Search

In social network applications, competitive influence propagation often exhibits a certain degree of time lag. In the scenario of positive and negative competitive propagation studied in this paper, under the premise that negative nodes are activated first, how to find a set of positive seed nodes to participate in competitive propagation is studied, aiming to minimize the spread of negative influence. In the current study, the time complexity of the improved algorithms based on greedy strategies is high, which limits their scope of application in practical scenarios; some heuristic algorithms achieve better scalability, but there is still much room for improvement. Therefore, this paper proposes a new method to solve the influence propagation problem of delayed competition, also known as a heuristic propagation factor evaluation algorithm (HeuPFE). The main process is as follows: (1) We build a shortest path snapshot for the nearest propagation region of negative nodes and reduce the search space of competing nodes. (2) Then, we construct a node propagation factor evaluation method based on this shortest negative-oriented node path snapshot to reduce computational complexity. Comparing results with those of traditional heuristic algorithms, we carry out experiments on real datasets and verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a new model predictive control termed as intelligent model predictive control (IMPC) combined with an improved new competitive swarm optimizer (CSO) is designed. The analytical predictive model is not necessarily established a priori in the proposed IMPC algorithm, and the control plant can be used directly as the predictive model to reduce the complexity of the algorithm. In addition, two new techniques, dynamic initialization and back steps methods, are proposed and utilized to improve the traditional CSO to realize constraints management during the optimization process. An application to aeroengine transient-state control is studied to verify the effectiveness of the presented IMPC algorithm. It is shown that, benefitting from the IMPC algorithm, the control task is well completed and all the constraints are satisfied. Practical Applications: This paper proposes a new optimization algorithm to solve some complex optimization problems with constraints. The designed new optimization algorithm is simple and easy to implement and it is more suitable for applications with black-box models or complex optimization objective functions compared with some existing optimization algorithms. This paper may provide some help to researchers who are interested in model predictive control or metaheuristic algorithms and to people whose work contains some complex optimization problems. This paper also provides a design method of aeroengine transient-state control plans. The simulation results showed that compared with some existing control methods or optimization algorithms, the aeroengine could accelerate to the desired steady-state point with shorter transient time and all constraints are satisfied when utilizing the proposed new optimization algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a scenario in which protected and unprotected services coexist in an elastic optical network under dynamic traffic. In the investigated scenario, unprotected services can reuse the reserved idle bandwidth to provide protection to the protected services. Under this scenario, we propose a new heuristic algorithm that enables such reuse as well as define and introduce a new assignment problem in elastic optical networks, named a Transmission Spectrum Assignment (T-SA) problem. In this paper, we consider a scenario in which services may be routed using the multipath routing approach. Additionally, protection using bandwidth squeezing is also considered. We assess our proposal through simulations on three different network topologies and compare our proposal against the classical protection approach, in which bandwidth reuse is not allowed. For the simulated range of network loads, the maximum (minimum) blocking probability reduction obtained by our proposal is approximately 48% (10%) in the European topology, 46% (7%) in the NSFNET topology, and 32% (6%) in the German topology. [ABSTRACT FROM AUTHOR]

Quantum computing has already demonstrated great computational potential across multiple domains and has received more and more attention. However, due to the connectivity limitations of Noisy Intermediate-Scale Quantum (NISQ) devices, most of the quantum algorithms cannot be directly executed without the help of inserting SWAP gates. Nevertheless, more SWAP gates lead to a longer execution time and, inevitably, lower fidelity of the algorithm. To this end, this paper proposes an optimized qubit mapping algorithm based on a dynamic look-ahead strategy to minimize the number of SWAP gates inserted. Firstly, a heuristic algorithm is proposed based on maximizing physical qubit connectivity to generate the optimal initial qubit mapping, which reduces the need for logical qubit shifts during subsequent SWAP gate insertion. Secondly, in the form of directed acyclic graphs, we identify quantum gates that violate the constraints of physical coupling and insert SWAP gates to remap qubits, thereby overcoming the limitations of qubit interactions. Finally, the optimal SWAP gate insertion strategy is built by comparing the cost of different SWAP gate insertion strategies through a multi-window look-ahead strategy to reduce the number of SWAP gates inserted. The experimental results show that the strategy in this paper decreases the number of SWAP gate insertions and significantly reduces the depth of quantum circuits when performing qubit mapping compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we consider the Cumulative Capacitated Vehicle Routing Problem (CCVRP), which is used to model many real-world applications, especially in the area of supplying humanitarian aid after a natural disaster. Interestingly, in this study, Unmanned Aerial Vehicles (UAVs) so-called drones are used to assist the trucks in delivering parcels to customers. Each of them travels to different locations to perform specified tasks and meet periodically with the vehicle from which it takes off to swap its battery. In this paper, a mathematical model is formulated which is then tested with small-sized problems using CPLEX software. Due to the difficulty of solving large instances to optimality, we propose a heuristic algorithm based on a well-established type of cluster-first, route-second approach. Then, it is enhanced by some proposed properties. Hereafter, we develop a minimum spanning tree based-lower bound. Finally, extensive computational experiments are carried out by using instances from the literature. They show the good performance of the proposed heuristic algorithm to solve large instances in a very competitive running time. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) systems generate a vast volume of data to process, and the volume of data generated by the IoT will continue to expand exponentially in the future. Due to the large-scale nature of such systems and their geographically distributed nature, how to optimally place operators and brokers for data processing across cloud, fog, and edge is the main concern of this paper. Furthermore, in the past, IoT systems relied on cloud-based brokers. If the communication between operators in an IoT application has to be regulated by a broker located in the cloud, the advantages brought by the processing of nodes located at the edge and the fog may be neglected. We consider various factors, such as the impact of different network topologies on operators and brokers, the needs of IoT application users, the performance of sensor layers, and the location characteristics of IoT sensors. In diverse placement strategies, the characteristics of the broker have been given special attention. This paper considers the combined architecture of the operator and broker on the edge, fog, and cloud as an optimization problem of minimizing computational processing and data transmission cost. We propose jointly placing the IoT application's operator and broker on the edge, fog, and cloud nodes. Then we proposed two algorithms: the low-complexity heuristic algorithm named GREEDY and the genetic algorithm called GA. Simulation results show that, in most situations, the GA placement method outperforms others and provides a near-optimal solution. [ABSTRACT FROM AUTHOR]

Mission scheduling is an essential function of the management control of remote-sensing satellite application systems. With the continuous development of remote-sensing satellite applications, mission scheduling faces significant challenges. Existing work has many inherent shortcomings in dealing with dynamic task scheduling for remote-sensing satellites. In high-load and complex remote sensing task scenarios, there is low scheduling efficiency and a waste of resources. The paper proposes a scheduling method for remote-sensing satellite applications based on dynamic task prioritization. This paper combines the and Bound methodologies with an onboard task queue scheduling band in an active task prioritization context. A purpose-built emotional task priority-based scheduling blueprint is implemented to mitigate the flux and unpredictability characteristics inherent in the traditional satellite scheduling paradigm, improve scheduling efficiency, and fine-tune satellite resource allocation. Therefore, the Branch and Bound method in remote-sensing satellite task scheduling will significantly save space and improve efficiency. The experimental results show that comparing the technique to the three heuristic algorithms (GA, PSO, DE), the BnB method usually performs better in terms of the maximum value of the objective function, always finds a better solution, and reduces about 80% in terms of running time. [ABSTRACT FROM AUTHOR]

This paper presents an improved swarming algorithm that enhances low-illumination images. The algorithm combines a hybrid Harris Eagle algorithm with double gamma (IHHO-BIGA) and incomplete beta (IHHO-NBeta) functions. This paper integrates the concept of symmetry into the improvement steps of the image adaptive enhancement algorithm. The enhanced algorithm integrates chaotic mapping for population initialization, a nonlinear formula for prey energy calculation, spiral motion from the black widow algorithm for global search enhancement, a nonlinear inertia weight factor inspired by particle swarm optimization, and a modified Levy flight strategy to prevent premature convergence to local optima. This paper compares the algorithm's performance with other swarm intelligence algorithms using commonly used test functions. The algorithm's performance is compared against several emerging swarm intelligence algorithms using commonly used test functions, with results demonstrating its superior performance. The improved Harris Eagle algorithm is then applied for image adaptive enhancement, and its effectiveness is evaluated on five low-illumination images from the LOL dataset. The proposed method is compared to three common image enhancement techniques and the IHHO-BIGA and IHHO-NBeta methods. The experimental results reveal that the proposed approach achieves optimal visual perception and enhanced image evaluation metrics, outperforming the existing techniques. Notably, the standard deviation data of the first image show that the IHHO-NBeta method enhances the image by 8.26%, 120.91%, 126.85%, and 164.02% compared with IHHO-BIGA, the single-scale Retinex enhancement method, the homomorphic filtering method, and the limited contrast adaptive histogram equalization method, respectively. The processing time of the improved method is also better than the previous heuristic algorithm. [ABSTRACT FROM AUTHOR]

Trauma-informed design and technology have been an important topic of discussion in the last 5 years in user experience. However, little exists to offer practical direction for practitioners applying trauma-informed approaches to websites, apps, and other digital tools. This paper shares an example of applying trauma-informed (TI) principles to a social service agency website in the United States using wellknown usability heuristics, which are principles used in interaction design to ensure usability. There appears to be a significant overlap between usability heuristics and TI principles; practitioners can use usability heuristics as a familiar foundation to make any design more traumainformed. This paper describes specific ways TI principles can be operationalized using usability heuristics to develop more user-friendly and trauma-informed websites. A case study in design shows how user experience professionals can be more trauma-informed in their work to better serve the general public or users who have experienced trauma. [ABSTRACT FROM AUTHOR]

In this paper, a novel Moth-Flame Optimization (MFO) algorithm, namely MFO algorithm enhanced by Multiple Improvement Strategies (MISMFO) is proposed for solving parameter optimization in Multi-Kernel Support Vector Regressor (MKSVR), and the MISMFO-MKSVR model is further employed to deal with the software effort estimation problems. In MISMFO, the logistic chaotic mapping is applied to increase initial population diversity, while the mutation and flame number phased reduction mechanisms are carried out to improve the search efficiency, as well the adaptive weight adjustment mechanism is used to accelerate convergence and balance exploration and exploitation. The MISMFO model is verified on fifteen benchmark functions and CEC 2020 test set. The results show that the MISMFO has advantages over other meta-heuristic algorithms and MFO variants in terms of convergence speed and accuracy. Additionally, the MISMFO-MKSVR model is tested by simulations on five software effort datasets and the results demonstrate that the proposed model has better performance in software effort estimation problem. The Matlab code of MISMFO can be found at https://github.com/loadstar1997/MISMFO. [ABSTRACT FROM AUTHOR]

In many engineering optimization problems, the number of function evaluations is severely limited by the time or cost constraints. These limitations present a significant challenge in the field of global optimization, because existing metaheuristic methods typically require a substantial number of function evaluations to find optimal solutions. This paper presents a new metaheuristic optimization algorithm that considers the information obtained by a radial basis function neural network (RBFNN) in terms of the objective function for guiding the search process. Initially, the algorithm uses the maximum design approach to strategically distribute a set of solutions across the entire search space. It then enters a cycle in which the RBFNN models the objective function values from the current solutions. The algorithm identifies and uses key neurons in the hidden layer that correspond to the highest objective function values to generate new solutions. The centroids and standard deviations of these neurons guide the sampling process, which continues until the desired number of solutions is reached. By focusing on the areas of the search space that yield high objective function values, the algorithm avoids exhaustive solution evaluations and significantly reduces the number of function evaluations. The effectiveness of the method is demonstrated through a comparison with popular metaheuristic algorithms across several test functions, where it consistently outperforms existing techniques, delivers higher-quality solutions, and improves convergence rates. [ABSTRACT FROM AUTHOR]

Harris Hawks optimization (HHO) algorithm was a powerful metaheuristic algorithm for solving complex problems. However, HHO could easily fall within the local minimum. In this paper, we proposed an improved Harris Hawks optimization (IHHO) algorithm for solving different engineering tasks. The proposed algorithm focused on random location-based habitats during the exploration phase and on strategies 1, 3, and 4 during the exploitation phase. The proposed modified Harris hawks in the wild would change their perch strategy and chasing pattern according to updates in both the exploration and exploitation phases. To avoid being stuck in a local solution, random values were generated using logarithms and exponentials to explore new regions more quickly and locations. To evaluate the performance of the proposed algorithm, IHHO was compared to other five recent algorithms [grey wolf optimization, BAT algorithm, teaching–learning-based optimization, moth-flame optimization, and whale optimization algorithm] as well as three other modifications of HHO (BHHO, LogHHO, and MHHO). These optimizers had been applied to different benchmarks, namely standard benchmarks, CEC2017, CEC2019, CEC2020, and other 52 standard benchmark functions. Moreover, six classical real-world engineering problems were tested against the IHHO to prove the efficiency of the proposed algorithm. The numerical results showed the superiority of the proposed algorithm IHHO against other algorithms, which was proved visually using different convergence curves. Friedman's mean rank statistical test was also inducted to calculate the rank of IHHO against other algorithms. The results of the Friedman test indicated that the proposed algorithm was ranked first as compared to the other algorithms as well as three other modifications of HHO. [ABSTRACT FROM AUTHOR]

This paper addresses a two-machine re-entrant flow shop scheduling problem with stochastic processing times where each job is expected to require a rework phase, flowing twice within the whole system. Due to the stochastic characteristics of the addressed problem, the proposed approach aims to devise robust schedules, i.e., schedules that are less sensitive to the occurrence of uncertain events, specifically, to the variability of the processing times. Two classes of approaches are proposed: the first is a branch-and-bound algorithm capable of solving the problem optimally, although with limitations regarding the size of the scheduling instances; the second is heuristic algorithms that can be applied to medium/large instances. For both approaches, the goal is to minimise the value-at-risk associated with the makespan, to assist decision-makers in balancing expected performance and mitigating the impact of extreme scenarios. A Markovian Activity Network (MAN) model is exploited to estimate the distribution of the makespan and evaluate its value-at-risk. Phase-type distributions are used to cope with general distributions for the processing times while exploiting a Markovian approach. A set of computational experiments is conducted to demonstrate the effectiveness and performance of the proposed approaches. [ABSTRACT FROM AUTHOR]

Mobile robots play an important role in the industrial Internet of Things (IIoT); they need effective mutual communication between the cloud and themselves when they move in a factory. By using the sensor nodes existing in the IIoT environment as relays, mobile robots and the cloud can communicate through multiple hops. However, the mobility and delay sensitivity of mobile robots bring new challenges. In this paper, we propose a dynamic cooperative transmission algorithm with mutual information accumulation to cope with these two challenges. By using rateless coding, nodes can reduce the delay caused by retransmission under poor channel conditions. With the help of mutual information accumulation, nodes can accumulate information faster and reduce delay. We propose a two-step dynamic algorithm, which can obtain the current routing path with low time complexity. The simulation results show that our algorithm is better than the existing heuristic algorithm in terms of delay. [ABSTRACT FROM AUTHOR]

This paper deals with an optimization problem of determining the maintenance units and age‐based inspection schemes to minimize the expected maintenance cost rate and satisfy a target system availability for multi‐indenture systems that have a modular structure with redundant units. Failures of primary components directly lead to system failures and can therefore be identified and maintained immediately, while failures of redundant components need to be identified by inspections. And the designated maintenance units need to be replaced during maintenance. By constructing the dual structure of the fault tree, we present a failure path enumeration method, based on which the system availability and expected maintenance cost rate models considering maintenance time are derived. For redundant units, two types of inspection policies are considered in our models: one is the corrective maintenance (CM)‐based inspection policy, and the other one is the age‐based inspection (PM) policy, inspections are performed at failures or age h, depending on which occurred earlier. A heuristic algorithm is proposed to find the reasonable maintenance units and inspection intervals simultaneously, and its performance is also compared and analyzed. Several numerical examples are studied to investigate the effect of parameters on optimal solutions. [ABSTRACT FROM AUTHOR]

In IoT paradigm, Sensor-Cloud Infrastructure provides sensor nodes that sense various environmental parameters, generates the data and sends the same to the desired destination, say a cloud server through a common gateway. Sensor nodes with different data streaming specifications, can generate huge amount of traffic, if streams data simultaneously towards the destination, according to a random schedule. This can lead to higher bandwidth requirements in the wireless medium and increase the amount of data to be received at the gateway in any time slot. This further increases the channel capacity required at the access link to transmit the received data from gateway to the server. An optimal schedule of the sensor nodes will lead to minimization of instantaneous aggregated traffic in both the wireless medium and the access link. Thus leading to minimization of required bandwidth at the wireless medium and channel capacity at the access link. This would further increase the resource utilization of minimize the service provisioning cost of the sensor-cloud infrastructure. A straight forward optimization of the problem of minimizing the instantaneous aggregated traffic load generated from n sensor nodes require an exponential time to find the optimal schedule. Thus, in this paper, an ILP formulation and a polynomial-time heuristic algorithm is presented. [ABSTRACT FROM AUTHOR]

This paper incorporates the shortest passenger travel paths into the first train timetabling problem in urban rail transit networks. We first propose a path-based first train timetabling problem, which is formulated as a mixed-integer nonlinear programming model to reduce the travel time for passengers. To solve this model, we present a tailored branch-and-bound combined with the timetable-based Dijkstra algorithm. To further enhance the efficiency of the exact algorithm, we design a rolling time heuristic algorithm as a substitute for the branch-and-bound algorithm. Then, we proved that the rolling time heuristic combined with the timetable-based Dijkstra algorithm achieves 97.86% and 99.39% reductions in CPU time while sacrificing only 0.007% and 0.057% of optimality gap by two numerical experiments. Finally, the results based on two test networks with different structures demonstrate that the path-based first train timetabling model effectively improves passenger travel efficiency. Specifically, the travel times are reduced by 744 mins and 761 mins, while the transfer waiting times are reduced by 37.87% and 58.91% compared to the existing timetables. Moreover, the results also demonstrate that loop line networks will increase the difficulty of timetabling and lead to many passengers taking detours. To further enhance the efficiency of commuting for passengers who take the first train, URT operators may consider relaxing the limitations on the departure time of the depot stations. • A path-based first train timetabling model to minimize the total travel time in urban rail transit networks. • A tailored branch-and-bound combined with a timetable-based Dijkstra algorithm. • A rolling time heuristic algorithm to solve large-scale networks cases. [ABSTRACT FROM AUTHOR]

This article delves into the critical realm of modern computer cluster management. It focuses on the effect that the increasing heterogeneity of the clusters has on the workload managers. The proposed schedulers consider node properties instead of job properties to make decisions, which is something not currently done by mainstream scheduling algorithms. In order to increase the knowledge in this topic, this paper proposes two novel algorithms whose main task is to choose the best compute nodes to schedule the incoming jobs. To this effect, they exclusively take into account the properties of the nodes, instead of the common trend of considering the properties of the jobs. The experimental results show that these algorithms outperform well-known heuristic algorithms found in the literature. [ABSTRACT FROM AUTHOR]

Diverse demands have promoted the rapid development of the cold chain logistics industry. In the paper, a novel approach for calculating the comprehensive carbon emission cost was proposed and the front warehouse mode was analyzed under the background of energy conservation and emission reduction. To solve the two-echelon low-carbon location-routing problem (2E-LCLRP), a mathematical model considering operating cost, total transportation cost, fixed cost, refrigeration cost, cargo damage cost, and comprehensive carbon emission cost was proposed to determine the minimum total cost. A hybrid ant colony optimization (HACO) algorithm based on an elbow rule and an improved ant colony optimization (IACO) algorithm was proposed to solve the 2E-LCLRP. According to the elbow rule, the optimal number of front warehouses was determined and an IACO algorithm was then designed to optimize vehicle routes. An adaptive hybrid selection strategy and an optimized pheromone update mechanism were integrated into the HACO algorithm to accelerate convergence and obtain global optimal solutions. The proposed model and algorithm were verified through the case study of the 2E-LCLRP in Nanjing, China. The HACO algorithm outperformed the original ant colony optimization (ACO) algorithm in terms of convergence rate and solution quality. This study provides significant insights for enhancing heuristic algorithms as well as valuable research methods. Furthermore, the results can help cold chain logistics companies in balancing economic costs and environmental benefits and address cold chain distribution of agricultural products. [ABSTRACT FROM AUTHOR]

In the realm of offline handwritten text recognition, numerous normalization algorithms have been developed over the years to serve as preprocessing steps prior to applying automatic recognition models to handwritten text scanned images. These algorithms have demonstrated effectiveness in enhancing the overall performance of recognition architectures. However, many of these methods rely heavily on heuristic strategies that are not seamlessly integrated with the recognition architecture itself. This paper introduces the use of a Pix2Pix trainable model, a specific type of conditional generative adversarial network, as the method to normalize handwritten text images. Also, this algorithm can be seamlessly integrated as the initial stage of any deep learning architecture designed for handwritten recognition tasks. All of this facilitates training the normalization and recognition components as a unified whole, while still maintaining some interpretability of each module. Our proposed normalization approach learns from a blend of heuristic transformations applied to text images, aiming to mitigate the impact of intra-personal handwriting variability among different writers. As a result, it achieves slope and slant normalizations, alongside other conventional preprocessing objectives, such as normalizing the size of text ascenders and descenders. We will demonstrate that the proposed architecture replicates, and in certain cases surpasses, the results of a widely used heuristic algorithm across two metrics and when integrated as the first step of a deep recognition architecture. [ABSTRACT FROM AUTHOR]

The design of fault-tolerant production and delivery systems is one of the priority areas in modern operations research. The traditional approach to modeling such systems is based on the use of stochastic models that describe the choice of a possible scenario of actions in the event of problems in a production or transportation network. Along with a number of advantages, this approach has a known drawback. The occurrence of problems of an unknown nature that can jeopardize the performance of the entire simulated system significantly complicates its use. This paper introduces the minimax problem of constructing fault-tolerant production plans (reliable production process design problem, RPPDP), the purpose of which is to ensure the uninterrupted operation of a distributed production system with minimal guaranteed cost. It is shown that the RPPDP is NP-hard in the strong sense and remains intractable under quite specific conditions. To find exact and approximate solutions with accuracy guarantees for this problem, branch-and-bound methods are developed based on the proposed compact model of the mixed integer linear program (MILP) and novel heuristic of adaptive search in large neighborhoods (adaptive large neighborhood search, ALNS) as part of extensions of the well-known Gurobi MIP solver. The high performance and complementarity of the proposed algorithms is confirmed by the results of numerical experiments carried out on a public library of benchmarking instances developed by the authors based on instances from the PCGTSPLIB library. [ABSTRACT FROM AUTHOR]

Causal discovery is central to human cognition, and learning directed acyclic graphs (DAGs) is its foundation. Recently, many nature-inspired meta-heuristic optimization algorithms have been proposed to serve as the basis for DAG learning. However, a single meta-heuristic algorithm requires specific domain knowledge and empirical parameter tuning and cannot guarantee good performance in all cases. Hyper-heuristics provide an alternative methodology to meta-heuristics, enabling multiple heuristic algorithms to be combined and optimized to achieve better generalization ability. In this paper, we propose a multi-population choice function hyper-heuristic to discover the causal relationships encoded in a DAG. This algorithm provides a reasonable solution for combining structural priors or possible expert knowledge with swarm intelligence. Under a linear structural equation model (SEM), we first identify the partial v-structures through partial correlation analysis as the structural priors of the next nature-inspired swarm intelligence approach. Then, through partial correlation analysis, we can limit the search space. Experimental results demonstrate the effectiveness of the proposed methods compared to the earlier state-of-the-art methods on six standard networks. [ABSTRACT FROM AUTHOR]

The besiege and conquer algorithm has shown excellent performance in single-objective optimization problems. However, there is no literature on the research of the BCA algorithm on multi-objective optimization problems. Therefore, this paper proposes a new multi-objective besiege and conquer algorithm to solve multi-objective optimization problems. The grid mechanism, archiving mechanism, and leader selection mechanism are integrated into the BCA to estimate the Pareto optimal solution and approach the Pareto optimal frontier. The proposed algorithm is tested with MOPSO, MOEA/D, and NSGAIII on the benchmark function IMOP and ZDT. The experiment results show that the proposed algorithm can obtain competitive results in terms of the accuracy of the Pareto optimal solution. [ABSTRACT FROM AUTHOR]

This paper presents an approach to community detection in complex networks by simultaneously incorporating a connectivity-based metric and Max-Min Modularity. By leveraging the connectivity-based metric and employing a heuristic algorithm, we develop a novel complementary graph for the Max-Min Modularity that enhances its effectiveness. We formulate community detection as an integer programming problem of an equivalent yet more compact counterpart model of the revised Max-Min Modularity maximization problem. Using a row generation technique alongside the heuristic approach, we then provide a hybrid procedure for near-optimally solving the model and discovering high-quality communities. Through a series of experiments, we demonstrate the success of our algorithm, showcasing its efficiency in detecting communities, particularly in extensive networks. [ABSTRACT FROM AUTHOR]

The motivation of task scheduling in heterogeneous computing systems is the optimal management of heterogeneous distributed resources as well as the exploitation of system capabilities. Energy consumption is one of the most important issues in dealing with task scheduling in heterogeneous distributed systems. In addition to energy, the task completion time and the task cost have also been added to the concerns of the users. Since the nature of computing systems is heterogeneous and dynamic, task scheduling with traditional methods is inefficient. Meta-heuristic approaches for task scheduling in heterogeneous distributed systems are open problems that have attracted the attention of researchers. So far, many meta-heuristic approaches have addressed the task scheduling problem. However, most of these algorithms are developed for homogeneous systems and optimize only one of the quality-of-service parameters. With this motivation, this paper presents an optimization for energy-aware design of task scheduling in heterogeneous distributed systems using meta-heuristic approaches. We simultaneously consider several parameters such as energy, task completion time and task execution cost for task scheduling. The Harris Hawk Optimization (HHO) algorithm is considered for the optimization task due to its adaptability to large search spaces. We combine HHO with a greedy algorithm to avoid local optima and early convergence. The evaluation of the proposed method has been done through numerical simulations. Experimental results show promising performance of the proposed method in terms of energy consumption. [ABSTRACT FROM AUTHOR]

The shuttle-based storage/retrieval system (SBS/RS) is a new type of automatic warehouse system. More and more enterprises choose it because of its dense storage structure, small footprint, high efficiency, and many other advantages. Each enterprise has different requirements for system efficiency, the number of storage units, and the size of the warehouse. It makes sense to study the design of the minimum cost Shuttle-Based Storage/Retrieval System under the limitation of warehouse space size to meet enterprise requirements on the number of cargos and enterprise needs on system efficiency. An optimization model to minimize the system cost is established, and a heuristic algorithm is designed in this paper. Through the experiment, we identified that different system selection schemes and the configuration of facilities in the system under different throughput capacity requirements. The relationship between the system's total cost, unit cost, and design scheme of the system is found. This study can guide designers and planners to design a more reasonable system. Under the different needs of enterprises, build a Shuttle-Based Storage/Retrieval System with the lowest cost. [ABSTRACT FROM AUTHOR]

The determination of transport schemes represents a critical facet of operational management in express freight logistics and constitutes a significant scientific challenge within the domain of freight transportation. Globally, there is an escalating focus on adopting low-carbon transit strategies, and it has been established that multimodal transport methods can significantly diminish carbon emissions associated with freight transportation. Moreover, the scheduled arrival and departure times for railway and aviation are pivotal in shaping the selection of transport schemes. Regrettably, these critical considerations are frequently neglected in the prevailing body of research. This paper endeavors to bridge this research gap by advocating for a lowcarbon transport scheme for express freight through the utilization of an integrated transportation system. It undertakes a collaborative optimization study concerning transportation modes and routes. This document proposes an optimization framework for the selection of express freight transport schemes that incorporates considerations for carbon emissions and fixed timetables. A three-layer service network is developed, integrating the fixed schedules of railway and aviation to formulate a space-time shortest path problem aimed at minimizing the generalized transportation cost. Three models are formulated to reduce the transportation costs and carbon emissions of logistics enterprises, taking into account diverse carbon emission policies. Subsequently, a two-stage heuristic algorithm is introduced to address this complex problem. The findings reveal that by accounting for customers' transportation time constraints and judiciously selecting transportation modes and routes, it is feasible to effectively minimize transportation costs and carbon emissions. [ABSTRACT FROM AUTHOR]

The aim of this paper is to optimize the allocation of multiple advertisements on a Web banner, where the price of an advertisement depends on the location at the banner. This problem can be defined as a two-dimensional single orthogonal knapsack problem with a location-based pixel-price model. A formulation is proposed in which the problem is specified as a 0–1 integer programming problem. As this problem is NP-complete, we mainly focus on a heuristic approach to solve the problem. We propose two new heuristic algorithms: the reactive GRASP algorithm and the partitioning left-justified algorithm. Next to that, we present an exact algorithm that is able to solve small problem instances in a reasonable time. These newly presented algorithms are compared with respect to efficiency and effectiveness to existing algorithms that solve the problem without a location-based pixel-price model. To test the quality of the algorithms, we have executed two experiments. The results of these experiments show that overall the reactive GRASP algorithm is the most effective algorithm, whereas the greedy stripping algorithm is the most efficient. [ABSTRACT FROM AUTHOR]

Topology control is a significant research problem when it comes to designing energy-efficient wireless sensor networks (WSNs). It is a metric for evaluating network service quality. To ensure the quality of network services, it is critical to provide energy-efficient sensor node scheduling in order to extend the network's lifespan. In this paper, we propose an energy-aware scheduling protocol (ESP)-based hybrid metaheuristic technique for minimizing the amount of energy consumption and ensuring sufficient coverage for the monitored area while maximizing the network lifespan for WSNs. The ESP consists of two phases: distributed clustering and sensor node scheduling. The clustering phase implements the DBSCAN clustering algorithm with slide modification to cluster the sensor nodes in the area of interest into clusters of nodes, and the efficient periodic cluster head election approach is proposed based on criteria such as remaining energy, number of neighbors, and distance for each node in the cluster. This clustering is only performed at the beginning to group the nodes into clusters. Then, only the cluster head election will be done every period. In the scheduling phase, we model the scheduling optimization problem using three objectives: reducing the number of uncovered zones, minimizing the number of active sensors, and selecting the active sensor nodes with the maximum remaining energy. This scheduling optimization model is solved using a hybrid metaheuristic algorithm. When the genetic algorithm's operators were added to the regular Cuckoo algorithm, the balance between exploration and exploitation abilities was improved even more. The algorithms were able to search a larger area. Extensive simulation experiments were conducted using the OMNeT++ network simulator. The results show that the proposed ESP protocol introduces better performance in terms of coverage ratio, active sensor ratio, energy consumption, network lifespan, and execution time compared to other existing techniques. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hyper-heuristic algorithm termed evolutionary multi-mode slime mold optimization (EMSMO) for addressing continuous optimization problems. The architecture of a typical hyper-heuristic algorithm comprises two main components: the high-level component and the low-level component. The low-level component contains a set of low-level heuristics (LLHs) and intrinsic problem attributes, while the high-level component manipulates the LLHs to construct the sequence of heuristics. Inspired by the foraging behaviors of slime mold, we designed four easy-implemented search strategies including the search for food, approach food, wrap food, and re-initialization as the LLHs for the low-level component. In the high-level component, we adopt an improvement-based probabilistic selection function that contains two metrics: (1) the probability of improvement and (2) the normalized improvement. The selection function cooperates with the roulette wheel strategy to construct the optimization sequence. To evaluate the performance of our proposal, we implement comprehensive numerical experiments on CEC2013 benchmark functions and three engineering optimization problems. Six classic or advanced evolutionary algorithms and three hyper-heuristic algorithms are applied as competitor algorithms to evaluate the competitiveness of EMSMO. Experimental and statistical results show that EMSMO has broad prospects for solving continuous optimization problems. [ABSTRACT FROM AUTHOR]

The coati optimization algorithm (COA) is a recently proposed heuristic algorithm. The COA algorithm, which solved the continuous optimization problems in its original paper, has been converted to a binary optimization solution by using transfer functions in this paper. Thus, binary COA (BinCOA) is proposed for the first time in this study. In this study, twenty transfer functions are used (four S-shaped, four V-shaped, four Z-shaped, four U-shaped, and four taper-shaped transfer functions). Thus, twenty variations of BinCOA are obtained, and the effect of each transfer function on BinCOA is examined in detail. The knapsack problem (KP) and uncapacitated facility location problem (UFLP), which are popular binary optimization problems in the literature, are chosen to test the success of BinCOA. In this study, small-, middle-, and large-scale KP and UFLP datasets are selected. Real-world problems are not always low-dimensional. Although a binary algorithm sometimes shows superior success in low dimensions, it cannot maintain the same success in large dimensions. Therefore, the success of BinCOA has been tested and demonstrated not only in low-dimensional binary optimization problems, but also in large-scale optimization problems. The most successful transfer function is T3 for KPs and T20 for UFLPs. This showed that S-shaped and taper-shaped transfer functions obtained better results than others. After determining the most successful transfer function for each problem, the enhanced BinCOA (EBinCOA) is proposed to increase the success of BinCOA. Two methods are used in the development of BinCOA. These are the repair method and the XOR gate method. The repair method repairs unsuitable solutions in the population in a way that competes with other solutions. The XOR gate is one of the most preferred methods in the literature when producing binary solutions and supports diversity. In tests, EBinCOA has achieved better results than BinCOA. The added methods have proven successful on BinCOA. In recent years, the newly proposed evolutionary mating algorithm, fire hawk optimizer, honey badger algorithm, mountain gazelle optimizer, and aquila optimizer have been converted to binary using the most successful transfer function selected for KP and UFLP. BinCOA and EBinCOA have been compared with these binary heuristic algorithms and literature. In this way, their success has been demonstrated. According to the results, it has been seen that EBinCOA is a successful and preferable algorithm for binary optimization problems. [ABSTRACT FROM AUTHOR]

In recent years, the automotive industry has witnessed the increasing adoption of mixed-model assembly lines (MMALs) to meet the demands of mass customization. However, the diverse components of various end products are posing significant challenges to the material feeding processes in MMALs. To address this issue and enhance the material feeding efficiency, this paper proposes an innovative automatic monorail shuttle system (AMSS). The AMSS is designed based on the layout of line-integrated supermarkets and incorporates the load-exchangeable shuttles and crossovers. Considering the importance of cost control in manufacturing enterprises, the objective of this paper is to minimize the total costs associated with the material feeding system, which includes the installation costs of crossovers, as well as the input and operation costs of load-exchangeable shuttles. A mathematical model is thereby established to describe the problem, and its accuracy is validated through the Gurobi solver. Recognizing the NP-hard nature of the proposed problem, a shuffled frog leaping-based hyper-heuristic (SBHH) algorithm is developed to allocate and schedule the shuttles and crossovers, which adopts the shuffled frog leaping algorithm as the high-level heuristic for selecting low-level heuristics. To further improve the performance, dynamic decision unit is introduced to raise the solution accuracy and the convergence speed. Simulation results verify the superiority of the proposed SBHH algorithm both in solution quality and convergence speed by comparing with other benchmark optimization algorithms. Managerial applications indicate the effectiveness and practicality of the proposed approach, offering implications for practitioners in the automotive industry. [ABSTRACT FROM AUTHOR]

Given the complexity and wide range of applications being developed for the Internet of Things (IoT) requiring an efficient mapping of application tasks in hardware resources, especially embedded systems, it is necessary to address the problem of resource optimization, taking into account constraints for energy consumption, latency, and bandwidth, among others. This paper proposes a population based multi-objective heuristic algorithm to solve the task mapping for an application into a hierarchical hardware architecture known as Wireless Network on Chip using a mathematical modeling, as well as Artificial Intelligence algorithms, that combine the strategies of neural networks and genetic algorithms. A Population Based Incremental Learning (PBIL) algorithm is used in this research to solve the task mapping problem oriented to IoT applications, allowing heterogeneous systems to be involved with different bandwidths, latencies and technologies. The optimization goals addressed in this paper are bandwidth, speedup, power consumption and communication cost. The PBIL algorithm was tested on synthetic applications with heavy workload, where performance is measured taking into account the combination of low power consumption and maximum speedup and bandwidth. Other tests carried out with the PBIL algorithm were aimed to reduce the cost of communication in task mapping for real applications such as Multi-Window Display (MWD) and MPEG4 decoder. The results obtained in both synthetic and real applications showed an improvement when Escort entropy was used compared to Renyi and Shannon. In the case of real applications, when using a 2D Mesh topology, an improvement of 15.25% for MWD and 40.32% for MPEG-4 was observed when compared to the ISFL algorithm. Also, when the Mesh-Star topology was used, better results were obtained compared to ISFL (48.74% MWD and 62.06% MPEG-4). [ABSTRACT FROM AUTHOR]

This paper presents an improved arithmetic optimization algorithm that incorporates hybrid elite pool strategies to address the limitations of the arithmetic optimization algorithm (AOA). In AOA, the linear mathematical optimization acceleration (MOA) function cannot balance global exploitation and local exploration well. Therefore, the accuracy and convergence speed of the algorithm cannot be guaranteed. To improve the performance of AOA, this paper reconstructed a nonlinear MOA function, which is expected to balance the exploitation and the exploration of AOA. Furthermore, four hybrid elite pool strategies are integrated to enhance the ability to escape local optima. The proposed algorithm inherits the fast convergence of AOA and develops the performance of escaping local optima. Numerical experiment results on benchmark functions and engineering problems show that the proposed algorithm outperforms other compared meta-heuristic algorithms in terms of convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

The techniques for solving complex optimization problems using nature inspired metaheuristic algorithms are widely accepted. Nature inspired methods use nature derived approaches to offer an efficient solution within polynomial time. This paper presents analytics of some of the significant nature inspired metaheuristic algorithms. It elaborates on the principles and concepts that are used in these algorithms representing their similarities, variations, and exceptions. The taxonomical classification and analytics presented in this paper list the nature derived phenomenon used to develop a wide variety of nature-inspired techniques. The algorithms are classified as per the type of agents used, search techniques, sub-optimization methods, type of constraints, and nature of problems. The survey comprehends the use of control parameters like exploration and convergence applicable to these algorithms and their domain specifications. The sources of nature inspiration are also presented with their variants. It establishes the analytics required to choose a specific nature-inspired heuristic algorithm for smart farming and related applications. Metaheuristic algorithms like Particle Swarm optimization, Ant colony optimization, Whale optimization, Firefly optimization, etc. have contributed significantly in assisting smart farming methods for better productivity of crops. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of traditional water level prediction methods such as insufficient information mining ability and unclear mechanism of heuristic algorithms, this paper proposes for the first time a water level prediction method based on blockchain technology fused with long short-term memory (LSTM) network. The method utilizes blockchain and LSTM neural network to build a combined model, and directly uploads monitoring data such as import and export water flow and water level to predict the water level, which avoids the secondary error brought by the indirect calculation of flow. In this paper, the flow compensation strategy is proposed for the first time, and the monitoring data with large deviations are compensated accordingly to reduce the prediction error from the source. The results show that the combined Blockchain-LSTM model has the smallest prediction error after adopting the compensation strategy, with the MAE of 0.290 and the RMSE of 0.490, which are smaller than those of other models, and has high prediction accuracy and practicability, which provides technical support for real-time scheduling of the South-to-North Water Diversion Reservoir. [ABSTRACT FROM AUTHOR]

Improving production processes includes not only activities concerning manufacturing itself, but also all the activities that are necessary to achieve the main objectives. One such activity is transport, which, although a source of waste in terms of adding value to the product, is essential to the realization of the production process. Over the years, many methods have been developed to help manage supply and transport in such a way as to reduce it to the necessary minimum. In the paper, the problem of delivering components to a production area using trains and appropriately laid-out carriages was described. It is a milk run stop locations problem (MRSLP), whose proposed solution is based on the use of heuristic algorithms. Intelligent solutions are getting more and more popular in the industry because of the possible advantages they offer, especially those that include the possibility of finding an optimum local solution in a relatively short time and the prevention of human errors. In this paper, the applicability of three algorithms -- tabu search, genetic algorithm, and simulated annealing -- was explored. [ABSTRACT FROM AUTHOR]

This paper studies a class of gradient-descent heuristic algorithms for the bi-level demand adjustment problem (DAP), which seeks to adjust origin-destination (OD) matrices based on observed link flows in congested transportation networks. We first present a general gradient-descent solution framework for the bi-level DAP and then examine and further develop its two building blocks, namely, the gradient approximation and stepsize calculation. This paper presents two gradient approximation and four stepsize calculation methods, of which two stepsize methods are newly developed. Similarities and differences between these algorithms, as well as the relevant implementation issues are discussed in great detail. The numerical results show that algorithms employing the new stepsize calculation strategies consistently outperform existing algorithms in terms of both computational precision and efficiency. [ABSTRACT FROM AUTHOR]

The core of logistics is scheduling and monitoring. After the modern interprise logistics development concept change, the development prospect of enterprise logistics is more optimistic. Major enterprises have begun to use intelligent logistics scheduling platforms. In order to solve the problem that heterogeneous information fusion is complex in the temporal heterogeneous graphs, this paper proposes to dynamically store and update node representation through an augmented memory matrix in a memory network. At the same time, the model also designs a novel read-write module for the memory matrix, which can effectively capture the timing information in the long interaction sequence and has high flexibility. The model has significantly improved in tasks such as node classification, timing recommendation and visualization. This paper studies the logistics supply chain of modern enterprises and establishes the mathematical model of vehicle scheduling. This paper takes the non-full load scheduling model as the critical research object. Based on the research of logistics supply chain, the vehicle scheduling model is established. The intelligent heuristic algorithm is applied to solve it, and the effective vehicle distribution scheme and driving route are formed. The simulation results show that the approximate Pareto optimal solution obtained by our designed model and algorithm has good robustness. NSGAIIROELSDR can get a better solution in small-scale scheduling. However, in large-scale numerical experiments, the final solution obtained by MOEA/DROELSDR is obviously better than that of NSGAIIROELSDR, and the running time of MOEA/DROELSDR is also shorter. Therefore, we conclude that MOEA/DROELSDR is more suitable for large-scale scheduling, and NSGAIIROELSDR is more suitable for more minor scheduling. [ABSTRACT FROM AUTHOR]

Green scheduling that aims to enhance efficiency by optimizing resource allocation and job sequencing concurrently has gained growing academic attention. To tackle such problems with the consideration of scheduling and resource allocation, this paper considers a single-machine group scheduling problem with common/slack due-date assignment and a controllable processing time. The objective is to decide the optimized schedule of the group/job sequence, resource allocation, and due-date assignment. To solve the generalized case, this paper proves several optimal properties and presents a branch-and-bound algorithm and heuristic algorithms. Numerical experiments show that the branch-and-bound algorithm is efficient and the heuristic algorithm developed based on the analytical properties outruns the tabu search. [ABSTRACT FROM AUTHOR]

Micro-irrigation pipe network systems are commonly utilized for water transmission and distribution in agricultural irrigation. They effectively transport and distribute water to crops, aiming to achieve water and energy conservation, increased yield, and improved quality. This paper presents a model for the scaled micro-irrigation pipeline network system and provides a comprehensive review of the fundamental concepts and practical applications of optimization techniques in the field of pipeline network design. This paper is divided into four main sections: Firstly, it covers the background and theoretical foundations of optimal design for scaled micro-irrigation pipeline network systems. Secondly, the paper presents an optimal design model specifically tailored for scaled micro-irrigation pipeline networks. And then, it discusses various optimization solution techniques employed for addressing the design challenges of scaled micro-irrigation pipeline networks, along with real-world case studies. Finally, this paper concludes with an outlook on the ongoing research and development efforts in the field of scaled micro-irrigation pipeline network systems. In addition, this paper establishes a fundamental model for optimizing pipeline networks, to achieve minimum safe operation and total cost reduction. It considers constraints such as pipeline pressure-bearing capacity, maximum flow rate, and diameter. The decision-making variables include pipeline diameter, length, internal roughness, node pressure, future demand, and valve placement. Additionally, this paper provides an extensive overview of deterministic methods and heuristic algorithms utilized in the optimal design of micro-irrigation pipeline networks. Finally, this paper presents future research directions for pipeline network optimization and explores the potential for algorithmic improvements, integration of machine learning techniques, and wider adoption of EPANET 2.0 software. These endeavors aim to lay a strong foundation for effectively solving complex and challenging optimization problems in micro-irrigation pipeline network systems in the future. [ABSTRACT FROM AUTHOR]

A multi-modal search space can be defined as having multiple attraction basins - each basin has a single local optimum which is reached from all points in that basin when greedy local search is used. Optimization in multi-modal search spaces can then be viewed as a two-phase process. The first phase is exploration in which the most promising attraction basin is identified. The second phase is exploitation in which the best solution (i.e. the local optimum) within the previously identified attraction basin is attained. The goal of thresheld convergence is to improve the performance of search techniques during the first phase of exploration. The effectiveness of thresheld convergence has been demonstrated through applications to existing metaheuristics such as particle swarm optimization and differential evolution, and through the development of novel metaheuristics such as minimum population search and leaders and followers. [ABSTRACT FROM AUTHOR]