dynamic adjustments for unscheduled tasks to meet workflow deadlines. Furthermore, we introduce an adaptive ACO-based task ordering mechanism with self-adaptive heuristic information to optimize task scheduling sequences, reducing search space redundancy and enhancing convergence speed. The approach includes a cost-driven task scheduling method designed to allocate each task to a virtual machine with minimal execution cost and idle time, further optimizing the overall workflow scheduling cost. To validate our F-ACO algorithm, we conducted numerous simulations using real-world workflows and compared its performance against state-of-the-art algorithms. Our experimental results affirm F-ACO’s competitive edge in effectively scheduling IoT applications in cloud computing environments.

Abstract In this paper, we introduce a new class of $T_0$ spaces called wb-sober spaces, which is strictly larger than the class of open well-filtered spaces. Unlike open well-filtered spaces, wb-sober spaces are defined more intuitively by requiring certain special subsets, termed wb-irreducible closed sets, to have singleton closures. We establish several key results about these spaces, including (1) every open well-filtered space is wb-sober, but not vice versa; (2) every strongly core-coherent wb-sober space is open well-filtered; (3) a space is core-compact iff its irreducible closed sets are wb-irreducible, providing a characterization of core-compactness; (4) every core-compact wb-sober space is sober, thereby generalizing the Jia-Jung problem. In addition, we investigate the core-coherence of the Xi-Zhao model. We prove that a $T_1$ space contains finite number of isolated points iff its Xi-Zhao model is core-coherent iff its Xi-Zhao model is strongly core-coherent. Based on this result, we then propose a general approach to constructing a non-routine open well-filtered but not well-filtered dcpo.

Bus scheduling is vital for public transportation to ensure high transit service quality. In actual bus operation, buses’ travel time may change due to some uncertain factors, which makes the planned scheduling scheme fail to meet users’ actual requirements. This work proposes a Controller-based Bus Scheduling Approach (CBSA). In this approach, each departure time in a timetable is regarded as a decision point, and a controller is devised to select a bus in real-time to depart from the departure time. The controller makes a decision at each departure time to cover all departure times in a given timetable. The controller consists of a Duty Type Converter (DTC) and a Bus Selector (BS). DTC determines bus duty types to improve bus utilization, while BS selects a bus to cover the departure time. Since the controller makes decisions in a real-time manner, it can effectively handle uncertain events and factors (such as uncertain travel time). Some key parameters of the controller are optimized by a particle swarm optimizer (PSO) to improve its performance. CBSA is applied to real-world problem instances. Experimental results show that it outperforms the compared algorithms and a manual scheduling scheme. It can schedule buses in real-time to generate a high-quality scheduling solution under uncertain environments.

A transit route network design problem is a vitally important problem in the area of public transit systems. Most of studies on this problem aim to design a new transit network, which is often an infeasible option in practice since it is highly challenging to replace an existing network with a completely new one. In this paper, we propose a Multi-objective Ant Colony System-based Approach (MACSA) to adjust routes of bus lines for an existing transit network, such that transit service quality is improved while making the smallest deviation of the adjusted network from the existing one. First, all the bus lines in a network are sorted according to their performance. Then, a multi-objective ant colony system is adapted to adjust the sorted bus lines one by one. Besides traditional optimization objectives to maximize direct passenger flow and minimize line repetition coefficient, a new optimization objective (metric), termed adjustment degree, is proposed to measure the difference between adjusted bus lines and existing ones. Needleman-Wunsch algorithm is introduced to calculate the adjustment degree. A multi-pheromone updating mechanism is suggested to guide ants to search for better bus lines for each objective. MACSA is applied to benchmark problem instances and a real-world problem and compared with six approaches. Experiments show that MACSA can achieve an adjusted network with higher direct passenger flow, lower repetition coefficient and smaller adjustment degree. The adjustment degree achieved by MACSA is 1.61-53.82% smaller than that of other comparative approaches.

Facility layout problems (FLPs) in hospitals are typically to arrange facilities or rooms along both sides of a corridor to minimize some objectives. In a hospital, very often there are center-islands to decrease the flow cost among facilities or rooms. However, these islands have not been considered before. In this article, we propose an FLP with center-islands that involves two parallel rows and center-islands. A mixed-integer program formulation is established for modeling it. A methodology for combining a multiobjective evolutionary algorithm based on decomposition (MOEA/D) and linear program is proposed to solve this problem. MOEA/D optimizes the sequence of facilities on two rows and center-islands while the linear program is embedded into MOEA/D to optimize the exact locations of center-islands. A tabu search with a local search is also integrated into MOEA/D to enhance its search capability. Experiments show that our proposed methodology can effectively solve the problem.

Facility layout is vital to save operational cost and enhance production efficiency. Multirow layout is a common pattern in practical manufacturing environment. Although parallel machines are frequently implemented in practice to enhance productivity, there lacks any in-depth study on multirow layout problem with parallel machines. In this paper, its mathematical programming formulation is established to minimize material flow cost. A three-stage approach is proposed to solve it. First, a Monte Carlo heuristic is devised to optimize the sequence of machines on multiple rows. Second, a linear program is used to determine the optimal exact location of each machine. Finally, an exchange heuristic is adopted to reassign material flows among parallel machines in different machine groups. An iterative optimization strategy is suggested to execute the three stages repeatedly to improve the solution quality. This approach is applied to a number of problem instances and compared against others. The experimental results show that it is able to effectively solve this new problem and significantly decrease material flow cost. Note to Practitioners-Multirow layout is common in practical manufacturing systems in which parallel machines are often used to improve productivity, shorten production time, and guarantee some flexibility. This paper studies a multirow parallel machine layout problem that involves machine groups, each of which contains parallel machines. Solving it is to locate all machines at multiple parallel rows to minimize material flow cost. It is challenging because one needs to decompose material flows and determine exact locations of machines simultaneously. A three-stage approach is proposed to do so. It is applied to many problem instances. The results demonstrate that it works well for such layout problems with parallel machines.

Hospital department layout problems (HDLPs) are significant in enhancing service quality and reducing patients' travel distance and time. Their studies are scarce in comparison with those for facility layout problems in manufacturing systems. Existing approaches to HDLPs usually adopt simplified models and thus gain very limited applications in a real world. HDLPs typically involve multiple objectives that may conflict with each other. There have been no studies on their multiobjective heuristic approaches to our best knowledge. In this paper, we propose multiobjective tabu search (MTS) for a real-world HDLP. Beside the frequently used objective of flow cost, ensuring the closeness among certain departments is introduced as another one. A solution coding scheme is designed to represent a solution. A penalty function is devised to handle infeasible solutions. Local search is integrated into tabu search to optimize the assignment of departments. Experiment results show that MTS is able to produce Pareto solutions that outperform those of the comparative method. Compared to the actually implemented layout, solutions produced by MTS can save about 5%-15% patients' travel time (distance).

Although there exist numerous literatures on facility layout in manufacturing systems, studies on department layout in hospitals are relatively scarce. In this paper, we proposed a MOEA/D based approach for a hospital department layout problem, where three objectives are simultaneously optimized, namely patient flow cost, the closeness of departments and rearrangement cost. A constraint handling technology is introduced to deal with infeasible solutions. Experiments show that the proposed approach is able to get satisfactory Pareto solutions with lower patients' flow cost and the closeness compared to the original layout, and that the optimized layouts decrease the patients' move time, thereby improving quality of service.

Bare-bones Particle Swarm Optimization (BPSO) is a simplified PSO variant, which has shown potential performance on many multimodal optimization problems. However, BPSO is also possible to be trapped into local optima for high-dimensional and complicated optimization problems. In order to enhance the performance of BPSO, this paper presents a modified BPSO, called NMBPSO. It combined the ideas of the traditional PSO and a modified BPSO to improve the capacity of balancing exploration and exploitation during the search process. To verify the effect and benefit of the proposed algorithm, a set of well known benchmark functions are employed and compared against some competitive PSO variants. Experiment results indicate that NMBPSO performs better than the traditional PSO, BPSO and a modified BPSO algorithm.

In this paper, we propose a robust double row layout problem (RDRLP), where the material flow between any two machines may vary in different periods. A MOEA/D based solution approach is proposed to solve it. First, MOEA/D is used to find a collection of non-dominated machine sequences. Then, for each found machine sequence, MOEA/D is used to produce a set of non-dominated solutions. Finally, the final set of Pareto solutions is constructed from all the produced non-dominated solutions. The crowding-distance calculation is added to the procedure of updating the elite population to make nondominated solutions distributed uniformly. An integer coding and a real-valued coding with their corresponding crossover and mutation operators are presented. This approach is applied to a number of problem instances with 10-35 facilities and 3-5 periods. Experimental results show that the approach is able to effectively solve this problem.

Double-row layout problem (DRLP) is a new problem proposed in 2010. Different from single or multi-row layout problems, DRLP needs to determine not only the sequence of machines on both rows but also the exact location of each machine. Aiming at the dynamic environment of product processing in practice, in this paper we study DRLP under dynamic environment and propose a dynamic double-row layout problem (DDRLP) where the material flows may change over time. A mixed-integer programming model is established for the DDRLP. An improved simulated annealing (ISA) algorithm is proposed to for this problem. To represent a feasible solution, a mixed coding scheme is suggested to express the sequence of facilities and the exact location of each facility. Five operators are devised to make the ISA able to effectively solve this problem. Experiment results show that the proposed algorithm is able to find the optimal solutions for small size problem instances and outperform an exact approach (CPLEX) under limited run time for large size instances.

Virtual network embedding, which means mapping virtual networks requested by users to a shared substrate network maintained by an Internet service provider, is a key function that network virtualization needs to provide. Prior work on virtual network embedding has primarily focused on maximizing the revenue of the Internet service provider and did not consider the energy cost in accommodating such requests. As energy cost is more than half of the operating cost of the substrate networks, while trying to accommodate more virtual network requests, minimizing energy cost is critical for infrastructure providers. In this paper, we make the first effort toward energy-aware virtual network embedding. We first propose an energy cost model and formulate the energy-aware virtual network embedding problem as an integer linear programming problem. We then propose two efficient energy-aware virtual network embedding algorithms: a heuristic-based algorithm and a particle-swarm-optimization-technique-based algorithm. We implemented our algorithms in C++ and performed side-by-side comparison with prior algorithms. The simulation results show that our algorithms significantly reduce the energy cost by up to 50% over the existing algorithm for accommodating the same sequence of virtual network requests.

The adoption of radio frequency identification (RFID) tags and devices facilitates the observation and monitoring of event patterns, while highly temporal restrained RFID data further make events complicated. Although the existing Pub/Sub systems can help distributed applications monitor and disseminate interesting events, they are unable to express RFID-related events directly. Extending a system for new RFID requirements not only needs a lot of work, but also provides no performance guarantees. It is, therefore, necessary to design and implement an effective and efficient Pub/Sub system in order to capture and disseminate RFID-related events. This paper presents a composite subscription specification for RFID-related application scenarios. It enables the subscription of predicates for RFID code as well as various complex events. Moreover, this paper proposes the algorithms for RFID-code subscription matching and complex events detection which have been implemented in our Pub/Sub system, OncePubSub. Also, experiments were conducted to quantify the performance and overhead of the above algorithms. Performance evaluation results indicate that OncePubSub is more efficient than SIENA- and JESS-based systems.

RFID technology facilitates monitoring and managing products. In most RFID applications, users pay much attention to temporal and logical event patterns, which is further strengthened by highly temporal RFID data. If RFID events or other kinds of events are disseminated to users through Pub/Sub service, visibility of product states will be greatly increased and fast processing these events will become possible. However, the traditional Pub/Sub systems cannot support composite events involving abundant temporal constraints. Therefore, they cannot fully meet the needs of RFID applications. Aiming at RFID-related application scenarios, this paper designs a composite subscription language embedded with diversified temporal operators and proposes a matching algorithm for detecting composite events which has been implemented in our Pub/Sub system OncePubSub. Performance evaluation results indicate our algorithm is more efficient than the two systems based on Siena and JESS respectively.

Evolutionary algorithm (EA) with non-uniform mutation has the merits of even ldquolonger jumpsrdquo than Cauchy mutation at the early stage of the algorithm and much ldquofiner-tuningsrdquo than Gaussian mutation operator at the later stage. Empirical comparisons with the recently proposed EAs show its excellence solution quality and reliability. One unified algorithmic framework with non-uniform mutation operator and its convergence analysis based on this algorithmic framework are provided in this paper. Two lemmas and two theorems are presented to show the relevant convergence properties of unimodal and multimodal functions.

For a given real-world problem, we do not know a priori which representation suits this problem. Schnier and Yao showed the benefit of using the multiple real-coded evolutionary algorithm. This paper discusses the multiple bit encoding-based (standard binary and reflected Gray code) search algorithms. The population-based genetic algorithm and single individual-based random bit climber algorithm are used to show the efficacy of the multiple encoding. Each algorithm has three versions which are based on the standard binary encoding, standard reflected Gray code and multiple encoding scheme respectively. The experiments on two search algorithms show the robust and better performance than the single encoding scheme and also show empirically how multiple representations can benefit search as much as a good search operator could.