Search

Ant-inspired metaheuristic algorithms known as ant colony optimization (ACO) offer an approach that has the ability to solve complex problems in both discrete and continuous domains. ACOs have gained significant attention in the field of water resources management, since many problems in this domain are non-linear, complex, challenging and also demand reliable solutions. The aim of this study is to critically review the applications of ACO algorithms specifically in the field of hydrology and hydrogeology, which include areas such as reservoir operations, water distribution systems, coastal aquifer management, long-term groundwater monitoring, hydraulic parameter estimation, and urban drainage and storm network design. Research articles, peer-reviewed journal papers and conference papers on ACO were critically analyzed to identify the arguments and research findings to delineate the scope for future research and to identify the drawbacks of ACO. Implementation of ACO variants is also discussed, as hybrid and modified ACO techniques prove to be more efficient over traditional ACO algorithms. These algorithms facilitate formulation of near-optimal solutions, and they also help improve cost efficiency. Although many studies are attempting to overcome the difficulties faced in the application of ACO, some parts of the mathematical analysis remain unsolved. It is also observed that despite its popularity, studies have not been successful in incorporating the uncertainty in ACOs and the problems of dimensionality, convergence and stability are yet to be resolved. Nevertheless, ACO is a potential area for further research as the studies on the applications of these techniques are few. [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue of the journal AIMS Mathematics on the topic of discrete optimization. The issue includes 21 papers covering a range of subjects, including molecular trees, network systems, variational inequality problems, scheduling, image restoration, spectral clustering, integral equations, convex functions, graph products, optimization algorithms, air quality prediction, humanitarian planning, inertial methods, neural networks, transportation problems, emotion identification, fixed-point problems, structural engineering design, single machine scheduling, and ensemble learning. The papers present new theoretical results, algorithms, and applications in these areas. The guest editor expresses gratitude to the journal staff and reviewers and hopes that readers will find inspiration for their own research. [Extracted from the article]

Due to the complexity and the magnitude of optimisation models that appeared in sustainable supply chain management (SSCM), the use of meta-heuristic algorithms as competent solution approaches is being increased in recent years. Although a massive number of publications exist around SSCM, no extant paper explicitly investigates the role of meta-heuristics in the sustainable (forward) supply chain. To fill this gap, a literature review is provided on meta-heuristic algorithms applied in SSCM by analyzing 160 rigorously selected papers published by the end of 2020. Our statistical analysis ascertains a considerable growth in the number of papers in recent years and reveals the contribution of 50 journals in forming the extant literature. The results also show that in the current literature the use of hybrid meta-heuristics is overtaking pure meta-heuristics, the genetic algorithm (GA) and the non-dominated sorting GA (NSGA-II) are the most-used single- and multi-objective algorithms, the aspects of sustainability are mostly addressed in connection with product distribution and routing of vehicles as pivotal operations in supply chain management, and last but not least, the economic-environmental category of sustainability has been further noticed by the scholars. Finally, a detailed discussion of findings and recommendations for future research are provided. [ABSTRACT FROM AUTHOR]

Particle swarm optimization algorithms (PSOA) is a metaheuristic algorithm used to optimize computational problems using candidate solutions or particles based on selected quality measures. Despite the extensive research published, studies that critically examine its recent scientific developments and research impact are lacking. Therefore, the publication trends and research landscape on PSOA research were examined. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and bibliometric analysis techniques were applied to identify and analyze the published documents indexed in Scopus from 2001 to 2021. The published documents on PSOA increased from 8 to 1,717 (21,362.50%) due to the growing applications of PSOA in solving computational problems. "Conference papers" is the most common document type, whereas the most prolific researcher on PSOA is Andries P. Engelbrecht (South Africa). The most active affiliation (Ministry of Education) and funding organization (National Natural Science Foundation) are based in China. The research landscape on PSOA revealed high levels of publications, citations, and collaborations among the top authors, institutions, and countries worldwide. Keywords co-occurrence analysis revealed that "particle swarm optimization (PSO)" occurred more frequently than others. The findings of the study could provide researchers and policymakers with insights into the prospects and challenges of PSOA research relative to similar algorithms in the literature. [ABSTRACT FROM AUTHOR]

The second paper, authored by M. Kara, A. Laouid, M. AlShaikh, M. Hammoudeh, A. Bounceur, R. Euler, A. Amamra, and B. Laouid proposed a multi-round Proof of Work (PoW) consensus algorithm for preserving energy consumption and resisting attacks [[9]]. Special Features on AMC The most commonly seen AMC algorithms include genetic algorithm (GA), genetic programming (GP), evolutionary strategy (ES), evolutionary programming (EP), memetic algorithm (MA), particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), differential evolution (DE), firefly algorithm (FA), simulated annealing (SA), tabu search (TS), scatter search (SS), variable neighborhood search (VNS), and GRASP, to name a few. The first paper, authored by J. Chou, T. Pham, and C. Ho, developed a metaheuristic-optimized machine-learning algorithm for multi-level classification in civil and construction engineering [[1]]. The adaptive shrinking grid search chaos wolf optimization algorithm was proposed to optimize the parameters of the neural network to enhance the image recognition accuracy. [Extracted from the article]

Nature-Inspired Metaheuristics (NIM) have emerged as a potent tool for solving complex and difficult optimization problems, arising in various industries, which otherwise become quite difficult (if not impossible) to solve by the classical methods based on gradient search. Further, NIM techniques are more likely to obtain a global optimal solution, often desired and sometimes a necessity in several real life situations. In this study we employ SABC, a variant of Artificial Bee Colony (ABC), a relatively newer NIM algorithm, for solving four typical processes of a paper mill where optimization can be applied. We have also considered two chemical process problems that can be related to paper industry. Numerical results show that the proposed SABC scheme is efficient in dealing with these problems. [ABSTRACT FROM AUTHOR]

Experimental results on five public gene expression profile datasets verify that the proposed algorithm outperforms other methods in terms of diversity, distribution stability, and quality of generated samples. The fifth paper by I Tingyu Ye i et al. on "Artificial bee colony algorithm with an adaptive search manner and dimension perturbation" proposes a modified artificial bee colony (ABC) algorithm with an adaptive search manner and dimension perturbation (ASDABC). The tenth paper by I Fei Han i et al. on "Gene-CWGAN: A Data Enhancement Method for Gene Expression Profile Based on Improved CWGAN-GP" tackles the problem of high dimension and small sample size of gene expression profile data classification. [Extracted from the article]

The paper [[10]] considers an assignment problem and some modifications which can be converted to routing, distribution, or scheduling problems. This special issue of I Algorithms i is dedicated to recent developments of scheduling algorithms and new applications. References 1 Werner F., Burtseva L., Sotskov Y. Special Issue on Algorithms for Scheduling Problems. For this problem, a hybrid metaheuristic algorithm is presented which combines a genetic algorithm with a so-called spotted hyena optimization algorithm. [Extracted from the article]

Lightning strikes can cause equipment damage and power outages, so the distribution system's reliability in withstanding lightning strikes is crucial. This research paper presents a model that aims to optimise the configuration of a lightning protection system (LPS) in the power distribution system and minimise the System Average Interruption Frequency Index (SAIFI), a measure of reliability, and the associated cost investment. The proposed lightning electromagnetic transient model considers LPS factors such as feeder shielding, grounding design, and soil types, which affect critical current, flashover rates, SAIFI, and cost. A metaheuristic algorithm, PSOGSA, is used to obtain the optimal solution. The paper's main contribution is exploring grounding schemes and soil resistivity's impact on SAIFI. Using 4 grounding rods arranged in a straight line under the soil with 10 Ωm resistivity reduces grounding resistance and decreases SAIFI from 3.783 int./yr (no LPS) to 0.146 int./yr. Unshielded LPS has no significant effect on critical current for soil resistivity. Four test cases with different cost investments are considered, and numerical simulations are conducted. Shielded LPSs are more sensitive to grounding topologies and soil resistivities, wherein higher investment, with 10 Ωm soil resistivity, SAIFI decreases the most by 73.34%. In contrast, SAIFIs for 1 kΩm and 10 kΩm soil resistivities show minor decreases compared to SAIFIs with no LPS. The study emphasises the importance of considering soil resistivity and investment cost when selecting the optimal LPS configuration for distribution systems, as well as the significance of LPS selection in reducing interruptions to customers. [ABSTRACT FROM AUTHOR]

There are many issues and problems in cloud computing that researchers try to solve by using different techniques. Most of the cloud challenges are NP-hard problems; therefore, many meta-heuristic techniques have been used for solving these challenges. As a famous and powerful meta-heuristic algorithm, the Ant Colony Optimisation (ACO) algorithm has been recently used for solving many challenges in the cloud. However, in spite of the ACO potency for solving optimisation problems, its application in solving cloud issues in the form of a review article has not been studied so far. Therefore, this paper provides a complete and detailed study of the different types of ACO algorithms for solving the important problems and issues in cloud computing. Also, the number of published papers for various publishers and different years is shown. In this paper, available challenges are classified into different groups, including scheduling, resource allocation, load balancing, consolidation, virtual machine placement, service composition, energy consumption, and replication. Then, some of the selected important techniques from each category by applying the selection process are presented. Besides, this study shows the comparison of the reviewed approaches and also it highlights their principal elements. Finally, it highlights the relevant open issues and some clues to explain the difficulties. The results revealed that there are still some challenges in the cloud environments that the ACO is not applied to solve. [ABSTRACT FROM AUTHOR]

The article focuses on the fourth issue of the Publisher in 2023, featuring 10 papers from various countries. Topics include enhanced ultrasound classification of microemboli using convolutional neural networks, a hybrid metaheuristic optimization algorithm for global optimization and data classification, and a novel tolerance-based moderator-guided heterogeneous group decision-making involving experts and end-users.

Over the past decade, solving complex optimization problems with metaheuristic algorithms has attracted many experts and researchers. Nature has always been a model for humans to draw the best mechanisms and the best engineering out of it and use it to solve their problems. The concept of optimization is evident in several natural processes, such as the evolution of species, the behavior of social groups, the immune system, and the search strategies of various animal populations. For this purpose, the use of nature-inspired optimization algorithms is increasingly being developed to solve various scientific and engineering problems due to their simplicity and flexibility. Anything in a particular situation can solve a significant problem for human society. This paper presents a comprehensive overview of the metaheuristic algorithms and classifications in this field and offers a novel classification based on the features of these algorithms. [ABSTRACT FROM AUTHOR]

Vehicle-to-grid (V2G) systems play a key role in the integration of electric vehicles (EVs) into smart grids by enabling bidirectional energy flows between EVs and the grid. Optimizing V2G operations poses significant challenges due to the dynamic nature of energy demand, grid constraints, and user preferences. This paper addresses the optimization challenges in V2G systems and explores the use of artificial intelligence (AI) methods to tackle these challenges. The paper provides a comprehensive analysis of existing work on optimization in V2G systems and identifies gaps where AI-driven algorithms, machine learning, metaheuristic extensions, and agile optimization concepts can be applied. Case studies and examples demonstrate the efficacy of AI-driven algorithms in optimizing V2G operations, leading to improved grid stability, cost optimization, and user satisfaction. Furthermore, agile optimization concepts are introduced to enhance flexibility and responsiveness in V2G optimization. The paper concludes with a discussion on the challenges and future directions for integrating AI-driven methods into V2G systems, highlighting the potential for these intelligent algorithms and methods. [ABSTRACT FROM AUTHOR]

The flying foxes optimization (FFO) algorithm, as a newly introduced metaheuristic algorithm, is inspired by the survival tactics of flying foxes in heat wave environments. FFO preferentially selects the best-performing individuals. This tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search area. To address this issue, the paper introduces an opposition-based learning-based search mechanism for FFO algorithm (IFFO). Firstly, this paper introduces niching techniques to improve the survival list method, which not only focuses on the adaptability of individuals but also considers the population's crowding degree to enhance the global search capability. Secondly, an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its quality. Finally, to verify the superiority of the improved search mechanism, IFFO, FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test functions. The results prove that compared with other algorithms, IFFO is characterized by its rapid convergence, precise results and robust stability. [ABSTRACT FROM AUTHOR]

The prediction of the ultimate bearing capacity (UBC) of composite foundations represents a critical application of test monitoring data within the field of intelligent geotechnical engineering. This paper introduces an effective combinational prediction algorithm, namely SA-IRMO-BP. By integrating the Improved Radial Movement Optimization (IRMO) algorithm with the simulated annealing (SA) algorithm, we develop a meta-heuristic optimization algorithm (SA-IRMO) to optimize the built-in weights and thresholds of backpropagation neural networks (BPNN). Leveraging this integrated prediction algorithm, we forecast the UBC of soil–cement mixed (SCM) pile composite foundations, yielding the following performance metrics: RMSE = 3.4626, MAE = 2.2712, R = 0.9978, VAF = 99.4339. These metrics substantiate the superior predictive performance of the proposed model. Furthermore, we utilize two distinct datasets to validate the generalizability of the prediction model presented herein, which carries significant implications for the safety and stability of civil engineering projects. [ABSTRACT FROM AUTHOR]

Many companies prefer to use third party logistics firms to deliver their goods and as such planning the return of the vehicles to the depot is not required. This is called open vehicle routing problem (OVRP). In literature, the OVRP is handled with minimum distance as objective function like vehicle routing problem. But in the real world, the objective function achieves minimum many costs like standard routing cost, stopping by cost and the deviation cost. The standard routes are previously defined under free market conditions by third party logistic firms. The deviation from the standard route is required to arrive cities which are not on the standard route. The stop by cost occurs on the delivery points. In this paper mentioned three costs are considered in the objective function while many papers consider only distance related costs in the literature. This paper proposes a new mathematical model for the OVRP. In the constraints, the last points of the routes are researched in detail. The standard route costs are determined by considering the last point of the route. Because of the NP-hard structure of the OVRP, the proposed mathematical model is solved with a hybrid metaheuristic called Civilized Genetic Algorithm (CGA). CGA is developed by hybridizing a modified genetic algorithm and a local search algorithm. The application of this study is implemented for the delivery routing of a combi boiler producer in Turkey. The third party logistic firms may use this proposed model and the solution approach for the real life applications. [ABSTRACT FROM AUTHOR]

The real world data are complex in nature and addition to that a large number of features add more value to the complexity. However, the features associated with the data may be redundant and erroneous in nature. To deal with such type of features, feature selection plays a vital role in computational learning. The reduction in the dimensionality of the dataset not only reduces the computational time required for classification but also enhances the classification accuracy by removing the misleading features. This paper presents a Filter Approach using Elitism based Multi-objective Differential Evolution algorithm for feature selection (FAEMODE) and the novelty lies in the objective formulation, where both linear and nonlinear dependency among features have been considered to handle the redundant and unwanted features of a dataset. Finally, the selected feature subsets of 23 benchmark datasets are tested using 10-fold cross validation with four well-known classifiers to endorse the result. A comparative analysis of the proposed approach with seven filter approaches and two conventional as well as three metaheuristic based wrapper approaches have been carried out for validation. The result reveals that the proposed approach can be considered as a powerful filter method for feature selection in various fields. [ABSTRACT FROM AUTHOR]

This paper carries out the performance analysis of three control system structures and approaches, which combine Reinforcement Learning (RL) and Metaheuristic Algorithms (MAs) as representative optimization algorithms. In the first approach, the Gravitational Search Algorithm (GSA) is employed to initialize the parameters (weights and biases) of the Neural Networks (NNs) involved in Deep QLearning by replacing the traditional way of initializing the NNs based on random generated values. In the second approach, the Grey Wolf Optimizer (GWO) algorithm is employed to train the policy NN in Policy Iteration RL-based control. In the third approach, the GWO algorithm is employed as a critic in an Actor-Critic framework, and used to evaluate the performance of the actor NN. The goal of this paper is to analyze all three RL-based control approaches, aiming to determine which one represents the best fit for solving the proposed control optimization problem. The performance analysis is based on non-parametric statistical tests conducted on the data obtained from real-time experimental results specific to nonlinear servo system position control. [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]

Flexible job shop scheduling problem (FJSP) is the core decision-making problem of intelligent manufacturing production management. The Harris hawk optimization (HHO) algorithm, as a typical metaheuristic algorithm, has been widely employed to solve scheduling problems. However, HHO suffers from premature convergence when solving NP-hard problems. Therefore, this paper proposes an improved HHO algorithm (GNHHO) to solve the FJSP. GNHHO introduces an elitism strategy, a chaotic mechanism, a nonlinear escaping energy update strategy, and a Gaussian random walk strategy to prevent premature convergence. A flexible job shop scheduling model is constructed, and the static and dynamic FJSP is investigated to minimize the makespan. This paper chooses a twosegment encoding mode based on the job and the machine of the FJSP. To verify the effectiveness of GNHHO, this study tests it in 23 benchmark functions, 10 standard job shop scheduling problems (JSPs), and 5 standard FJSPs. Besides, this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company's FJSP. The optimized scheduling scheme demonstrates significant improvements in makespan, with an advancement of 28.16% for static scheduling and 35.63% for dynamic scheduling. Moreover, it achieves an average increase of 21.50% in the on-time order delivery rate. The results demonstrate that the performance of theGNHHO algorithm in solving FJSP is superior to some existing algorithms. [ABSTRACT FROM AUTHOR]

In the paper, I Traffic flow guidance algorithm in intelligent transportation systems considering the effect of non i - I floating vehicle i , Chen et al. ([4]) present the method of estimating non-floating vehicles' driving information according to floating vehicles' information. The authors presented the estimation method, a new traffic flow guidance algorithm, Estimated Weighted Vehicle Density Feedback Strategy (EWVDFS) based on Weighted Vehicle Density Feedback Strategy (WVDFS). In the paper, A I robust lane detection method based on hyperbolic model i , Li et al. ([9]) investigated a robust lane detection method under structured roads. The paper I Road network i - I based region of interest mining and social relationship recommendation i by Tan and Zhang ([17]) proposed road context-based active region extraction algorithm (RAREA) which explores the method to extract the specific regions within the road network. [Extracted from the article]

For managing data redundancy, the proposed Intelligent Data Fusion Technique (IDFT) decreases the quantity of transmitting data, broadens the network life cycle, enhances bandwidth utilization, and therefore resolves the energy and bandwidth usage bottleneck. Over the last five decades, fuzzy optimization has found numerous successful applications in diverse fields including operations research, manufacturing, information technology, energy optimization, data science and smart cities, big data analytics, etc. Fuzzy optimization is one kind of approximation of nonlinear optimization techniques, which has formed some systematic but not unified theories of fuzzy systems and other fuzzy-set-based methodologies. [Extracted from the article]

The aim of this paper is to evaluate the optimization capabilities of the salp swarm algorithm (SSA), a metaheuristic algorithm capable of addressing contemporary global challenges. The paper focuses on assessing SSA as an optimizer and observing its impact as a predictor in an example energy problem to gauge its predictive power. Salp swarm algorithm (SSA) distinguishes itself with its optimization capabilities, providing effective solutions to optimization problems. The quality, competitiveness, and efficiency of the algorithm were initially assessed using the CEC 2019 and CEC 2020 function sets. The results demonstrated that SSA is a competitive, effective, and up-to-date algorithm. This competitive nature suggests that SSA can be effectively employed across a wide range of problems. Therefore, the paper aims to evaluate its success in providing solutions to an energy prediction problem. In addressing the challenge of effective energy utilization, the accurate prediction of heat loading (HL) and cool loading (CL) factors, critical in building design, contributes significantly to the solution. In solving this problem, machine learning algorithms, specifically the multi-layer perceptron (MLP) as an artificial neural network architecture, were chosen. SSA was approached in a supervised manner, and a comparison with alternative metaheuristic algorithms was conducted. The obtained results indicate that the SSA-based MLP architecture (SSA-MLP) exhibits effective predictive capabilities in energy problems. By combining the optimization power of SSA and the learning capabilities of MLP, a robust solution with a competitive advantage in energy efficiency is presented. [ABSTRACT FROM AUTHOR]

Precisely locating the voltage sag sources is significant for improving power quality and clarifying the responsibilities of both power supply and consumption. However, the existing location models are complex, computationally intensive, and influenced by the measurement error and fault type, which have low positioning efficiency and large errors. Therefore, this paper proposes a method for locating voltage sag sources based on power polarity and twin characteristics matching. The method firstly uses the polarity of the sequence sag component disturbance reactive power at the monitoring point to determine the sag line. Secondly, the basic realization method of the twin power system is discussed, and the twin characteristics are defined by combining the positive and negative sequence sag component disturbance reactive power and the twin database is established. Thirdly, the spectral norm and Pearson product‐moment correlation coefficient are used to define the data matching degree, and the precise location model of the sag source is established with the objective function of maximizing the data matching between the twin characteristics of the preset and actual sag. Finally, the Improved Whale Optimization Algorithm is used to solve the location model. The correctness and superiority of the proposed method is verified based on IEEE33 node modelling. [ABSTRACT FROM AUTHOR]

To further improve the ride comfort of commercial vehicles, a seat ISD (Inerter–Spring–Damper) suspension utilizing a mechatronic inerter is proposed in this paper. Firstly, a five-DOF (degree-of-freedom) commercial vehicle seat ISD model was built. Then, the positive real network constraint conditions of a biquadratic impedance transfer function were determined, and the meta-heuristic intelligent optimization algorithm was used to solve the parameters. According to the solution, the impedance transfer function was obtained and the specific network structure was realized by network synthesis. Lastly, this study compares the vibration isolation performance of the mechatronic ISD suspension of the vehicle seat with that of a passive suspension. In comparison to passive seat suspension, the seat mechatronic ISD suspension reduces seat vibration transmissibility by 16.33% and vertical acceleration by 16.78%. Results indicate that the new suspension system can be an effective improvement in ride comfort. [ABSTRACT FROM AUTHOR]

In the process of multi-energy system optimal scheduling, due to the high data processing requirements of the multi-energy devices and loads and the complexity of the operating states of the multi-energy devices, the scheduling optimization of the system is to some extent more difficult. To address this problem, this paper proposes a regional multi-energy system optimal scheduling model based on the theory of cloud-edge collaboration. First, based on intelligent data sensors, a cloud-edge cooperative scheduling framework of the regional multi-energy system is constructed. Then, the physical model of operating state data of multi-energy system equipment and the allocation mechanism of system scheduling tasks are studied. With the cloud service application layer and the edge computing layer as the upper and lower optimization scheduling layers, the double-layer optimization scheduling model of the regional multi-energy system is established. The objectives of the model are optimal scheduling cost and minimum delay of scheduling data transmission. The multi-objective whale optimization algorithm is used to solve the model. Finally, a simulation model is built for verification. The simulation results show that the scheduling model established in this paper can effectively improve the scheduling data processing capability and improve the economy of regional multi-energy system scheduling. [ABSTRACT FROM AUTHOR]

Purpose: Advancement in optimization of VLSI circuits involves reduction in chip size from micrometer to nanometer level as well as fabrication of a billions of transistors in a single die where global routing problem remains significant with a trade-off of power dissipation and interconnect delay. This paper aims to solve the increased complexity in VLSI chip by minimization of the wire length in VLSI circuits using a new approach based on nature-inspired meta-heuristic, invasive weed optimization (IWO). Further, this paper aims to achieve maximum circuit optimization using IWO hybridized with particle swarm optimization (PSO). Design/methodology/approach: This paper projects the complexities of global routing process of VLSI circuit design in mapping it with a well-known NP-complete problem, the minimum rectilinear Steiner tree (MRST) problem. IWO meta-heuristic algorithm is proposed to meet the MRST problem more efficiently and thereby reducing the overall wire-length of interconnected nodes. Further, the proposed approach is hybridized with PSO, and a comparative analysis is performed with geosteiner 5.0.1 and existing PSO technique over minimization, consistency and convergence against available benchmark. Findings: This paper provides high performance–enhanced IWO algorithm, which keeps in generating low MRST value, thereby successful wire length reduction of VLSI circuits is significantly achieved as evident from the experimental results as compared to PSO algorithm and also generates value nearer to geosteiner 5.0.1 benchmark. Even with big VLSI instances, hybrid IWO with PSO establishes its robustness over achieving improved optimization of overall wire length of VLSI circuits. Practical implications: This paper includes implications in the areas of optimization of VLSI circuit design specifically in the arena of VLSI routing and the recent developments in routing optimization using meta-heuristic algorithms. Originality/value: This paper fulfills an identified need to study optimization of VLSI circuits where minimization of overall interconnected wire length in global routing plays a significant role. Use of nature-based meta-heuristics in solving the global routing problem is projected to be an alternative approach other than conventional method. [ABSTRACT FROM AUTHOR]

The aim of this special issue is to introduce the burgeoning topic of Autonomous System. Finally, in order to check the conformity of this enforcement with defined multi-cloud SLA, the authors propose an approach for multi-cloud SLA reporting inspired by conformance checking techniques. I In i I the i I paper i I authored i I by i I Jeremy i I Mechouche i , the authors propose a hierarchical representation of multi-cloud SLAs: sub-SLAs associated with a system's components deployed on distinct cloud service providers and global-SLA associated with the whole system. [Extracted from the article]

Considering the power transformers fault diagnosis model has unstable performance and prone to over-fitting, we propose a transformers fault diagnosis model based on a meta-learning approach to kernel extreme learning machine with opposition-based learning sparrow search algorithm optimization (Meta-OSSA-KELM) in this paper. Its learning proceeds in two steps. Firstly, the base-learner KELMs is trained on the disjoint training subset. Then, meta-learner KELM is trained with the hidden codes of training set in base-learner KELMs that have been trained. In this paper, chaotic mapping and opposition-based learning are integrated into Sparrow search algorithm(SSA) and used it to optimize each learner. We simulate this model with measured dissolved gas analysis(DGA) data, the results show that compared with PSO and SSA, opposition-based learning sparrow search algorithm(OSSA) has better global search-ability on the optimization for the proposed model. In addition, compared with Adaboost.M1, BPNN, SVM and KELM, Meta-OSSA-KELM has a higher average accuracy (90.9% vs 78.5%, 74.0%, 76.9%, 76.9%) and a lower standard deviation (1.56x10-2 vs 4.21x10-2, 10.5x10-2, 3.7x10-2, 2.18x10-2) in simulation tests for 30 times. It is shown that the proposed model is a stable and better performance transformers fault diagnosis method. [ABSTRACT FROM AUTHOR]

Cloud computing delivers computing resources like software and hardware as a service to the users through a network. Due to the scale of the modern datacentres and their dynamic resources provisioning nature, we need efficient scheduling techniques to manage these resources. The main objective of scheduling is to assign tasks to adequate resources in order to achieve one or more optimization criteria. Scheduling is a challenging issue in the cloud environment, therefore many researchers have attempted to explore an optimal solution for task scheduling in the cloud environment. They have shown that traditional scheduling is not efficient in solving this problem and produce an optimal solution with polynomial time in the cloud environment. However, they introduced sub-optimal solutions within a short period of time. Meta-heuristic techniques have provided near-optimal or optimal solutions within an acceptable time for such problems. In this work, we have introduced the major concepts of resource scheduling and provided a comparative analysis of many task scheduling techniques based on different optimization criteria. [ABSTRACT FROM AUTHOR]

This document is a discussion of a research paper titled "Predicting Solid-Particle Erosion Rate of Pipelines Using Support Vector Machine with Improved Sparrow Search Algorithm." The authors of the discussion express their appreciation for the authors of the original paper for enhancing the sparrow search algorithm (SSA) through adaptive t-distribution (AT) and evaluating its performance in optimizing the support vector machine (SVM) for predicting erosion rates in pipelines. The authors compared the results of the AT-SSA-SVM with other algorithms and found that the performance of SSA was improved by incorporating AT, resulting in better predictions. The discussion also highlights some discrepancies and issues with overfitting and optimization parameters in the original paper. The data availability statement and acknowledgments are also included. [Extracted from the article]

Visual Cryptography (VC) is a process employed for the maintenance of secret information by hiding the secret messages that are embedded within the images. Typically, an image is partitioned into a number of shares that are stacked over one another in order to reconstruct back the original image accurately. The major limitation that existed in the traditional VC techniques is pixel expansion, in which pixel expansion is replaced with a number of sub-pixels in individual share, which causes a considerable impact on the contrast and resolution of the image that further gradually decreases the quality of the image. VC is named for its essential characteristics, such as transmitting the images with two or more shares with an equal number of black pixels and color pixel distribution. The secret message can be decrypted using Human Visual System (HVS). In this paper, 50 research papers are reviewed based on various classification algorithms, which are effectively used for the VC technique. The classification algorithms are categorized into three types, namely, meta-heuristic, heuristic, and evolutionary, and the research issues and challenges confronted by the existing techniques are reported in this survey. Moreover, the analysis is done based on the existing research works by considering the classification algorithms, tools, and evaluation metrics. [ABSTRACT FROM AUTHOR]

A supply chain is an order of activities engaged which circulates, assembles and handles the products to move the benefits from a dealer under the control of the last customer. It is an interconnected compound network controlled by supply and demand. Cyber security in SCM is one of the segment of its estimates of protection which primarily gives attention in managing the essential virtual protection which comprises of system software of information technology. In the existing system, cloud services must need extra applications and assistances to locate, govern and protect data which initiate extra supply chain contributors. The manufacturing process data will mislead the manufacturing process in this system based on errors which are done manually. To Store and Maintain data in a protected manner, most algorithms such as DES (Data Encryption Standard) algorithm has disadvantages and threats which seems to be an upper hand for the hackers who are working to steal the data all around. In this paper, for securing the private and secret data, we applied and executed AES (Advanced Encryption Standard) algorithm and MD5 (Message-Digest algorithm 5) in supply chain management. We apply PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyzes) approach in domain of supply chain management, data security, and cyber security to screen the various methods and algorithms which are published in various journal papers and to select a unique and best approach to be used in supply chain management and its security. This method is basically a kind of literature survey to select a best topic for doing a project or a research paper. [ABSTRACT FROM AUTHOR]

The green vehicle routing problem (GVRP) is a relatively new topic, which aims to minimize greenhouse gasses (GHG) emissions produced by a fleet of vehicles. Both internal combustion vehicles (ICV) and alternative fuel vehicles (AFV) are considered, dividing GVRP into two separate subclasses: ICV-based GVRP and AFV-based GVRP. In the ICV-based subclass, the environmental aspect comes from the objective function which aims to minimize GHG emissions or fuel usage of ICVs. On the other hand, the environmental aspect of AFV-based GVRP is implicit and comes from using AFVs in transport. Since GVRP is NP-hard, finding the exact solution in a reasonable amount of time is often impossible for larger instances, which is why metaheuristic approaches are predominantly used. The purpose of this study is to detect gaps in the literature and present suggestions for future research in the field. For that purpose, we review recent papers in which GVRP was tackled by some metaheuristic methods and describe algorithm specifics, VRP attributes, and objectives used in them. [ABSTRACT FROM AUTHOR]

Background: Possible solutions to overcome the many challenges of home delivery are collection and delivery points (CDPs). In addition to commercial facilities, the role of CDPs can also be played by users' households, providing a crowd storage service. Key decisions regarding CDPs relate to their location, as well as the allocation of users to selected locations, so that the distance of users from CDPs is minimal. Methods: In this paper, the described problem is defined as a p-median problem and solved for the area of the city of Belgrade, using the heuristic "greedy" and the simulated annealing algorithm. Results: Fifty locations of CDPs were selected and the users allocated to them were distributed in over 950 zones. The individual distances between users and the nearest CDPs and the sum of these distances, multiplied by the number of requests, were obtained. An example of modification of the number of CDPs is presented as a way of obtaining solutions that correspond to different preferences of operators and/or users in terms of their distances from the CDPs. Conclusions: User households can be used as CDPs to achieve various benefits. Locating CDPs, i.e., selecting households, can be solved as a p-median problem, using a combination of heuristic and metaheuristic algorithms. In addition, by modifying the number of medians, the total and average distances between users and CDPs can be better managed. The main contributions of the paper are the establishment of users' households as potential locations of CDPs, the establishment of a framework for analysis of impact of the number of CDPs on the sum and average distances from the customers, as well as the creation of a basis for upgrading and modifying the model for implementation in the business practice. [ABSTRACT FROM AUTHOR]

Data clustering has attracted the interest of scholars in many fields. In recent years, using heuristic algorithms to solve data clustering problems has gradually become a tendency. The black hole algorithm (BHA) is one of the popular heuristic algorithms among researchers because of its simplicity and effectiveness. In this paper, an improved self-adaptive logarithmic spiral path black hole algorithm (SLBHA) is proposed. SLBHA innovatively introduces a logarithmic spiral path and random vector path to BHA. At the same time, a parameter is used to control the randomness, which enhances the local exploitation ability of the algorithm. Besides, SLBHA designs a replacement mechanism to improve the global exploration ability. Finally, a self-adaptive parameter is introduced to control the replacement mechanism and maintain the balance between exploration and exploitation of the algorithm. To verify the effectiveness of the proposed algorithm, comparison experiments are conducted on 13 datasets creatively using the evaluation criteria including the Jaccard coefficient as well as the Folkes and Mallows index. The proposed methods are compared with the selected algorithms such as the whale optimization algorithm (WOA), compound intensified exploration firefly algorithm (CIEFA), improved black hole algorithm (IBH), etc. The experimental results demonstrate that the proposed algorithm outperforms the compared algorithms on both external criteria and quantization error of the clustering problem. [ABSTRACT FROM AUTHOR]

The energy management (EM) solution of the multi-microgrids (MMGs) is a crucial task to provide more flexibility, reliability, and economic benefits. However, the energy management (EM) of the MMGs became a complex and strenuous task with high penetration of renewable energy resources due to the stochastic nature of these resources along with the load fluctuations. In this regard, this paper aims to solve the EM problem of the MMGs with the optimal inclusion of photovoltaic (PV) systems, wind turbines (WTs), and biomass systems. In this regard, this paper proposed an enhanced Jellyfish Search Optimizer (EJSO) for solving the EM of MMGs for the 85-bus MMGS system to minimize the total cost, and the system performance improvement concurrently. The proposed algorithm is based on the Weibull Flight Motion (WFM) and the Fitness Distance Balance (FDB) mechanisms to tackle the stagnation problem of the conventional JSO technique. The performance of the EJSO is tested on standard and CEC 2019 benchmark functions and the obtained results are compared to optimization techniques. As per the obtained results, EJSO is a powerful method for solving the EM compared to other optimization method like Sand Cat Swarm Optimization (SCSO), Dandelion Optimizer (DO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the standard Jellyfish Search Optimizer (JSO). The obtained results reveal that the EM solution by the suggested EJSO can reduce the cost by 44.75% while the system voltage profile and stability are enhanced by 40.8% and 10.56%, respectively. [ABSTRACT FROM AUTHOR]

Antennas play a crucial role in designing an efficient communication system. However, reducing the maximum sidelobe level (SLL) of the beam pattern is a crucial challenge in antenna arrays. Pattern synthesis in smart antennas is a major area of research because of its widespread application across various radar and communication systems. This paper presents an effective technique to minimize the SLL and thus improve the radiation pattern of the linear antenna array (LAA) using the chaotic inertia-weighted Wild Horse optimization (IERWHO) algorithm. The wild horse optimizer (WHO) is a new metaheuristic algorithm based on the social behavior of wild horses. The IERWHO algorithm is an improved Wild Horse optimization (WHO) algorithm that combines the concepts of chaotic sequence factor, nonlinear factor, and inertia weights factor. In this paper, the method is applied for the first time in antenna array synthesis by optimizing parameters such as inter-element spacing and excitation to minimize the SLL while keeping other constraints within the boundary limits, while ensuring that the performance is not affected. For performance evaluation, the simulation tests include 12 benchmark test functions and 12 test functions to verify the effectiveness of the improvement strategies. According to the encouraging research results in this paper, the IERWHO algorithm proposed has a place in the field of optimization. [ABSTRACT FROM AUTHOR]

The football team training algorithm (FTTA) is a new metaheuristic algorithm that was proposed in 2024. The FTTA has better performance but faces challenges such as poor convergence accuracy and ease of falling into local optimality due to limitations such as referring too much to the optimal individual for updating and insufficient perturbation of the optimal agent. To address these concerns, this paper presents an improved football team training algorithm called IFTTA. To enhance the exploration ability in the collective training phase, this paper proposes the fitness distance-balanced collective training strategy. This enables the players to train more rationally in the collective training phase and balances the exploration and exploitation capabilities of the algorithm. To further perturb the optimal agent in FTTA, a non-monopoly extra training strategy is designed to enhance the ability to get rid of the local optimum. In addition, a population restart strategy is then designed to boost the convergence accuracy and population diversity of the algorithm. In this paper, we validate the performance of IFTTA and FTTA as well as six comparison algorithms in CEC2017 test suites. The experimental results show that IFTTA has strong optimization performance. Moreover, several engineering-constrained optimization problems confirm the potential of IFTTA to solve real-world optimization problems. [ABSTRACT FROM AUTHOR]

The permeability coefficient of landslide mass, a key parameter in the study of reservoir landslides, is commonly obtained through in situ and laboratory tests; however, the tests are costly and subject to high variability, leading to potential biases. In this paper, a new method was proposed to inversely estimate the permeability coefficient of landslide layers using monitoring data of groundwater level (GWL). First, the landslide transient seepage simulation was conducted to generate sample data for permeability coefficients and GWL during a reservoir operation cycle. Second, using GWL data as input and permeability coefficient data as output, the least-square support vector machine (LSSVM) was trained with two optimization algorithms, the particle swarm optimization (PSO) algorithm and the whale optimization algorithm (WOA), to construct the nonlinear mapping relationship between simulated GWL and permeability coefficients. Third, the accurate permeability coefficients for landslide seepage simulation were inverted or predicted based on the monitored GWL. Finally, using the inverted permeability coefficients for landslide seepage simulation, we compared simulation results with actual monitored GWL and achieved good consistency. In addition, this paper compared the inversion effects of three different algorithms: the standard LSSVM, PSO-LSSVM, and WOA-LSSVM. This study showed that these three algorithms had good nonlinear fitting effects in studying landslide seepage fields. Among them, using the inversion values from PSO-LSSVM for landslide seepage simulation resulted in the smallest relative error compared to actual monitoring data. Within a single reservoir operation cycle, the simulated water level changes were also largely consistent with the monitored water level changes. The results could provide a reference to determine landslide permeability coefficients and seepage. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive switching anti-disturbance attitude control scheme based on improved snake optimizer (SO) is proposed for quadrotor attitude control when a quadrotor unmanned aerial vehicle is affected by measurement noise. The adaptive switching disturbance rejection controller (AWDRC) is composed of linear active disturbance rejection control and adaptive switching extended state observer which is used to achieve accurate signals reconstruction performance under measurement noise. Then, the improved SO (ISO) algorithm is developed with quadratic interpolation and comprehensive learning strategies to obtain the optimal parameters of the quadrotor attitude controller. The performance validity of ISO is demonstrated here by experiments on the CEC-2017 and the CEC-2020 benchmark functions with several state-of-the-art meta-heuristic algorithms. Secondly, the proposed ISO-based AWDRC algorithm is used in quadrotor attitude tracking control and compared with three other excellent active disturbance rejection controllers in a comparative experiment, and the experimental results show the effectiveness of the proposal. Finally, the robustness of the proposed method to parameters perturbation of the quadrotor attitude system is analyzed by Monte Carlo experiments. [ABSTRACT FROM AUTHOR]