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In recent years, distributed optimization problem have a wide range of applications in various fields. This paper considers the prescribed-time distributed optimization problem with/without constraints. Firstly, we assume the state of each agent is constrained, and the prescribed-time distributed optimization algorithm with constraints is designed on the basis of gradient projection algorithm and consensus algorithm. Secondly, the constrained distributed optimization problem is transformed into the unconstrained distributed optimization problem, and according to the gradient descent algorithm and consensus algorithm, we also propose the prescribed-time distributed optimization algorithm without constraints. By designing the appropriate objective functions, we prove the multi-agent system can converge to the optimal solution within any prescribed-time, and the convergence time is fully independent of the initial conditions and system parameters. Finally, three simulation examples are provided to verify the validity of the designed algorithms. • For distributed optimization problem with convex constraints, we design an effective prescribed-time distributed algorithm which can guarantee the multi-agent system converge to the optimal solution. • The designed algorithm in this paper can achieve convergence within any prescribed-time, and the convergence time is fully independent of the initial conditions and system parameters. • In this paper, we not only consider the condition that the agents' states are constrained, but also ensure that the multi-agent system can reach the optimal solution within any prescribed-time, which is more practical. [ABSTRACT FROM AUTHOR]

The paper discusses the possibility of increasing the effectiveness of strategic management of research and development activities in higher education institutions. To achieve this goal, the authors proposed an algorithm for selecting competencies, which allows for the distribution of university employees and students among research and development teams in accordance with their individual competencies for carrying out research and development work. The paper covers the problem statement of competence selection, an overview of natural language processing methods for algorithm development, problematic areas of the algorithm, and prospects for its implementation in university management. [ABSTRACT FROM AUTHOR]

Honey badger algorithm (HBA) is a recent swarm-based metaheuristic algorithm that excels in simplicity and high exploitation capability. However, it suffers from some limitations including weak exploration capacity and an imbalance between exploration and exploitation. In this paper, an improved honey badger algorithm called ODEHBA is proposed to improve the performance of basic HBA. Firstly, an improved orthogonal opposition-based learning technique is employed to assist population in escaping local optimum. Secondly, differential evolution is utilized to ensure the enrichment of population diversity and to enhance convergence speed. Finally, the exploration capability of ODEHBA is boosted by an equilibrium pool strategy. To validate the efficacy of proposed ODEHBA, it is compared with 13 well-known metaheuristic algorithms on CEC2022 benchmark test sets. Friedman test and Wilcoxon rank-sum test are utilized to assess the performance of ODEHBA. Furthermore, three engineering design problems and Internet of Vehicles (IoV) routing problem are applied to validate the capability of ODEHBA. The simulation results demonstrate that ODEHBA excels in solving complex numerical problems, engineering design, and IoV routing problems. This holds significant practical implications for cost reduction and improved resource utilization. [ABSTRACT FROM AUTHOR]

Cognitive radio network (CRN) is getting growing interest of the researchers due to its wide applicability for spectrum sharing with massive number of active devices in Internet of Things (IoT). In existing schemes, a large part of the spectrum may remain underutilized which was assigned to service providers as primary users (PU). Secondary user (SU) may be the alternative to use the spectrum. The main challenge is that SU keeps on sending the packets in an incremental manner until a free channel is found in real conditions. It results in excessive communication and packet loss. This paper presents an efficient tree-centric approach where a centralized base-station maintains a tree of available channels and intelligently allocate to the SUs as per the real-time availability of channels. It analyzes for the effective usage of these channels by the SUs. It also manages the authenticated SUs to avoid any interference due to the hidden node problem. A number of extensive simulations are performed using network simulator. The existing instantaneous approaches require 10 to 12 attempts to send request for acquiring free channel with an average delay of 315 ms, the Hybrid-P1 and Hybrid-P2 approaches consumed 4 requests each with a delay of 128 ms to 135 ms. Our proposed CDC mechanism achieves a channel access mostly in 1 to 2 requests with an average delay of about 72 ms. Results are extracted from the trace files that prove the dominance of proposed scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of optimal preview pinning control for Boolean networks. The primary objective is to design efficient control strategies that leverage future reference information for improved control performance. We propose a policy iteration algorithm specifically for Boolean networks based on an augmented error system, constructed using the state augmentation technique in combination with the Exclusive-Or operator approach. The algorithm effectively optimizes control policies using future information. Finally, an example is presented to illustrate the effectiveness of the proposed algorithm. • First study of optimal preview pinning control problem of BNs. • Transformation using the semi-tensor product and exclusive-OR operator. • Deduction of a policy iteration algorithm for BNs. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) is considered as one of Maximum Power Point Tracking (MPPT) controller algorithm developed for PhotoVoltaic system (PV) to guarantee a maximum power extraction under different climatic conditions of temperature and irradiation. Classical MPPT algorithms like Perturbe and Observe (P&O), PSO, Adaptive Neuro-Fuzzy Inference System (ANFIS) are an effective method for tracking the maximum power point (MPP) for the PV systems, but the problems with these approaches that they are less stable, high oscillation around steady state and slower convergence to the MPP. Based on recent research, the purpose of this paper is to introduces a novel MPPT controller based on a modified version of heterogeneous multi swarm PSO algorithm using an adaptive factor selection strategy (FMSPSO), to overcome the previous shortcomings and compared with conventional PSO, ANFIS and classical P&O controllers. Simulation and experimental results revealed that the new FMSPSO algorithm can overcome the previous shortcomings providing the superior performance to track the MPP efficiently with a shorter convergence time and small oscillations compared to other algorithms. The experimental confirmation of the FMSPSO algorithm has been carried out using NI-myRIO-1900 card and shows that with the proposed MPPT approach efficiency can reach a value greater than 99% even in climatic variation of irradiation and temperature. • A classical MPPT controller cannot track the dynamic the MPP, has obvious oscillations, and converges slowly. • A novel MPPT controller based on a modified version of MSPSO algorithm using an adaptive factor selection strategy (FMSPSO) is proposed. • A FMSPSO based MPPT algorithm is compared with conventional PSO, classical P&O and ANFIS controllers for validation. • The proposed FMSPSO algorithm provides the superior performance by boosting the PV system tracking efficiency more than 99%. [ABSTRACT FROM AUTHOR]

Graphs are the most complex data structure that involves heavy mathematical computations. Various real-time applications use the graph at storage, processing, object characterization, and behavior specification stages. The connectivity analysis, coverage building, and routing are the critical applications of graph evaluation. Various associated applications need the exploration of a graph with a practical and real-time outcome. The processing of larger graphs is a big challenge for the researcher. In this paper, a hybrid graph-based computation optimization method is designed to optimize the performance of graph processing in multi-core processors. The BFS algorithm is applied over the multiple processors to optimize the functional response. The multithreaded-BFS is implemented to reduce the time of graph processing and computation. The proposed algorithm is implemented in the OpenMP environment. The experimentation is done on two-core and four-core processors. The experiment is conducted on a 100 to 1000-node network. The traversing and graph coloring algorithms are implemented for the analysis. The comparative analysis is conducted on single-core and multi-core processors with DFS and BFS-based algorithms. The analysis results identified that the proposed algorithm reduced the processing time effectively. [ABSTRACT FROM AUTHOR]

Access points (APs) are used to define coordinates in an indoor positioning system with Wi-Fi. These systems utilize existing infrastructure and Wi-Fi APs to find out the exact location of a device based on its RSSI and MAC address. The accuracy of these devices usually depends on the number of APs located nearby and the environment in which they are deployed. Therefore, the ideal selection of these points increases the discernibility of the localization technique. The rapid development of metaheuristic algorithms in recent years has demonstrated their effectiveness in resolving challenging optimization issues. The primary research goal is to investigate how to enhance indoor localization accuracy with metaheuristic algorithms and to assess the efficacy of positioning using these methods. In this paper, we propose a novel optimization algorithm called the Improved Pathfinder Algorithm (IPFA) using metaheuristic hybridization, where our contribution is twofold. The IPFA's superiority in optimization is used to choose the important APs. Subsequently, to maintain the generality of the localization performance, we created a feature-based classification model for the chosen AP subsets. Two prominent benchmark datasets, UJIIndoorLoc and JUIndoorLoc, were used to test the proposed framework. The proposed Indoor Localization framework attained an accuracy of 98.26% with a mean absolute error (MAE) of approximately 0.79 m. The results demonstrate that the IPFA method is capable of accurately locating the position with minimal positioning errors. [ABSTRACT FROM AUTHOR]

In recent times, the use of autonomous mobile robots (AMRs) has increased in many industries. As more AMRs roam the same area, traffic management becomes essential to prevent congestion and deadlocks. In related work, traffic management is often achieved using sophisticated, centralised planning approaches, albeit this often suffers from scalability issues. This paper therefore explores an approach where multi-AMR traffic management is achieved with layered costmaps and a modified Dijkstra's algorithm. This keeps the global path planner in the individual AMRs, thus not suffering scalability issues. To achieve multi-AMR coordination, traffic lanes and restricted areas are added to the AMRs' global costmaps. Furthermore, the AMRs also use a modified Dijkstra's algorithm that supports implementation of traffic directions. A proof-of-concept solution is implemented in Robot Operating System 2 with Nav2 and Gazebo. The implemented solution was tested against a standard solution without any traffic management in three scenarios designed to provoke collisions. The results indicate that the implemented solution can prevent a set of collisions better than one without traffic management. [ABSTRACT FROM AUTHOR]

Rough set is an important method for dealing with incomplete information systems. In incomplete information systems, the most common way to determine the relation between two samples is the tolerance relation. However, the condition for the tolerance relation to determine those samples may belong to the same category is very lenient, which makes the reduction rate low when using the rough set generated by this relation to select features. In response to the above problems, we design the neighborhood equivalence tolerance relation to solve them. Different from other improved tolerance relations, firstly, the relation designed in this paper does not require additional threshold to accomplish the above goals, which will avoid the trouble caused by the given threshold. Secondly, we notice that most of the current improvements for this kind of problems are computationally cumbersome, and the relation designed in this paper is simple and effective. Based on this, we construct a neighborhood rough set model that handles incomplete information by using this relation, introduce its properties, expound the properties that a reduction set should satisfy, quantify the importance of conditional attributes with attribute dependence degree, which provides the basis for the design of feature selection algorithm. Finally, the greedy strategy is used to design a forward feature selection algorithm. Experimental results show that the model is effective in dealing with incomplete information systems. The feature selection algorithm has the smallest size of the average reduced subset on twelve datasets, and maintains the accuracy of the classifier, which verifies that the feature selection algorithm can effectively deal with incomplete information systems. ● As the judgment of tolerance relation is very lenient, the existing improvement methods either rely on the probability distribution of attribute values in the information system, or need to give a additional threshold. To solve the above problems, this paper improves the tolerance relation, and the improved binary relation only relies on the existing information system itself, without giving additional threshold. ● Rough set in incomplete information systems can only select more attributes to refine the knowledge granules when making feature selection due to the lenient judgment of tolerance relation, which is equivalent to making up for the problems caused by tolerance relation when selecting attributes, but this will lead to low reduction rate. In this paper, we use the newly proposed binary relation to construct neighborhood rough set model in incomplete information systems, and use this model to make feature selection, which improves the problem of low reduction rate above, while maintaining the accuracy of the classifier. [ABSTRACT FROM AUTHOR]

Discrete orthogonal polynomials (DOPs) have gained significant research attention owing to their crucial role in digital signal processing applications such as computer vision, pattern recognition, and compression. However, the computation of DOP coefficients often incurs a substantial computational burden, exacerbating for higher-degree moments along with the resulting numerical errors. To address this challenge, this paper exploits the inherent parallelism in Charlier polynomial coefficient calculations to achieve enhanced polynomial performance. Independent calculations are distributed among threads, making efficient use of the available processing resources. Two algorithms are presented, the first algorithm evenly distributes the rows in a sequential manner (straightforward). Additionally, to achieve a more equitable distribution of coefficient calculations, this paper proposes alternative distribution approaches, aimed at balancing processing load among threads. Through extensive comparative experiments, we have confirmed that the proposed approaches achieved high performance across different degrees (1540 to 7370) and at different numbers of threads (2 to 256). The results show processing time in the multithreaded case is improved by up to 9.1 times with respect to the unthreaded case. Furthermore, by increasing the number of threads from 2 to 256, the trend indicates that the most significant improvement occurs in the range of 32 to 128 threads, confirming the robustness of the proposed algorithm. These findings signify the importance of this paper. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile communications, the dense deployment of small base stations (SBSs) in heterogeneous wireless networks (HW-Nets) has met the explosive growth in demand from terminals. However, due to the irregular deployment of SBSs, users frequently handover base stations in pursuit of high-quality services, which inevitably leads to load imbalance among SBSs. To alleviate load imbalance in HW-Nets and improve the efficiency of seamless handover, this paper proposes a distributed auction algorithm scheme based on dual active protocol stack (DAPS), which transforms the wireless access selection of terminals into the terminal-base station association process during movement to globally optimize network resource utilization. At the same time, this paper constructs a terminal-base station association graph and uses auction algorithms to solve the maximum weighted matching of the graph. The DAPS-based scheme can effectively avoid the problem of excessively long outage time caused by handovering outages. Simulation results show that compared with general distributed algorithms, the distributed auction algorithm based on DAPS can effectively balance the load of base stations and reduce the mobile outage latency by 59%. [ABSTRACT FROM AUTHOR]

• A model was created to examine the reliability of maintenance budget allocation. • The model was applied to network-level pavement maintenance in Canada. • The impacts of algorithm selection and parameter tuning were studied. • The significance of pavement clustering in increasing reliability was highlighted. • The importance of incorporating actual pavement improvement curves was clarified. The purpose of this paper is to increase the reliability of the network-level pavement maintenance budget allocation by reducing uncertainties of algorithm selection and parameter tuning. In this paper, reliability is defined as the ability of a network-level pavement maintenance plan to improve the condition of a pavement network within a certain budget. In order to quantify reliability, the reliability index is defined as the ratio between the post-maintenance network condition and the net maintenance cost. With this purpose in mind, a two-objective optimization model was developed. To test its applicability, the model was applied to a pavement network in Canada. The model was solved using the Non-Dominated Sorting Genetic Algorithm II and the differential evolution algorithms. Finally, the reliability indices of algorithms and termination criteria were computed. The results indicated that the differential evolution algorithm recommended less frequent but more intense interventions that made the solutions expensive and less reliable. This paper contributed to the network-level pavement maintenance body of knowledge by (1) developing a multi-objective optimization model to reduce uncertainties of parameter tuning and algorithm selection; (2) increasing the reliability of budget allocation; (3) showcasing the significance of pavement clustering in increasing reliability; and (4) developing and incorporating actual pavement improvement curves. [Display omitted] [ABSTRACT FROM AUTHOR]

Negative selection algorithms play an important role in anomaly detection. Interface detectors are a special negative selection algorithm that completely eliminates outer holes, but there are detection blind areas. This paper proposes a negative selection algorithm with hypercube interface detectors for anomaly detection. It uses self-sample clusters to construct self-space, and boundary self-sample clusters to describe the interface detectors. It eliminates the detection blind area and improves the detection rate. To validate the performance of the proposed method, experiments were conducted using the iris dataset, the skin segmentation dataset, the Breast Cancer of Wisconsin (BCW) dataset, and the Waveform Database Generator (Version 2) dataset. Experimental results show that the proposed method in this paper has a higher detection rate, lower false alarm rate, and fewer detectors than other anomaly detection methods for the same parameters. • HI-detector breaks away from the hyperspherical detector used by traditional negative selection algorithms. • HI-detector consists of a cluster of multiple hypercube self-samples. • HI-detector completely eliminates duplicate coverage of holes and detectors. • HI-detector has only one parameter that needs to be set. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new multichannel fast normalized least mean square (MC-FNLMS) algorithm for audio teleconferencing systems. The main idea behind the proposed MC-FNLMS algorithm is based on the introduction of a first order prediction process on the input noisy signals before the adaptive filtering process of each channel. The proposed algorithm is considered as an alternative solution for the existing algorithm in multi-channel applications. A full stability analysis of the proposed algorithm is derived along this paper. Simulation results of a comparison between the proposed MC-FNLMS algorithm and the two classical Multichannel Normalized Least Mean Square (MC-NLMS) and the Multichannel Affine Projection (MC-APA) algorithms, in terms of convergence speed, noise reduction and computational complexity, show the superiority of the proposed algorithm on multichannel adaptive noise reduction. [ABSTRACT FROM AUTHOR]

The consortium blockchain has been widely applied in various fields such as agricultural product traceability, supply chain management, and logistics transportation. As an indispensable component of a consortium blockchain, the consensus algorithm ensures the consistency and trustworthiness of each node in the network. However, existing consensus algorithms in large-scale consortium blockchain scenarios suffer from low system throughput and high latency due to the complexity of communication processes, rendering them impractical for real-world use. To address these issues, this paper proposes a novel consensus algorithm called credit evaluation-based practical Byzantine fault tolerance (CE-PBFT). This algorithm designs a new node credit evaluation model that considers node completion rate, consensus decay, and node behavior. It effectively measures and reflects the specific reliability status of nodes during system operation, thereby enhancing system reliability and security. Additionally, the paper introduces the innovative use of decision tree algorithms to analyze network node behavior and simplifies the existing consensus protocol. Nodes are categorized as excellent, good, ordinary, or poor based on the classification results, and non-Byzantine nodes are dynamically selected accordingly. This greatly improves the overall efficiency of the system. The performance of CE-PBFT is validated through experiments and compared with PBFT, G-PBFT, RBFT, WBFT and PPoR. Experimental results demonstrate that in large-scale consortium scenarios, CE-PBFT significantly improves system throughput, effectively reduces transaction latency and communication overhead, and outperforms the compared protocols. [ABSTRACT FROM AUTHOR]

The objective of image hiding is to conceal a secret image in a cover image inconspicuously. The challenges for image hiding focus on the embedding capacity, image quality and security. To contend with these challenges, this paper presents a novel image hiding method using Invertible Rescaling Net (IRN) and Similarity of Bits Pairs (SBP). Because of the downscaling ability of IRN, our method is able to hide a secret image into a cover image with the same dimensions. Moreover, the superior rescaling capabilities of IRN enable us to produce a recovered image of high-quality. We improve the SBP embedding technique and combine it with differential coding to boost the quality of the stego image. In the experiments, the quality of the recovered secret image and stego image is assessed using various quality evaluation metrics, and for the security, the anti-steganalysis performance is also evaluated in this paper. Extensive experimental results reveal that the quality of both the recovered secret image and the stego-image in our method outperforms several advanced methods and provides a high degree of security. • We apply invertible rescaling net (IRN) to image hiding task for the first time. • We improve the SBP encoding algorithm to prevent the degradation of the quality of the stego image. • Our method considers the robustness against the classical adversary's steganalysis. [ABSTRACT FROM AUTHOR]

The widespread development of monitoring devices in power system has indeed led to the generation of large amounts of power consumption data. Storing and transmitting this enormous volume of data has become a significant challenge for power system operators and researchers. Although high data rates are available for transmission, data compression is still necessary for power system applications to lessen the load of data transmission and storage. The wavelet transform plays an important role in data compression for power system applications. This paper presents an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. In general, some wavelet coefficients are neglected to produce DWT-based data compression by using hard or soft thresholds. But the volume of compressed data differs for various threshold values. In this context, selecting an optimal threshold is a challenging task in power system data compression. This paper proposes a multi-objective particle swarm optimisation (MO-PSO) for optimal threshold selection. The proposed approach is tested using IEEE power quality wave data sets and Dutch Residential Energy Datasets (DRED). The proposed MO-PSO algorithm finds the global optimum threshold, and it outperforms the other existing algorithms. The proposed algorithm efficiently compresses the test dataset with a maximum compression ratio (CR) of 2.07. • The development of an adaptive algorithm for data compression is applied in power system data. • MO-PSO algorithm is proposed to find the optimal threshold in WT based data compression. • Results are compared with recent research works. • The efficiency of data compression algorithm is evaluated by using compression ratio, mean square error and signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Economic dispatch (ED) has received considerable interest in the field of energy management and optimization. The problem aims to determine the most cost-effective power allocation strategy that satisfies the power demand and all physical constraints of the power system. To solve this problem, we propose an algorithm based on differential evolution and adopt a hybrid mutation strategy, a linear population size reduction mechanism, and an improved single-unit repair mechanism. Experimental results confirmed that these mechanisms are useful for performance improvement. The proposed algorithm (L -HMDE) showed good performance when compared with more than 90 algorithms in solving 22 test cases. It could provide high-quality solutions stably and efficiently. In addition to designing a good algorithm, we present a review of over 100 papers and highlight their algorithm features. We also provide a comprehensive collection of test cases in the literature. Through careful examination and verification, data coefficients of these test cases and solutions to them are included in this paper as a useful reference for researchers who are interested in this problem. • A differential evolution-based algorithm (L-HMDE) is proposed to address the economic dispatch problem. • The L-HMDE integrates a hybrid mutation strategy, a population size reduction mechanism, and an improved repair procedure. • It shows good solution quality and high efficiency when compared with more than 90 existing algorithms on 22 test cases. • A comprehensive collection of test cases and solutions is also provided. [ABSTRACT FROM AUTHOR]

The field of machine learning (ML) has witnessed significant advancements in recent years. However, many existing algorithms lack interpretability and struggle with high-dimensional and imbalanced data. This paper proposes SPINEX, a novel similarity-based interpretable neighbor exploration algorithm designed to address these limitations. This algorithm combines ensemble learning and feature interaction analysis to achieve accurate predictions and meaningful insights by quantifying each feature's contribution to predictions and identifying interactions between features, thereby enhancing the interpretability of the algorithm. To evaluate the performance of SPINEX, extensive experiments on 59 synthetic and real datasets were conducted for both regression and classification tasks. The results demonstrate that SPINEX achieves comparative performance and, in some scenarios, may outperform commonly adopted ML algorithms. The same findings demonstrate the effectiveness and competitiveness of SPINEX, making it a promising approach for various real-world applications. • SPINEX: An interpretable neighbor exploration algorithm is presented. • Combines ensemble learning and feature interaction for meaningful insights. • Quantifies feature contributions and interactions to enhance interpretability. • 59 experiments were conducted to validate SPINEX's comparative performance. [ABSTRACT FROM AUTHOR]

An intelligent detection of the communication channel by the transceiver is what is known as cognitive radio (CR), a sort of radio communication. Avoid using channels that are already in use to reduce interference and swiftly direct traffic to open channels. In cognitive wireless networks, spectrum allocation has long been a research hotspot as one of the key problems to increase spectrum efficiency. This article examines the issue of spectrum sharing, in which one primary user (PU) might rent out portions of their spectrum to numerous secondary users (SU) in order to share it. The suggested Fuzzy Ant Colony Optimization based Multiple Scheduling Resource Selection Algorithm (FAMSRSA) chooses the best network while maximising data rate and minimising interference and cost. Here, Nash's balanced multi-antenna-based spectrum sharing model (NE-MASS) is used to optimize and share the spectrum. In this paper, a mathematical model optimizes multiple network selection goals to maximize the overall bandwidth, and the total cost and idle spectrum assignment are determined to a minimum in the context of a comprehensive consideration of differences in the spectral resource characteristics of primary networks that differ in network domain and diversify the user's secondary requirements and analyze the complexity of the problem in the user domain. This approach provides an efficient way to access the spectrum available to both primary and secondary users. As a result of the experiment, this algorithm shows the algorithm's efficiency better than the four conventional algorithms in terms of performance metrics like Maximized Minimum Bandwidth, Maximized Network Efficiency, Maximized Proportion Fairness, Relative error and Spectrum Utilization Efficiency. The performance of the NE spectrum allocation algorithm was evaluated by extensive simulation. The proposed spectrum allocation algorithm has shown superior channel allocation performance than other conventional literature algorithms. [ABSTRACT FROM AUTHOR]

Pattern-based building envelope is a modern style of contemporary facades. Architects may rely on imitating known practices without knowing the processes involved in their design. Therefore, this paper seeks to identify the algorithmic steps implemented in the derivation of contemporary façade. The design algorithms of pattern-based building envelopes are classified into generative algorithms of the basic pattern and elements, and transformation algorithms. An experimentation method was applied using Grasshopper for Rhino to simulate the formation of nine contemporary pattern-based envelopes. The finding reveals that several approaches are implemented such as elements are extruded from the basic pattern or separated from it. The envelope pattern may result from repeting the elements, or the envelope pattern may be devoid of elements. Modification techniques can manipulate the basic pattern, the elements, or both patterns and elements. They act on the patterns and elements simultaneously in the same way, or separately using different ways. [ABSTRACT FROM AUTHOR]

Detecting the distribution and density of marine zoobenthos is crucial for monitoring healthy coastal ecosystems and for growth reference tracking in precision aquaculture. However, current detection algorithms for marine zoobenthos have high computational complexity and cannot guarantee a balance between accuracy and speed, limiting their deployment in fishery equipment. This study used a portion of the Augmented Underwater Detection Dataset, a large underwater biological dataset containing marine zoobenthos data. A marine zoobenthos recognition algorithm was proposed for sea cucumbers, sea urchins, and scallops based on an improved lightweight YOLOv5, which can recognize the three types of marine zoobenthos. In the image enhancement module, an underwater image enhancement algorithm based on color balance and multi-input fusion is used, which turns the blurred image into a natural appearance of the seabed image. The lightweight backbone network EfficientnetV2-S was chosen to replace the original YOLOv5 backbone network, reducing network parameter calculations and improving recognition speed. A Bottleneck Transformer was introduced into the backbone network, and an attention mechanism based on the convolution module was introduced to construct the embedded Convolutional Block Attention Module in the Neck structure of YOLOv5, thereby improving the recognition accuracy of the lightweight YOLOv5 model. The experimental results showed that the mAP of the proposed algorithm reached 0.941, which is an improvement of 0.002 compared with the original YOLOv5l algorithm. The computation of this algorithm is 37.0 FLOPs (G), the model size is 54 MB, and the inference time is 5.9 ms. Compared to the original YOLOv5l algorithm, the reductions are 66.1%, 40.5%, and 39.2%. The proposed algorithm efficiently identified and classified marine zoobenthos. • The paper proposes a new high-precision method for marine zoobenthos detection. • A lightweight high-precision Backbone fusing EfficientnetV2 and BotNet is proposed. • Introducing an underwater image enhancement module to improve underwater image quality. • Introducing CBAM in the Neck structure enhances the feature fusion capability. [ABSTRACT FROM AUTHOR]

In Vehicular ad hoc network (VANETs), Quality of Service (QoS)- aware protocols helps in handling the necessitated demand of delay sensitive applications for facilitating intelligent transportation. The fundamental challenge of VANETs lies in the process of establishing vehicle to infrastructure and vehicle-to-vehicle communication that are prone to link failure. Bio-inspired algorithms are identified to provide reliable solutions for securing the links of VANETs. In this paper, Komodo Mlipir Algorithm-based Optimal Route Determination Mechanism (KMAORDM) is proposed for achieving best optimal route that guarantees enhanced QoS. This QoS aware routing protocol utilizes the exceptional potentiality of Komodo Mlipir Algorithm attributed in terms of exploration and exploitation for the objective of evaluating and memorizing the impactful factors that helps in exchanging the necessitated messages to its neighbours. It adopts Mlipir as a best mode of modelling the behaviours of vehicular nodes during the process of data routing in VANETs. It uses a cache strategy which discovered reliable traversed routing paths such that the pre-cached route is used for data transmission. This cache strategy paves the way for preventing the route identification and maintenance process with minimized routing overhead. It also identifies the fittest vehicular node from its one hop distance as the successive forwarder in the absence of pre-cached route for addressing link failure during reliable packet transmission. The simulation results of this proposed KMAORDM approach with different vehicular nodes confirmed minimized communication overhead of 19.32 %, reduced network latency of 18.64 %, maximized throughput of 18.54 % better than benchmarked approaches. • Exploration and exploitation of Komodo Mlipir-Based Route-Finding Algorithm determines ideal route between vehicles. • Urban VANET communication overhead is reduced by cache memory-assisted RSUs' reliable backhaul link with neighbouring RSUs. • KMRFA algorithm balances exploration and exploitation, so nearby RSUs use past route info if vehicle clusters move uniformly. • Limiting control packet broadcast reduces discovery time and link failure when previous route is not in RSU cache memory. [ABSTRACT FROM AUTHOR]

Due to the complex environment and variable demands, unmanned surface vessel (USV) task assignment and path planning have received much attention from academia and industry in recent years. Artificial intelligence technologies are increasingly adopted for solving the USV task assignment and path planning problems. This paper aims to give a comprehensive literature review of achievements, trends and challenges about the USV tasks assignment and path planning. First, the concerned problems are divided into two categories (single-USV and multi-USV), and the related work is thoroughly reviewed. Second, the solution methods, especially artificial intelligence (AI) related technologies and approaches, are analyzed and discussed. Third, the common external constraints and disturbances in the maritime environment are described. In particular, the obstacle avoidance strategies and algorithms are discussed and summarized systematically. Next, the applications to scientific exploration , military, and fishery industry are presented. Finally, we conclude this survey and indicate future research directions. [ABSTRACT FROM AUTHOR]

Objective reduction is an efficient method to simplify many-objective optimization problems (MaOPs) with redundant objectives. However, most objective reduction algorithms operate on an entire sample set, which would easily omit local features and lead to an over-reduction of objectives. To alleviate the above problems, this paper proposes an objective reduction algorithm based on population decomposition and hyperplane approximation, denoted as PDHA, where the population is decomposed into several subpopulations, and a method based on hyperplane approximation is applied to extract the essential objectives from subpopulations. PDHA has two advantages. First, extracting essential objectives from the subpopulations could reduce errors produced by the reduction technique. Second, more attention is paid to local features via extracting the essential objectives from different subpopulations, which could prevent an over-reduction of objectives. The performance of PDHA is theoretically verified and experimentally compared with some state-of-the-art objective reduction algorithms and some algorithms for MaOPs on some benchmark problems. The experimental results show that PDHA is effective for the objective reduction of objective-redundant MaOPs. • Propose an objective reduction algorithm based on population decomposition and multi-hyperplane approximation, i.e., PDHA. • Effectively solve objective-redundant MaOPs with nonlinear PFs through multi-hyperplane approximation. • Effectively avoid over-reduction with conflict information detected from subpopulations. • Theoretically and experimentally verify the performance of PDHA for objective reduction. [ABSTRACT FROM AUTHOR]

In this paper, we investigate performance of a random access scheme that exploits binary-tree algorithm (BTA) with the free access. We assume a scenario where the receiver is capable to perform both multi-packet reception (MPR) and successive interference cancellation (SIC), where for the purpose of the latter only the last received and undecoded signal can be stored. We distinguish between two variants of the algorithm, where in the first the SIC can be triggered by a decoding event but also executed blindly among yet undecoded slots, while in the second the receiver can only execute the SIC after the decoding event. We analytically derive the maximum stable throughput (MST) of the scheme assuming Poisson arrivals. The evaluation shows that the scheme is able of achieving a favorable performance in comparison to the scenarios when only either MPR or SIC with single-slot memory is used, making it a suitable candidate for an access solution in applications that are characterized with a massive number of users and sporadic traffic arrivals. We also compare the performance of the scheme with the best performing BTA scheme that also exploits K -MPR and SIC and does not have memory limitations, showing that the relative difference in the MST's of the two schemes diminishes with K. [ABSTRACT FROM AUTHOR]

Edge computing is a key technology in computational imaging, where the algorithms determine reconstruction quality and reconstruction speed. Recovering spectral information from ill-conditioned data of coded aperture snapshots is a novel but challenging solution in spectral imaging for dynamic scenes. Further study is necessary to enhance the edge computing power of computational spectral programs. Deep unfolding networks have made considerable headway in this direction, but the existing methods still have two drawbacks: (1) The 3D spectral cube exhibits long-range dependency and non-local self-similarity in both spatial and spectral dimensions, yet learn the global features of spatial and spectral is difficult, which remains to be investigated. (2) The global features degenerate as the number of iterations increases. To solve the above problems, this paper proposes a Momentum Accelerated Unfolding Network with Spectral–Spatial prior (MAUNSS). Specifically, a denoising module based on a spectral–spatial transformer is designed, which uses both sparse spectral features to recover global information and dense spatial features to enhance detail texture. Within such a framework, cross-stage feature transmission channels transmitting features across spectral and spatial transformers at different stages are built to avoid feature degeneration. In order to increase the training speed and save the time and cost associated with the iterative approach, we improve it by means of the momentum acceleration module. Supplemented by a re-projection loss, a technique introduced from 3D measurement, the accuracy is further improved. To the best of our knowledge, the proposed method is an attempt to use momentum to expedite the convergence of unfolding networks. Extensive experiments demonstrate that our proposed method outperforms previous state-of-the-art methods by more than 1.94 dB. • MAUN with learnable hyper-parameters and Momentum methods ensures better accuracy. • A reprojection loss is proposed to improve the reconstruction accuracy of HIS. • MAUNSS exceeds the previous SOTA methods while requiring less memory costs. [ABSTRACT FROM AUTHOR]

The pSCAN algorithm is widely acknowledged for its efficiency in structural graph clustering across diverse graph applications, frequently utilized to detect significant clusters within graphs. The results of pSCAN are constrained by two parameters: (1) the structural similarity constraint ϵ ; (2) the density constraint μ. Nevertheless, determining appropriate values for these parameters proves challenging as users often lack the requisite professional knowledge. Consequently, users may inquire about the inclusion of unexpected vertices in specified clusters, posing w h y questions. In this paper, we aim to address these inquiries by investigating the challenge of answering why questions on structural graph clustering. The objective is to refine the initialized clustering parameters to exclude unexpected vertices from specified clusters. Initially, we introduce the crucial concept of l a b e l - v e r t i c e s and delve into a unified explanation framework designed for addressing why questions on structural graph clustering. Subsequently, we propose two effective refining algorithms, namely FReEPS and FReMU , specifically tailored to modify the similarity constraint ϵ and the density constraint μ independently. Moreover, in our approach to enhancing efficiency, we introduce two baseline explanation algorithms, ReParFirstMU and ReParFirstEPS , simultaneously refining the parameters ϵ and μ to exclude unexpected vertices from specified clusters. Additionally, we explore two improved algorithms, FReParFirstMU and FReParFirstEPS , incorporating a process of pruning unnecessary parameter combinations and computations. This enhancement aims to improve the efficiency of simultaneously refining the two parameters for addressing these questions. Finally, we conducted comprehensive experiments on real social network datasets. The results illustrate that the explanation efficiency of FReEPS and FReMU surpasses that of the state-of-the-art explanation algorithms by 1-2 orders of magnitude. Moreover, FReMu exhibits faster performance than FReEPS. Furthermore, simultaneous refinement of the parameters ϵ and μ yields better-refined parameters. Additionally, FReParFirstMU and FReParFirstEPS outperform ReParFirstMU and ReParFirstEPS, respectively. • A new framework for answering why questions on structural graph clustering. • An effective answering algorithm by refining the parameter ϵ. • An effective answering algorithm by refining the parameter μ. • Two effective answering algorithms by refining parameters ϵ and μ simultaneously. • Extensive experiments are conducted on three real social network datasets. [ABSTRACT FROM AUTHOR]

Most image inpainting algorithms have problems such as fuzzy images, texture distortion and semantic inaccuracy, and the image inpainting effect is limited when processing photos with large missing sections and resolution levels. The paper proposes an effective image inpainting algorithm via partial multi-scale channel attention mechanism and deep neural networks to address the above phenomenon that existing image inpainting methods using deep learning modules have insufficient perception and representation capabilities for multi-scale features with high proportion of irregular defects. Initially, we used the Res-U-Net module as a generator. The U-Net-like backbone network topology can achieve the encoding and decoding stages of damaged images. Secondly, the residual network structure was built in the encoder and decoder to improve the ability of the proposed network to extract and display the features of the damaged images. Finally, the partial multi-scale channel attention module was inserted in the skip connection with the decoder to increase the efficiency of using the low-level features of the original images. The experimental results of the research can show that the proposed method outperforms state-of-the-art methods in terms of subjective visual perception and objective evaluation indicators on the CelebA , Places2 and Paris Street View datasets. • The model has improved the ability to extract and express multi-scale features. • The method had included partial multi-scale channel attention and residual network. • The backbone network structure can realize encoding and decoding stage. • The structure of residual networks had constructed to enhance the abilities. [ABSTRACT FROM AUTHOR]

Efficient space delay-tolerant network (DTN) routing algorithms have been a hot issue in space network-related research. In this paper, for the problems of high algorithm complexity and high network overhead in space DTN routing algorithms, we introduce a node clustering algorithm and satellite node social attributes in the space DTN routing process and design a spatial delay-tolerant network routing algorithm (CD-SDTN) based on node clustering and social attributes. The algorithm designs the social attributes of the satellite nodes based on the connection frequency of the satellite nodes with the ground station and divides the community using the Circle-k-means algorithm. In the routing process, the center satellite node with the highest activity in the community manages the messages received by the satellite nodes located in the community and plans the forwarding of messages within and between communities. Simulation results show that the CD-SDTN routing algorithm has some advantages in terms of delivery rate, average delay and network overhead compared with Epidemic and Prophet, especially when the number of nodes in the satellite network is large. • CD-SDTN combines satellite node social attributes into routing algorithm. • CD-SDTN uses an improved circle-k-means clustering algorithm. • CD-SDTN proposes a novel definition of social attributes for satellite nodes. • CD-SDTN was significantly more effective compared to Epidemic and Prophet. [ABSTRACT FROM AUTHOR]

Most studies on distributed assembly permutation flowshop scheduling do not consider product priorities and factory heterogeneity. This causes delays in critical products and cannot reflect the real-world production situation. This paper focuses on the energy-efficient heterogeneous distributed assembly permutation flowshop scheduling considering priorities (EHDAPFS-P) to minimize total tardiness and total energy consumption simultaneously. Unlike traditional models, factory heterogeneity and product priorities are considered to better reflect the production environment and customer satisfaction in real-world situations. Then, a Q-learning memetic algorithm (QLMA) is proposed to solve this problem: (i) a high-quality initial population is obtained using a hybrid initialization strategy that combines four problem-specific heuristics; (ii) six efficient neighborhood structures are tailored to guide the population to converge to the promising areas; (iii) the most useful neighborhood structure is selected among the six structures using the Q-learning algorithm to accelerate the convergence, thus maximizing the cumulative and future improvements according to the population state; and (iv) an energy-saving strategy is developed to optimize the total energy consumption without deteriorating the total tardiness. The proposed QLMA is compared with seven state-of-the-art algorithms on 261 benchmark instances to demonstrate its superiority or at least competitiveness. • Factory heterogeneity and product priorities are considered in DAPFSP. • Four problem-specific heuristics are devised to generate the initial population. • Six efficient neighborhood structures are designed. • The best neighborhood structure is determined using the Q-learning algorithm. • An effective energy-saving strategy is developed to reduce TEC. [ABSTRACT FROM AUTHOR]

Many real-world problems can be abstracted as multimodal global optimization, which is one of the main challenges for optimization algorithms due to its complexity. The fireworks algorithm (FWA) is a swarm intelligence optimization algorithm that has been widely studied and applied by virtue of the synergistic property among fireworks. Current FWA variants have poor exploitation capability to handle some locally complex multimodal functions, which greatly limits the application of the FWA to practical problems. To solve the above problems, in this paper, we propose the multi-guiding spark fireworks algorithm (MGFWA) to solve multimodal functions by enhancing FWA exploitation capabilities. Three different strategies which are boosted guiding vector, multi-guiding sparks, and population-based random mapping are designed to boost the guiding vector, enrich the guiding spark diversity, and fix the mapping function, separately. The validity and parameter setting of MGFWA are theoretically analyzed. Experimentally, the results on the CEC2013 and CEC2017 single objective optimization benchmarks illustrate the remarkable performance of the MGFWA compared to other typical optimization algorithms and FWA variants. Moreover, the ablation study shows each of the three parts plays an important role in the algorithm and the efficiency experiment shows the MGFWA can improve the efficiency of guiding sparks by 10%. We believe that the MGFWA can be considered the SOTA variant of the FWA. • Utilizing boosted guiding vector, multi-guiding sparks, and population-based random mapping to enhance the exploitation capability of the fireworks algorithm. • State-of-the-art performance among fireworks algorithm variants. • Superior performance on multimodal global optimization among several typical swarm intelligence optimization algorithms and evolutionary algorithms. • Extensive experiments (about 60 test functions) and theoretical analyses to demonstrate the effectiveness. [ABSTRACT FROM AUTHOR]

• An efficient new AVMD algorithm for the extraction of adventitious lung sounds is proposed. • During iteration, the proposed algorithm adaptively updates the penalty factor to reduce frequency aliasing between modes. • The proposed algorithm adopts a strategy of gradually separating each mode to determine the optimal number of modes. • The proposed algorithm uses Pool Adjacent Violators to automatically initialize the modes' center frequency to reduce iteration steps. The rapid and accurate extraction of adventitious lung sounds (ALSs) from original lung sounds is greatly significant for diagnosing lung diseases and assessing lung condition. Due to interference from heart sounds and various noises, the empirical mode decomposition (EMD), variational mode decomposition (VMD) and some other classical algorithms have poor performance. Here, an efficient new adaptive variational mode decomposition (AVMD) algorithm is proposed to improve the efficiency for extracting ALSs. For minimizing the bandwidth of each extracted mode and reducing frequency aliasing between modes, the proposed algorithm constructs a frequency-constrained model based on VMD. In order to further improve its performance, the algorithm adopts a strategy of gradually extracting each mode to determine the optimal number of modes for avoiding the under-decomposition or over-decomposition, and uses Pool Adjacent Violators (PAV) to automatically initialize the center frequency of each mode to reduce algorithm iteration steps. Based on the above improved characteristics, the experiments presented in this paper, which conducted on the artificial simulation signal and 102 groups of lung sounds from 20 patients with severe pulmonary diseases, highlight the proposed AVMD's superiority over the other five algorithms when comprehensively considering both decomposition quality and computational efficiency. Furthermore, the above results of 102 groups of lung sounds are statistically analyzed to obtain the characteristic frequency distribution of ALSs, which can provide important references for clinicians and demonstrate the potential application of the proposed algorithm in the diagnosis of pulmonary diseases. [ABSTRACT FROM AUTHOR]

As a new network architecture, the air-ground cooperative network is a support for future 6G network to achieve ubiquitous connectivities. To effectively relieve the computational pressure of massive data in 6G wireless networks, Unmanned Aerial Vehicles (UAVs) equipped with Mobile Edge Computing (MEC) servers have become an emerging technology that provides computing resources for Mobile Devices (MDs). Due to limited on-board energy and computational capabilities, this paper investigates a multi-UAV collaborative assisted MEC architecture. The optimization problem of minimizing the total computational cost is constructed by jointly optimizing the UAVs trajectories and MDs offloading strategies scheduling. The coupling between the optimization variables and the non-convexity of the problem can make it difficult to solve directly. To address the above concerns, the non-convex optimization problem is converted into a Markov decision process. The UAVs-assisted Offloading Strategy based on Reinforcement Learning (UOS-RL) algorithm is proposed to address the convergence difficulties caused by the high-dimensional continuous action space. Furthermore, the experience data generated by agents interacting with the environment is highly differentiated due to the highly dynamic variation of the environment. Hence, a Priority Experience Replay (PER) mechanism is proposed to improve the training efficiency of the UOS-RL algorithm based on the priority of the experience data. Simulation results show that the proposed PER-UOS-RL algorithm outperforms the existing works in terms of the computational cost. [ABSTRACT FROM AUTHOR]

Clustering has received extensive attention in recent years as an important approach to address the poor scalability in flying ad hoc networks (FANETs). However, due to the highly mobile nature of FANETs, frequent changes in cluster topology result in increased control overhead and reduced communication efficiency. To tackle these challenges, a novel heuristic clustering algorithm called the physarum-inspired clustering algorithm (PICA) is proposed in this paper. It leverages the intelligence and adaptability of Physarum polycephalum. The PICA adopts a distributed multi-hop clustering approach. During the cluster formation phase, and inspired by the foraging behavior of Physarum polycephalum , the algorithm incorporates link stability, residual energy, and link communication quality as evaluation metrics, thereby ensuring cluster coverage and stability. By selecting the most stable node within an n-hop range as the cluster head, the cluster stability is significantly enhanced. In the cluster maintenance phase, the PICA improves the backup cluster head selection mechanism and introduces damage detection and cluster merging mechanisms to further enhance the robustness and stability of clusters. Experimental results demonstrate that compared to existing EMASS and OSCA algorithms, the proposed PICA reduces the average end-to-end delay by 31.6 % and 25.27 %, improves the average packet delivery ratio by 27.7 % and 19.12 %, and decreases the average inter-cluster switching times by 14.71 % and 14.38 %, respectively. In conclusion, the PICA effectively addresses the issue of poor scalability in FANETs by leveraging the intelligence of multi-headed myxobacteria and adopting a multi-hop clustering approach. As indicated by experimental results, the proposed algorithm demonstrates significant performance improvements in terms of network metrics. [ABSTRACT FROM AUTHOR]

In this study, a novel meta-heuristic search (MHS) algorithm for constrained global optimization problems is proposed. Since many algorithms aim to achieve well-balanced exploitation–exploration stages with often unsatisfactory results, in the approach introduced in this paper, Attraction–Repulsion Optimization Algorithm (AROA), the balance associated with attraction–repulsion phenomena that occur in nature is mimicked. AROA introduces a search strategy in which a candidate solution is moved in the search space depending on the quality of solutions in its neighborhood, as well as the best candidate. The candidates are managed by local search operators based on modified Brownian motion, trigonometric functions, randomly selected solutions, and a form of memory. Consequently, AROA exhibits a satisfactory exploitation–exploration balance exhibited by highly competitive performance. The introduced algorithm is experimentally compared with the state-of-the-art meta-heuristics on the CEC 2014, 2017, and 2020 test suites. The obtained results reveal the advantages of AROA over related algorithms and its suitability in solving complex real-world problems. • A novel attraction–repulsion search scheme is introduced. • Proposed meta-heuristic is governed by balanced search operators. • Algorithm is validated on three demanding CEC benchmarks. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel adaptive differential evolution algorithm for global optimization problems by introducing an enhanced mutation operator and a new mixed control parameter setting based on dual performance evaluation metrics. To further strengthen the search efficiency of algorithm and availably balance its exploration and exploitation, a dual performance metrics-based mutation operator is first proposed to implement the search of population by organically integrating the fitness value and history update of individual to find the potential promising areas and allocate the suitable search resources for them. Meanwhile, a dual performance metrics-based mixed parameter setting is developed to yield appropriate associated parameters for each individual by comprehensively measuring its search characteristic and requirement based on both its fitness value and history update. In addition, a new restart strategy is further put forward to boost the search performance of algorithm by reasonably replacing the meaningless individuals measured by both their fitness values and history updates with the randomly generated individuals based on Gaussian walk. In contrast to the existing DE versions, the new algorithm organically takes advantage of the fitness value and history update of individual to assign the proper computational resources for each potential promising region, create the suitable parameters for different individuals, and eliminate the valueless individuals from population. Thereby, it is capable of heightening the search efficiency of algorithm and maintaining a good balance between exploration and exploitation effectively. At last, the performance of the proposed algorithm is evaluated by comparing with 19 typical or up-to-date algorithms on 42 benchmark functions from both IEEE CEC2017 and CEC2022 test suites. Compared to these opponents, the proposed algorithm achieves significantly better performance on 60 out of 77 cases based on the multiproblem Wilcoxon signed-rank test at a significant level of 0.05, and thus is a more promising optimizer. [ABSTRACT FROM AUTHOR]

Decomposition-based multiobjective evolutionary algorithms are widely utilized for solving multiobjective optimization problems (MOPs). These algorithms have a higher degree of diversity due to a uniform distribution of the reference vectors in the objective space. However, these algorithms may struggle to select a suitable candidate set for solving many-objective optimization problems (MaOPs) with irregular Pareto fronts (PFs). To solve this problem, a solution potential-based adaptation reference vector evolutionary algorithm (SPARVEA) is proposed in this paper. In the algorithm, a concept called solution potential is presented to assess whether the direction of convergence of the solution to the ideal solution has potential. The solution potential is obtained with an evaluation function to calculate the potentials of the corresponding solutions. A solution potential-based adaptation strategy is then designed for adapting the reference vector to better guide the direction of convergence of the solution when solving MaOPs with irregular PFs. A modified angle penalty distance (mAPD) is adjusted to improve the convergence rate of the solution set in many-objective optimization problems. The proposed algorithm SPARVEA is compared with state-of-the-art many-objective evolutionary algorithms on four test sets with irregular PFs and four practical problems. Experimental results demonstrate the superior performance of SPARVEA on many-objective optimization problems. This contribution helps to advance the development of efficient algorithms for solving many-objective optimization problems. • SPARVEA is proposed for solving many-objective optimization problems with irregular Pareto fronts. • A concept of solution potential is evaluated to assess the direction of the solution to the ideal point. • A solution potential-based adaptation strategy is presented to adjust the reference vectors to guide the direction of convergence of the solution. • A modified angle penalty distance is adapted to control the convergence rate of the population. • The SPARVEA outperforms other contrasting state-of-the-art many-objective optimization algorithms. [ABSTRACT FROM AUTHOR]

For real-world design optimization, there may often exist multiple candidate concepts that may solve the problem at hand. The process of concurrently identifying the best concept and the corresponding variable values that optimize the design objective(s) is termed as a multi-concept optimization (MCO). While such problems are commonly encountered in practical domains such as engineering, transport, product design, there has been little focus on developing computationally efficient algorithms for MCO. One of the reasons contributing to this gap is the scarcity of benchmark problems with diverse challenges that could be used as a testbed for the development and systematic evaluation of advanced MCO algorithms. In this paper, we particularly focus on MCO problems with multiple conflicting objectives. We review the existing multi-objective MCO test problems and identify their shortcomings through preliminary numerical experiments. Then, we propose a methodology and provide a test problem generator for systematically constructing new multi-objective MCO instances with desired properties. We also propose 28 specific test problem instances using this generator to build a benchmark suite that poses a wide range of challenges to the prospective search strategies. Further, we conduct numerical experiments on the proposed test suite using multiple existing algorithmic strategies from the literature to demonstrate their performance. The results clearly highlight the challenges faced by the strategies for different types of problems. These observations emphasize the need for research efforts towards the development of more efficient and versatile MCO algorithms to tackle a wider range of MCO problems. [ABSTRACT FROM AUTHOR]

Many engineering optimization problems fall into the category of Mixed-Integer Nonlinear Programming (MINLP) problems, which combine nonlinear relations, constraint conditions, and different types of variables, including continuous, integer, and/or discrete variables. Solving MINLP problems can be a challenging exploration process since their landscape can be composed of many discontinuous feasible parts with different sizes. In this scheme, Evolutionary Algorithms (EAs) often fail to generate enough diversity to explore the discontinuous feasible parts. Consequently, EAs are vulnerable to being attracted to larger discontinuous feasible parts, even if they are not promising regions. In this paper, a new proposal based on two fundamental strategies is presented to improve the performance of the differential evolution algorithm when solving MINLP problems. The first strategy considers a set of "good fitness-infeasible solutions" that contribute to exploring promising regions from infeasible contours. It reduces the vulnerability of the solutions to be attracted to larger discontinuous feasible parts with unpromising objective function values. The second is a composite trial vector generation to improve the combinatorial exploration while ensuring a robust convergence capacity toward the final solution. Sixteen well-known MINLP problems are used in several experiments to evaluate the performance of the proposed algorithm, comparing it to state-of-the-art EAs. The results provided by the proposal show a better performance in terms of quality, robustness, and computational cost. • Solving mixed-integer nonlinear programming problems can be challenging. • Improved differential evolution for mixed-integer nonlinear programming problems. • Good fitness-infeasible solutions contribute to exploring the smaller feasible regions. [ABSTRACT FROM AUTHOR]

Development of 5G/F5G technology leads to massive applications accessing to backbone networks, which requires the backbone networks to be upgraded. Semi-filterless elastic optical network (semi-FEON) is a suitable technology to cheaply and gradually upgrade backbone networks. In semi-FEON, routing, modulation and spectrum assignment (RMSA) problem is one of the key issues. In this paper, we study the dynamic RMSA problem in semi-FEON and propose an RMSA algorithm. The algorithm includes three innovations: a K-shortest-subnet-paths (KSSP) algorithm is designed to search candidate paths in semi-FEON, a load-balancing-least-resources (LBLR) policy is introduced to re-sort the candidate paths, and a maximum-occupied-neighbors (MON) rule is proposed to assign spectrum resources to connection requests in semi-FEON. Simulation results show that the proposed KSSP-LBLR-MON algorithm outperforms the existing works in term of bandwidth blocking probability. Concretely, the improvement ratio is greater than 59.98% and 66.64% in German-Net and Henan-Net, respectively. [ABSTRACT FROM AUTHOR]

Space-division multiplexed elastic optical networks (SDM-EONs) utilizing multi-core fiber (MCF) have been considered to address the growing traffic demand in transport networks. The quality of transmission (QoT) of MCF-based SDM-EONs is affected by inter-core and intra-core physical layer impairments (PLIs). This paper proposes an inter-core crosstalk-aware and intra-core impairment-aware algorithm for modulation, core, and spectrum assignment (CIA-MCSA) in MCF-based SDM-EONs. The CIA-MCSA considers PLI estimation in a dynamic traffic scenario and allocates new lightpaths using strategies to avoid blocking by insufficient QoT of the new lightpath and of already active lightpaths. Using numerical simulation, the performance of the CIA-MCSA is compared with five algorithms proposed by other authors, considering two distinct network topologies, heterogeneous traffic demands, and different levels of inter-core crosstalk. The results show that, when compared with the most competitive of the other algorithms, (i) CIA-MCSA achieves an average reduction of the request blocking probability by at least 33.87%; (i i) CIA-MCSA achieves an average reduction of the bandwidth blocking probability by at least 20.74%; and (i i i) CIA-MCSA increases the network spectrum utilization by at least 3.04%. [ABSTRACT FROM AUTHOR]

Nash equilibrium is one of the most influential solution concepts in game theory. With the development of computer science and artificial intelligence, there is an increasing demand on Nash equilibrium computation, especially for Internet economics and multi-agent learning. This paper reviews various algorithms computing the Nash equilibrium and its approximation solutions in finite normal-form games from both theoretical and empirical perspectives. For the theoretical part, we classify algorithms in the literature and present basic ideas on algorithm design and analysis. For the empirical part, we present a comprehensive comparison on the algorithms in the literature over different kinds of games. Based on these results, we provide practical suggestions on implementations and uses of these algorithms. Finally, we present a series of open problems from both theoretical and practical considerations. • A classification on Nash equilibrium algorithms in the literature with basic ideas on design and analysis presented. • A comprehensive comparison on Nash equilibrium algorithms in the literature over different kinds of games. • Practical suggestions on implementations and uses of Nash equilibrium algorithms. [ABSTRACT FROM AUTHOR]

• Combine VMD and whale optimization algorithm for noise reduction of MCG signal. • Adaptive optimization of the variational mode decomposition is performed. • Prevention of local optimum problems in optimization is performed. • Adaptive thresholding is used to denoise the decomposition results. • The algorithm was evaluated with simulated and measured signals. In the diagnosis and treatment of cardiac diseases, magnetocardiography (MCG) technology is characterized by non-invasive, non-contact, and high precision. Therefore, it has currently become a research hotspot in the field of new medical technologies. However, due to the weak signal of the magnetocardiography, the noise needs to be filtered after acquisition. In this paper, an optimized variational mode decomposition algorithm based on the improved threshold method and the improved whale optimization algorithm (WOA) is proposed to process the MCG signal. In order to improve the denoising accuracy, the improved whale optimization algorithm, VMD algorithm, and the improved threshold method are combined. Firstly, the correlation coefficients are obtained by the improved whale optimization algorithm to decompose the IMFs, then the baseline drift is removed by using the moving average method for the low-frequency IMFs, and then the improved thresholding algorithm is applied to each IMF. Finally, the denoised signal is obtained by integration. Experimental tests show that the algorithm has good denoising performance compared with similar algorithms and can filter environmental noise as much as possible without changing the original signal information. The proposed method has the highest Signal-to-Noise Ratio improvement (SNR imp) and Correlation Coefficient (CC) and the lowest Percentage Root Mean Square Difference (PDR). Also, the method is validated in real MCG signal processing. The proposed algorithm can be applied in the field of signal denoising, and it has a wide range of application backgrounds. [ABSTRACT FROM AUTHOR]

In online three-dimensional packing problems (3D-PPs), unlike offline problems, items arrive sequentially and require immediate packing decisions without any information about the quantities and sizes of the items to come. Heuristic methods are of great importance in solving online problems to find good solutions in a reasonable amount of time. However, the literature on heuristics for online problems is sparse. As our first contribution, we developed a pool of heuristics applicable to online 3D-PPs with complementary performance on different sets of instances. Computational results showed that in terms of the number of used bins, in all problem instances, at least one of our heuristics had a better or equal performance compared to existing heuristics in the literature. The developed heuristics are also fully applicable to an intermediate class between offline and online problems, referred to in this paper as a specific type of "semi-online with full look-ahead", which has several practical applications. In this class, as in offline problems, complete information about all items is known in advance (i.e., full look-ahead); however, due to time or space constraints, as in online problems, items should be packed immediately in the order of their arrival. As our second contribution, we presented an algorithm selection framework, building on developed heuristics and utilizing prior information about items in this specific class of problems. We used supervised machine learning techniques to find the relationship between the features of problem instances and the performance of heuristics and to build a prediction model. The results indicate an 88% accuracy in predicting (identifying) the most promising heuristic(s) for solving any new instance from this class of problems. • Several heuristic methods to solve online 3D-packing problems (3D-PPs) are proposed. • Proposed heuristics are applicable to semi-online 3D-PPs with full look-ahead. • An algorithm selection framework to predict the most promising heuristic is proposed. • The prediction performance of three learning algorithms is compared in the framework. • The multi-label neural network demonstrates a notably high prediction accuracy. [ABSTRACT FROM AUTHOR]

Disorders with a transient signature (e.g., obstructive sleep apnea, drowsy driving, or atrial fibrillation) are difficult to predict. In addition, the aging population is moving towards dependency and medical structures lack caregivers. This paper introduces an innovative and generic vitals monitoring software. This real-time and embedded software, called BCG-VARS, uses ballistocardiography to perform contactless measurements of vital parameters on various types of equipment (e.g. medical bed, car seat, wheelchair). The software consists in two algorithms and was designed to extract three different vital parameters: actigraphy, Breath-to-Breath Interval (BBI), and heart Inter-Beats Interval (IBI). The algorithms were designed to analyze ballistocardiography signals but can be reused for other pseudo-periodic signals. BCG-VARS shows state-of-the-art high performances, even when sensors are deeply integrated in a medical bed. Absolute mean errors of 1.59 beats per minute for IBIs and 1.03 cycles per minute for BBIs were obtained in the deep integration configuration. In addition, all the subjects presences and movements (lie down, roll over or leave) were detected by the algorithms. Thanks to parameter tuning and interval prediction, the system is accurate, reliable in multiple applications and adapts to every new individual. [Display omitted] • BCG measurement of vitals monitoring in real-time, without learning requirement. • Reusable algorithms for the majority of pseudo-sinusoidal signals. • Use of interval prediction on heart pattern, reducing large measurement errors. • Parameter tuning can be done, to fit a use case and improve interval prediction. [ABSTRACT FROM AUTHOR]

The increasing demand for missions involving unmanned aerial vehicles (UAVs) raises the need for reliable and accurate time synchronization in ad hoc networks. Time synchronization plays a crucial role in coordinating the actions of multiple UAVs, particularly when it comes to positioning, coordination, and data fusion. However, achieving high-precision time synchronization is challenging due to the difficult-to-estimate communication transmission delay, measurement noise, relative motion, and clock source characteristics in wireless ad hoc networks formed by UAVs. To address this issue, this paper proposes a fully distributed high-precision network time synchronization algorithm that is suitable for multi-hop dynamic ad hoc networks. The proposed algorithm is based on bidirectional pseudo-range measurements, graph theory, and random matrix correlation theory. It is composed of relative clock skew estimation, logic clock skew compensation and logic clock offset compensation. The algorithm is rigorously derived, requiring no central node, and it is capable of obtaining relative clock skew even when nodes broadcast clock information asynchronously. In addition, the proposed algorithm enables real-time elimination of transmission delays and has noise-resistant capabilities. Simulation results validate the effectiveness of the algorithm, demonstrating that the time synchronization accuracy in UAV ad hoc networks can reach the sub-nanosecond level. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of vehicles (IoV) and the continuous emergence of vehicle information applications, the demand for content in vehicle networking is growing at an alarming speed. Mobile vehicular edge caching is regarded as a promising technology in improving Quality of Service (QoS) and reducing latency. Many caching algorithms have been proposed, which usually place contents in the Road Side Units (RSUs) to provide services to users near them. However, due to the high-speed movement of vehicles and limitations of RSU coverage, caching interrupts often occur frequently, leading to a deterioration in service quality. To deal with this problem, we make full use of Vehicle-to-Vehicle (V2V) collaboration to construct a caching system which does not require RSU support, and propose a Recursive Deep Reinforcement Learning based Collaborative Caching Relay strategy (RDRL-CR). On purpose to minimize the service delay under capacity constraints, the caching problem is formulated as an integer linear programming problem, and caching decisions are achieved through partially observable Markov Decision Process (MDP). Specifically, this strategy utilizes a Graph Neural Network (GNN) to predict vehicle trajectories, and then selects vehicles that can serve as caching nodes by calculating link stability metrics between vehicles. The Long Short Term Memory (LSTM) network is embedded into a deep deterministic strategy gradient algorithm to achieve the final caching decision. Compared with existing caching strategies, the proposed caching strategy in this paper improves the caching hit rate by about 25% and reduces content access latency by about 20%. [ABSTRACT FROM AUTHOR]

Moving sensor nodes can mitigate the coverage problem of random deployment in wireless sensor networks. However, the movement of nodes affects the lifetime and integrity of the network. Therefore, both energy saving and efficient coverage are crucial factors. In this paper, we propose an energy-efficient coverage optimization technique with the help of the multi-Strategy grey wolf optimization (MSGWO) algorithm. This method can reduce energy consumption and improve coverage area by mixing higher-order multinomial sensing models and a sort-driven hybrid opposition-based learning. In addition, node movement and boundary strategies are proposed to help nodes jump out of obstacles when facing obstacle-aware deployments. The MSGWO is validated and compared on several classical test functions, and the results show that the MSGWO performs well. The MSGWO algorithm is applied to optimize the WSN coverage on different obstacle scenarios, the experimental results show that the algorithm helps to increase the network coverage from 84 % to 97.86 %, extends the network lifecycle by 50 %, reduces the cost of node deployment, and the network has good connectivity and scalability. [ABSTRACT FROM AUTHOR]