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SCADA systems play an important role in tracking the behaviour of critical process variables and connecting geographically dispersed subsystems at the industrial plant level. This article presents a PLC and SCADA-based control framework to automate and supervise the temperature control processes in the heat exchanger plant. The OMRON (NX1P2-9024DT1) PLC is interfaced with the Wonderware InTouch SCADA system to gather data, create a simulated temperature control prototype and carry out the necessary control operations within the heat exchanger plant. The PLC controls the entire process and programming of PLC is done using Sysmac studio automation software using the ladder programming language. The proposed system controls the temperature of the heat exchanger system through PID and Fractional Order PID (P I λ D μ ) controllers with Integral Anti-windup technique. Various control strategies like Cascade Control, Feedforward Control and Smith Predictor for time delayed process are discussed for controlling the temperature of the process. The performance of both PID and fractional order PID controllers is optimized using adaptive heuristic optimization techniques like Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). In control system design and analysis, the calculated performance indices are used as quantitative measures for evaluating the performance of a system. The combined form of temperature controller with Cascade control, Feedforward control and dead-time compensator is modelled and examined for simulation using MATLAB. Simulation and real-time experimentation analysis of the developed controllers are executed with metaheuristic optimization techniques based on different performance indices like ISE, IAE and ITAE. [ABSTRACT FROM AUTHOR]

Various popular multiplayer battle royale games share a lot of common elements. Drawing from our observations, we summarized these shared characteristics and subsequently proposed a novel heuristic algorithm named multiplayer battle game-inspired optimizer (MBGO). The proposed MBGO streamlines mainstream multiplayer battle royale games into two discrete phases: movement and battle. Specifically, the movement phase incorporates the principles of commonly encountered "safe zones" to incentivize participants to relocate to areas with a higher survival potential. The battle phase simulates a range of strategies players adopt in various situations to enhance the diversity of the population. To evaluate and analyze the performance of the proposed MBGO, we executed it alongside ten other algorithms, including three classics and five latest ones, across multiple diverse dimensions within the CEC2017 and CEC2020 benchmark functions. In addition, we employed several industrial design problems to evaluate the scalability and practicality of the proposed MBGO. The statistical analysis results reveal that the novel MBGO demonstrates significant competitiveness, excelling in convergence speed and achieving high levels of convergence accuracy across both benchmark functions and real-world problems. [ABSTRACT FROM AUTHOR]

The Harris hawk Optimization (HHO) has the advantage of employing various local search strategies to adapt to different situations during the exploitation phase. Hence, HHO can search deeper into discovered prospective regions. The reason of poor robustness is that HHO exhibits high randomness in the exploration phase. In the scene of optimizing high-dimensional or multimodal Tasks, the increased dimensionality and parameter correlation lead to the problem of local optimal regions, which affects the accuracy and convergence of the solution. To address these issues, we propose Dynamic Random Mutation Hybrid Harris hawk Optimization (DRM-HHHO). In the hybrid algorithm, the Kepler Optimization Algorithm (KOA) was enhanced (called EKOA) to replace the exploration phase of HHO to solve the problem of excessive diffusion. At the end of each iteration, the leader's position is perturbed by the Dynamic Random Mutation (DRM) algorithm. The mutation probability is adjusted based on the dimension to suit problems of different scales. We conduct ablation experiment to test the effectiveness of the two improved mechanisms. Testing our method on the CEC2017 and CEC2019 benchmark functions, we find that the significance of mutation probability differences decreases with increasing dimensionality. Compared to other Metaheuristic Algorithms (MAs), such as WOA, TSA, and so on, DRM-HHHO shows superior overall performance, especially in solving high-dimensional problems. Furthermore, we successfully apply DRM-HHHO to optimize the Kernel Extreme Learning Machine (KELM) critical parameters. DRM-HHHO-KELM outperforms four traditional machine learning methods and five MAs-KELM strategies on the Australian Credit and Pima Indian Diabetes datasets. [ABSTRACT FROM AUTHOR]

In Industry 4.0, the focus of task scheduling is more on smart services such as robotic services under the paradigm of mobile edge computing, which are widely used to improve the efficiency of smart manufacturing. Existing scheduling research efforts on optimizing efficiency such as swarm algorithm, reinforcement learning, but with little real-time dynamic scheduling. Online learning and adaptation is a critical function when tackling real challenges in unpredictable and dynamically changing edge computing environments. Our goal is to propose algorithms that can meet the real-time dynamic task scheduling even reach the level of communication. We propose a Deep Reinforcement Learning (DRL)-based online real-time task scheduling method which has the new exploration and exploitation strategy using the improved sparrow search algorithm (ISSA). We replace the traditional strategy with a meta-heuristic algorithm, while introducing natural disturbances and modifying the population position update formula to converge to the optimal position. The algorithm has two layers. The first layer is scheduling generation layer, which is responsible for making decisions, and the second layer is scheduling policy update layer, which optimizes the strategy and finds the best set of hyperparameters. The experimental results show that the running time of the method to solve the problem can reach 0.0092 seconds, and the real-time performance reaches up to the millisecond communication level. At the same time other methods are compared, effectively reducing the running cost and meeting the requirements of production scheduling tasks. [ABSTRACT FROM AUTHOR]

Feature selection plays a critical role in reducing high-dimensional feature space in machine learning applications without affecting the accuracy of performance. The feature selection problem has been extensively studied in the literature. Nevertheless, few studies have been conducted on unsupervised text feature selection because of the absence of feature class labels and local optimization limitations. For that, this paper proposes three binary versions of the fire hawk optimizer based on different transfer functions for unsupervised text feature selection that can be used to select the most informative features for text clustering. The internal feature subset was evaluated using a mean absolute difference filter. The performance of the proposed methods is tested and compared with other state-of-the-art metaheuristic algorithms on several different benchmark text datasets from different sources using various evaluation metrics. The K-means clustering is applied to cluster documents based on the features selected by the methods. The results of the experiments indicate the effectiveness of the proposed method based on the S-shaped transfer function for improving the performance of document clustering compared with other feature selection methods. [ABSTRACT FROM AUTHOR]

Feature Selection (FS), a pre-processing step used in the majority of big data processing applications, aims to eliminate irrelevant and redundant features from the data. Its purpose is to select a final set of data characteristics that best represent the data as a whole. To achieve this, it explores every potential solution in order to identify the optimal subset. Meta-heuristic algorithms have been found to be particularly effective in solving FS problems, especially for high-dimensional datasets. This work adopts a recently developed meta-heuristic called the White Shark Optimizer (WSO) due to its simplicity and low computational overhead. However, WSO faces challenges in effectively balancing exploration and exploitation, particularly in complex multi-peak search problems. It tends to converge prematurely and get stuck in local optima, which can lead to poor search performance when dealing with FS problems. To overcome these issues, this paper presents three enhanced binary variants of WSO for well-known FS problems. These variants are as follows: (1) Binary Distribution-based WSO (BDWSO), where the algorithm refines the positions of white sharks by considering both the average and standard deviation of the current shark, the local best shark, and the global best shark. This strategy is designed to alleviate issues of premature convergence and stagnation during iterations , (2) Binary Sine Cosine WSO (BSCWSO), which uses sine and cosine adaptive functions for the social and cognitive components of the position update rule, and (3) Binary Hybrid Sine Cosine WSO (BHSCWSO), which employs sine and cosine acceleration factors to regulate local search and achieve convergence to the global optimal solution. Additionally, the population was initialized using the Opposition-Based Learning (OBL) mechanism, and the sine map was used to modify the inertia weight of WSO. These revised variants of WSO were established to have a better harmony between the exploration and exploitation facets. The proposed methods were extensively compared to the fundamental binary WSO and other well-known algorithms in the field. The experimental findings and comparisons demonstrate that the proposed methods outperform the conventional and most evaluated similar algorithms in terms of robustness and solution quality. In terms of classification accuracy, number of selected features, specificity, sensitivity, and fitness values, the proposed BHSCWSO optimizer performed better than all other proposed peer optimizers, including BSWO, BDWSO, and BSCWSO, in 11, 8, 13, 18, and 10 datasets, respectively. The proposed BHSCWSO optimizer showed performance levels of more than 90% in terms of accuracy, sensitivity, and specificity metric measures on 15, 14, and 11 of the 24 datasets deemed. [ABSTRACT FROM AUTHOR]

The lemur optimizer (LO) is a new metaheuristic algorithm that is inspired by the two primary locomotive patterns of the lemurs. However, LO suffers from improper balance between exploration and exploitation phases and sub-optimal solution stagnation due to poor exploration capability. To address these limitations, an improved lemur optimizer (ILO) with dynamic scaling factors and Gaussian mutation is introduced in this paper. The dynamic scaling factors balance the exploration and exploitation transition by dynamically adjusting their values throughout the optimization process. The proposed algorithm incorporates Gaussian mutation to induce diversity in the population and hence improve the exploration capability. The efficiency of the proposed approach is tested on 33 functions including classical benchmark and CEC-2019 test functions, and a comprehensive comparison is made with six other well-known metaheuristic algorithms. The simulation and statistical results indicate superior performance of ILO in achieving optimal solutions compared to the competing algorithms. The algorithm is further employed to address two constrained classical engineering design problems, demonstrating its robustness. [ABSTRACT FROM AUTHOR]

Traditional deterministic optimization algorithms are difficult to effectively solve many real-world nonlinear, complex, and high-dimensional optimization problems. The metaheuristic optimization algorithm has evolved in recent years into a kind of very popular algorithm due to its gradient-free and random nature. In this paper, a new metaheuristic algorithm, namely the literature research optimization algorithm (LRO), is proposed. In this algorithm, the literature research process is represented as a mathematical model, and new mechanisms are designed to help realize the global exploration and local exploitation. To evaluate the performance of LRO, three sets of test functions including the standard benchmark set, CEC2017, and CEC2019 are applied first, and then, six engineering problems are employed to further verify it. The Friedman test and Wilcoxon rank sum test are also used to statistically compare the proposed LRO algorithm with ten common existing metaheuristics. The results show that the LRO algorithm outperforms the comparison algorithms on most benchmark functions and that it also performs well in practical engineering applications. The LRO algorithm can provide superior solutions to most optimization problems compared to other metaheuristic algorithms and become a promising candidate for many real-life optimization problems. [ABSTRACT FROM AUTHOR]

The DWCS (dynamic wireless charging system) is an innovative electric vehicle charging solution that allows for charging while the vehicle is in motion without requiring direct physical contact. This groundbreaking technology has the capacity to significantly reduce the size of electric vehicle batteries and improve battery life if the DWCS is installed correctly along the route. However, using smaller batteries and improving battery life requires more DWCS stations to be installed along the route. This becomes more challenging due to the heterogeneity of battery vehicles, making finding the optimal compromise between batteries cost and charging infrastructure cost complex since each type has unique characteristics. To address this challenge, we present a quantitative analysis of the benefits of dynamic charging using a mathematical model that considers both batteries and charging infrastructure costs, considering the heterogeneity and life of battery vehicles. Our analysis focuses on identifying how much batteries cost can be reduced to decrease system costs while demonstrating the benefits of dynamic charging on battery life. In the resolution stage, we have created an innovative approach that utilizes multi-agent systems and reinforcement learning, explicitly Q-learning, within the framework of distributed artificial intelligence. [ABSTRACT FROM AUTHOR]

In wireless rechargeable sensor networks (WRSNs), due to data collection and charging, energy efficiency is a crucial factor because it affects the network's reliability and performance. Wireless energy transfer (WET) and wireless data collection (WDC) using separate vehicles are two promising new methods for improving the energy efficiency of sensors. We combine WET and WDC on a single mobile vehicle (MV) to charge and collect data from the sensors by traversing specified sojourn locations within the WRSNs. The objective of this work is to maximize data collection per unit of energy and reduce the number of inactive sensors. In order to accomplish this, we propose a novel scheme, namely DCPCS, that includes two heuristics and two optimization algorithms. We utilized the circle-covering algorithm to determine sojourn locations based on energy consumption rates and the location of sensors. The ant lion optimization (ALO) algorithm partitions the network into the minimum number of regions, assigns a MV to each region, and ensures balanced total energy consumption among the MVs. The ALO algorithm utilizes the first heuristic algorithm to validate the regions for the MVs. Subsequently, the discrete grey wolf optimization algorithm determines the optimal order of the sojourn locations for data collection and charging by a MV within a region. To accomplish our objectives, we propose a heuristic data collection and partial charging strategy to determine the sojourn times of the MVs at the sojourn locations. We conduct simulations and compare the results of the proposed DCPCS scheme with three existing schemes. The simulation outcomes demonstrate that our proposed scheme outperforms the other schemes in terms of various performance metrics. [ABSTRACT FROM AUTHOR]

This study fixates on determining the optimum laser input parameters that simultaneously meet the desired kerf width and depth during CO2 laser cutting of various polymethylmethacrylate (PMMA) sheets. It has three contributions. The first is to model the cutting process of PMMA by polynomial curve fitting as a function of laser power, laser speed, and standoff distance. R-squared (R2), adjusted R2 and root-mean-square error (RMSE) criteria were taken into account to measure the performance of the proposed model. The effect of laser parameters on the process is investigated by analysis of variance (ANOVA) and sensitivity analysis. The second is to optimize the derived nonlinear regression models using genetic algorithm (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA) and ant lion optimization (ALO) metaheuristic methods and compare the performances of the algorithms. The third is to compare the adequacy of the optimization process with the artificial neural network (ANN). The investigations exhibited that the best-fitting polynomials are obtained with the R2 and adjusted R2 values of over 99% and 97%, respectively. ANOVA and sensitivity test revealed that the sensitivity of the laser power, which is the most effective parameter, was 150 at low powers and decreased to 0 as the power value increased. When the nozzle distance is 4.1, the proposed metaheuristics gave effective and sufficiently accurate results. PSO stood out in terms of both best cost value (3.49 × 10–8) and relative error value (0.19%). The relative error of the ANN was found as 3% in terms of kerf depth. [ABSTRACT FROM AUTHOR]

It is known that manually detecting heart conditions is often costly and time-consuming and any study regarding diagnose these conditions has a great importance. In this study, a metaheuristic optimization model has been developed to automate the detection of heart diseases with artificial intelligence compatible methods. In the proposed model, the feature set is selected to represent the best heart sound signals and heart disease diagnoses using machine learning algorithms with these feature sets. The proposed method has been tested on the Pascal dataset which consists of four classes. Firstly, an electronic-based filter model is used as low-pass filter and has great potential to use as a filtering for heart sound signals to decrease noise. Secondly, the statistical and acoustic feature vector extracted from the audio signals in the Pascal dataset is passed through particle swarm optimization (PSO), firefly algorithm (FA) and cuckoo search algorithm (CSA), and the most suitable feature vector is selected. After obtaining the most suitable feature vector with metaheuristic optimization algorithms and filtering method, heart disease diagnosis is performed using random forest (RF), K-nearest neighbor (K-NN), support vector machine (SVM) and Naive Bayes machine learning algorithms. [ABSTRACT FROM AUTHOR]

Snake optimizer (SO) is an optimization algorithm drawn from the reproductive habits of serpents. It exhibits outstanding effectiveness in solving continuous optimization problems. However, SO may face some performance challenges related to its population diversity and early convergence behavior. In this paper, we address the challenges of SO by introducing the Hybrid snake optimizer algorithm (HSOA). HSOA is a novel approach to optimization that incorporates two new optimization techniques into the SO algorithm. First, it incorporates a new opposition-based learning technique called Oppositional-mutual learning into the initialization stage of the SO algorithm. Second, it integrates dynamic polynomial mutation, which is an intelligent mutation method, into the initialization and optimization stages of the SO algorithm. These integrated approaches aim to increase the population's diversity of SO, while improving its searchability during its optimization stage. In power systems, the economic load dispatch (ELD) is an intricate optimization problem that becomes more challenging when the restrictions of the valve point effect (VPE) are incorporated into it. ELD with VPE is non-convex, lacking smoothness, and exhibiting nonlinearity that considers operational limitations expressed as both equality and inequality constraints to generate electricity. The suggested HSOA algorithm underwent evaluation and was compared with 47 renowned optimization algorithms across five real-world ELD problems with different specifications: generators with different unit capacities, transmission losses, prohibited operation zones, and Ramp Rate restrictions. The experimental results demonstrate that HSOA produces competitive solutions for the five real-world ELD problems. In detail, HSOA achieves the top rank in three cases of ELD problems with a 3-unit generator, and it secures the second and third positions in high-dimensional ELD problems with 40-unit and 80-unit generators, respectively. The statistical tests confirm the reliability and efficiency of HSOA. In addition, the effectiveness of HSOA was evaluated using the single-objective IEEE-CEC 2014 functions and compared to the results of eight popular metaheuristic algorithms. The results demonstrate that HSOA is a competitive optimization algorithm capable of solving the functions of IEEE-CEC 2014. [ABSTRACT FROM AUTHOR]

Evaluation of petroleum fiscal regimes for alternative selection is a challenging task in the oil and gas industry. The qualitative methods or quantitative methods which are based on static reserve estimates are problematic. A new approach incorporating dynamic reserve estimates and quadrant scale to evaluate fiscal regimes is proposed in this study. In this article, metaheuristic algorithms were applied to self-adaptively learn about the complex nature of reservoir to incorporate the changes in reservoir properties and well behaviors to generate field production profiles for building dynamic economic models. It also utilized the z-score standardization to create attractiveness–competitiveness and attractiveness–progressiveness quadrant scales. The results showed that the scales readily identified fiscal regimes that were at same time attractive, competitive, and progressive from fiscal regimes that were unattractive, noncompetitive, and regressive, and fiscal regimes that were within these two extremes. The relative movement along the scales also revealed how fiscal regimes responded as the profitability levels of the project were improved under each algorithm. The quadrant scale is a simple and easy-to-use method and could be an invaluable standard assessment tool for oil and gas decision and policymakers to improve upon investment considerations. [ABSTRACT FROM AUTHOR]

This work presents a unique technique for optimising composite laminates used as structural components, which is critical for situations where failure might result in disastrous effects. Unlike traditional surrogate‐based optimisation approaches, this methodology combines the accurate modelling capabilities of finite element (FE) analysis with the iterative refining capacity of metaheuristic algorithms. By combining these two methodologies, our method intends to improve the design process of laminated shell structures, assuring robustness and dependability is crucial. Compared to existing benchmark solutions, the current FE shows a <1% error for cylindrical and spherical shells. The prime objective of this study is to identify the optimum ply angles for attaining a high fundamental frequency. The problem is NP‐hard because the possible ply angles span a wide range (±90°), making it difficult for optimisation algorithms to find a solution. Seven popular metaheuristic algorithms, namely the genetic algorithm (GA), the ant lion optimisation (ALO), the arithmetic optimisation algorithm (AOA), the dragonfly algorithm (DA), the grey wolf optimisation (GWO), the salp swarm optimisation (SSO), and the whale optimisation algorithm (WOA), are applied to and compared on a wide range of shell design problems. It assesses parameter sensitivity, discovering significant design elements that influence dynamic behaviour. Convergence studies demonstrate the superior performance of AOA, GWO, and WOA optimisers. Rigorous statistical comparisons assist practitioners in picking the best optimisation technique. FE‐GWO, FE‐DA, and FE‐SSA methods surpass the other techniques as well as the layerwise optimisation strategy. The findings obtained, employing the GWO, DA, and SSA optimisers, demonstrate ~3% improvement over the existing literature. With respect to conventional layup designs (cross‐ply and angle‐ply), the current optimised designs are better by at least 0.43% and as much as 48.91%. [ABSTRACT FROM AUTHOR]

This article investigates a 27-degree-of-freedom dynamic model of a full-scale railway vehicle. The controlling action is performed by developing a decentralized control structure with five independent Fraction Order Proportional Integral Derivative (FOPID) controllers three types of random track irregularities, vertical profile, lateral alignment, and cross-level. A novel metaheuristic optimization technique named hybrid Particle Swarm Optimization-Gray Wolf Optimization (hybrid PSO-GWO) is proposed to optimize active force for an active suspension system. The simulated results show that the hybrid metaheuristic algorithm outperforms with a significant reduction in vehicle vibration ensuring enhanced ride comfort of the car body. [ABSTRACT FROM AUTHOR]

Stope layout optimisation is an important sub-problem of the generic underground mining optimisation problem that contributes towards optimal ore extraction. It is a growing research area as some surface mines transition from open-pit to underground mining. Several researchers acknowledge the complexity of optimising underground mine layouts because exact methods are limited in generating three-dimensional optimal solutions, hence the growing use of metaheuristic approaches to solve the problem in three dimensions. This paper presents a PRISMA-based bibliometric mapping of stope layout optimisation approaches using R-Biblioshiny and VOSviewer software, their concomitant limitations, and suggests potential future research directions in stope layout optimisation. [ABSTRACT FROM AUTHOR]

Wild Horse Optimizer (WHO) is a swarm-based meta-heuristic algorithm inspired by animal behavior, which mainly imitates the decent behavior, grazing behavior, mating behavior and leadership dominance behavior of wild horses in nature in their lives for finding the optimal. The location update method of WHO is prone to low convergence accuracy, poor global search ability and local optimum problems. With the aim of balancing global searchability and exploitation performance, an improved WHO that incorporates a dual weight starvation strategy and a random convergence factor is proposed. In the exploration stage, the starvation strategy is inspired by the starvation characteristics of animals, so that the algorithm can continuously adjust the stallion position according to the starvation level and automatically adjust the distance between the stallion and the wild horse according to the changing adaptation value, which improves the global search performance and can jump out of the local optimum at the same time. In the exploitation stage, a convergence factor is added to help it jump out of the local optimum and continue to search for a better solution. The simulation experiment on 23 benchmark functions is to verify the effectiveness of the proposed algorithm being compared with Whale Optimization Algorithm (WOA), Moth-Flame Optimization (MFO), Rat Swarm Optimizer (RSO), Multi-Verse Optimizer (MVO), Gray Wolf Optimizer (GWO) and Artificial Bee Swarm Optimizer (ALO). Finally two real engineering design problems were solved. The simulation results show that the proposed SD3WHO has a strong seeking capability and optimization performance. [ABSTRACT FROM AUTHOR]

Even though advanced heuristic and metaheuristic models have been developed for minimizing financing cost by using total float to adjust the start times of activities, these models overlook the fact that the attempt to minimize financing cost may result in more than one work schedule with minimum financing cost. It is therefore not surprising that no research has been conducted to select the best financing schedule out of the multiple optimal work schedules. It is well known that total float must be used sparingly to avoid the creation of too many critical and near-critical activities that may cause delays in the project duration. Indeed, the loss of schedule flexibility caused by the use of total float comes at a cost. It is argued in this paper that the cost of float consumption should be a vital part of models designed to minimize financing cost. The research presented in this paper attempts to fill this gap. A model is proposed in this study that not only minimizes financing cost but also demonstrates the negative impact of not considering the cost of float consumption in reaching the optimal solution, an area of research that is mostly overlooked in the finance-based scheduling literature. This research contributes to the body of knowledge by demonstrating that there may be more than one work schedule with minimum financing cost, and that the cost of float consumption must be considered to find the most optimal solution. [ABSTRACT FROM AUTHOR]

• Deep auto-encoder model is used to handle the non-linear and redundant information in dam key parameters. • A hybrid system based on intelligent optimization algorithms and deep learning for predicting tailings dam parameters. • The uncertainty analysis provides more comprehensive and precise interval predictions. • The prediction accuracy of monitoring parameters of tailings dam is significantly improved. Tailings dams, as critical infrastructure, play a vital role in ensuring the safety and reliability of tailings pond systems. Predicting the trend of tailings dam monitoring parameters helps monitor its operational status and support safety management and decision-making. The traditional time series prediction model focuses on point prediction while neglecting the nonlinear feature of data and the uncertainty of prediction results, which is far from meeting the requirements for risk monitoring and safety management of tailings dam systems. To address this, Deep Auto-Encoder is used to extract and optimize features of multi-dimensional time series; Whale Optimization Algorithm, Long Short Term Memory, and Kernel Density Estimation are used to build a monitoring parameters trend interval prediction model. It can effectively predict and analyze the uncertainty of tailings dam monitoring parameters. Time series data of monitoring parameters are collected from a tailings dam in Anhui, China and modeled to verify the effectiveness of the model. The results show that the hybrid prediction model performs the best in monitoring parameter prediction accuracy and trend. The Root Mean Square Error values of the Whale Optimization Algorithm and Long Short Term Memory prediction for reservoir water level, surface displacement, internal displacement, phreatic line, and beach width are 0.0160, 1.0405, 0.0389, 0.0176, and 0.7525, with errors reduced by 0.37 % to 17.92 % compared to other models. The uncertainty analysis confirms the high reliability of the model. The Kernel Density Estimation effectively forecasts the fluctuation range of predicted values, significantly improving the prediction effectiveness of the monitoring parameters. [ABSTRACT FROM AUTHOR]

In this study, a machine learning-based hybrid optimization method is developed to optimize tuned mass damper considering soil-structure interaction. A total of 200,000 time history analyses of the tuned mass damper-structure-soil system are conducted and the results are used to construct the database of six machine learning models. Three meta-heuristic algorithms are used for optimization. A weighted optimization algorithm is developed based on the three individual optimization algorithms. Compared with three individual optimization algorithms, the weighted optimization algorithm can achieve better optimization results. The robustness of the single ground motion-based optimization applied to other ground motions is also investigated. [ABSTRACT FROM AUTHOR]

In this study, we introduce the pied kingfisher optimizer (PKO), a novel swarm-based meta-heuristic algorithm that draws inspiration from the distinctive hunting behavior and symbiotic relationships observed in pied kingfishers in the natural world. The PKO algorithm is structured around three distinct phases: perching/hovering for prey (exploration/diversification), diving for prey (exploitation/intensification), and fostering symbiotic relations. These behavioral aspects are translated into mathematical models capable of effectively addressing a wide array of optimization challenges across diverse search spaces. The algorithm's performance is rigorously evaluated across thirty-nine test functions, which encompass various unimodal, multimodal, composite, and hybrid ones. Additionally, eight real-world engineering optimization problems, including both constrained and unconstrained scenarios, are considered in the assessment. To gauge PKO's efficacy, it is subjected to a comparative analysis against 3 categories of rival optimizers. The 1st category comprises well-established and widely-cited optimizers such as particle swarm optimization and genetic algorithm. The 2nd category encompasses recently published algorithms, including Harris Hawks optimization, Whale optimization algorithm, sine cosine algorithm, Grey Wolf optimizer, gravitational search algorithm, and moth-flame optimization. The 3rd category includes advanced algorithms, such as covariance matrix adaptation evolution strategy and Ensemble Sinusoidal Differential Covariance Matrix Adaptation with Euclidean Neighborhood (LSHADE-cnEpSin). The comparative analysis employs various performance metrics, including the Friedman mean rank and the Wilcoxon rank-sum test, to reveal PKO's effectiveness and efficiency. The overall results highlight PKO's exceptional ability to tackle intricate optimization problems characterized by challenging search spaces. PKO demonstrates superior exploration and exploitation tendencies while effectively avoiding local optima. The source code for the PKO algorithm is publicly accessible at https://www.mathworks.com/matlabcentral/fileexchange/160043-pied-kingfisher-optimizer-pko. [ABSTRACT FROM AUTHOR]

This paper proposes a metaheuristic algorithm, called advanced differential evolution (ADE), by improving the DE algorithm. The ADE algorithm was developed with the goal of creating an optimization framework that addresses the challenges of exploration and exploitation balance, avoiding local minima, utilizing chaos theory for diverse initialization, and improving solution quality and convergence speed. By incorporating these features, ADE aims to enhance the effectiveness of optimization processes. The proposed algorithm utilizes chaos theory to generate the initial population, which is subsequently divided into two sub-populations with adaptive sizes. The size of each sub-population is determined using a formula based on the number of iterations during the algorithm's execution. The first sub-population has a larger size in the beginning and the second one has a smaller size, but the total size of these two populations is always constant. The main contribution of this paper is the proposal of two novel improved differential evolution algorithms, namely MDE1 and MDE2, which are utilized for exploration within these sub-populations. The proposed ADE is tested on 29 well-known benchmarks and six engineering problems, and the results are compared with seven other algorithms. Various statistical experiments are carried out showing that the proposed algorithm provides significant superiority over other well-known algorithms. [ABSTRACT FROM AUTHOR]

Liquefaction is one of the natural hazards that occurs due to earthquakes and has a significant impact on the loss of human lives and various civil infrastructures. In this study, metaheuristic ANN with optimization techniques (i.e., ANN-GWO, ANN-GTO, ANN-GAO, ANN-HHO, ANN-SSA, and ANN-SMA), machine learning techniques are used to predict the probability of liquefaction ( P L ) from the SPT-based dataset. A dataset of 834 case histories, including seven geotechnical and seismic parameters, was used for training and testing different metaheuristic algorithms. The performance of the proposed machine learning algorithm used at every stage of analysis includes statistical parameters evaluation, score analysis, actual vs. predicted curve, error matrix, Taylor diagram, OBJ criteria, DDR criteria, and AIC criteria. The ANN-GTO model has been found to be the best model for the prediction of the probability of liquefaction potential of soil. However, all proposed models can successfully predict the liquefaction potential of soil with reasonably good accuracy. The proposed models can be used as a key tool in the prediction of the liquefaction susceptibility of any soil deposit. [ABSTRACT FROM AUTHOR]

Summary: Breast cancer is the most prevalent kind of tumor to occur in females and the primary cause of death for women. Early detection is perhaps the most successful strategy to minimize breast cancer mortality. Early diagnosis necessitates a consistent and efficient diagnostics method that allows doctors to differentiate benign from malignant breast cancers without a surgical sample. The goal of this endeavor is to develop a sophisticated breast cancer diagnosis method. The primary goal of the paper is to reduce the death rate among women by promoting early detection of breast cancer. First, pre‐processing techniques such as median filtering and contrast limiting adaptive histogram equalization are used to the obtained raw images. By doing this, the machine‐learning model's computational complexity is decreased and the image quality is enhanced. K‐means clustering is used to segregate the pre‐processed image. Additionally, features including the enhanced local vector pattern, grey‐level co‐occurrence matrix and local vector patterns are produced in the course of the feature extraction stage. Finally, an optimized deep belief network (DBN) is carrying out the classification process. To boosts the classification accuracy, activation function of DBN (tanh, softmax, ReLu) as well as its weight function is optimized by the proposed grey wolf updated whale optimization algorithm The accuracy of the greywolf updated whale optimization algorithm+DBN is above 93% for datasets 1 and 2 when compared to extant models. Finally, calculation of the performance validates the proposed model's performance. [ABSTRACT FROM AUTHOR]

Among the most significant non-linear challenges for power network design and smooth functioning of current modern updated power system networks is the optimum power flow (OPF) problem. Importance of the electrical system modeling has recently come to light due to the incremental use of energy from renewable sources in power systems networks. The goal is to use wind, solar and tidal sources to recreate the issue of OPF. In this work, Weibull, Lognormal, and also Gumbel probability distribution functions were applied to simulate the uncertainties of wind, photovoltaic, and tidal energy system. Additionally, by adding test scenarios of unpredictable wind, solar and tidal energy systems involving minimization of the cost function, loss of active power, voltage deviation, and increase stability of voltage. In accordance with the chosen thermal producing units, the solutions were evaluated using different locations using IEEE 30-bus systems of testing that incorporate renewable energy sources. The proposed power system planning problem was solved by multi-objective function where unified power flow controller are utilized as flexible AC transmission systems controllers via recently introduced optimization algorithms and the simulation outcomes of the aforementioned technique have been compared with Multi Objective Adaptive Guided Differential Evolution algorithms. The adaptive improved flower pollination algorithm (AIFPA) is a strong and reliable algorithm that is presented in this work. The AIFPA can efficiently deal with many kinds of high-complexity objective regions in multi-objective optimization situations. Utilizing an IEEE 30-bus test system, the suggested approaches' performance was examined and evaluated for a range of objective functions. The simulation results obtained from the proposed algorithm were effective in finding the optimal solution compared to the results of the meta-heuristic algorithm and were reported in the literature. [ABSTRACT FROM AUTHOR]

A control strategy must be developed to maintain the product composition of the two- or more-mixed-flow reactor series. In this research, a two-mixing tank series (TMTS) was utilized in the laboratory to approach the two-mixed flow reactors' problems. Both tanks are designed to overflow so that the volume remains constant. The salt solution was directly fed to Tank 1, while the water was charged to both tanks as the mixing progressed. The output of Tank-1 flowed to Tank-2 through a rather long pipe, resulting in dead time in Tank-2. The Smith predictor was applied to overcome dead time. The PRC (process reaction curve) method was used to tune the PID (proportional-integral-derivative) parameters of Tank-1 and Tank-2. The novel composition controls, adaptive-PID (APID) and modified metaheuristic adaptive-PID (MMAPID), have been proposed and compared with conventional PID. The mathematical models were rigorously examined through XCOS simulations. The processing system is considered sensitive to changes in input disturbances since the process time constants of both tanks were quite low. The dead time of 0.5 minutes was found in Tank 2. The PID parameters produced by PRC experiments resulted in stable responses to changes in the disturbance system. As shown in the dynamic simulation study, both novel APID and MMAPID give almost the same results; their responses are faster than the conventional PID. Based on the integral absolute error (IAE) results, APID with scheduling PID parameters produced the smallest IAE and outperformed the conventional PID. The results of the study highlight APID and MMAPID potential in PID control applications for composition control in a TMTS. The combined APID-Smith and MMAPID-Smith were able to overcome the dead time well. This study creates opportunities for engineers and professionals to use cutting-edge control technologies, improving automation and chemical process industries. [ABSTRACT FROM AUTHOR]

Cyber security is the most studied in recent years by the association of metaheuristic techniques thanks to their ability to solve optimization problems in a limited time and NP-hard problems. The Early Warning System (EWS) can identify malicious exercises and anomalies in the CPS and provide robust network systems protection. In addition, grouping attacks in EWS is important for defining defense policies. Information security in the CPS architecture is about securing information during data aggregation, large-scale processing and sharing in the network environment, especially low-linkage open networks. Motivated by these considerations, we propose a new security approach based on the Artificial Bee Colony (ABC) metaheuristic algorithm to strengthen the security framework of the CPS architecture against the system and within CPS attacks. In this work, we propose a new Early Warning System approach based on metaheuristics. Based on two fundamental concepts of clustering, coherence and separation, an integrated index was proposed based on Davies-Bouldin (DB) and Silhouette Index (SI). An improved ABC algorithm was proposed based on these indices. After having identified two different types of an objective function, based on the above concepts, and after having tested the algorithm on DBSI, the results of the experiments found are auspicious. [ABSTRACT FROM AUTHOR]

Ant colony optimization (ACO) algorithms have previously been used to compile single short scales of psychological constructs. In the present article, we showcase the versatility of the ACO to construct multiple parallel short scales that adhere to several competing and interacting criteria simultaneously. Based on an initial pool of 120 knowledge items, we assembled three 12-item tests that (a) adequately cover the construct at the domain level, (b) follow a unidimensional measurement model, (c) allow reliable and (d) precise measurement of factual knowledge, and (e) are gender-fair. Moreover, we aligned the test characteristic and test information functions of the three tests to establish the equivalence of the tests. We cross-validated the assembled short scales and investigated their association with the full scale and covariates that were not included in the optimization procedure. Finally, we discuss potential extensions to metaheuristic test assembly and the equivalence of parallel knowledge tests in general. [ABSTRACT FROM AUTHOR]

In coastal areas, coconuts are a common crop. Everyone from farmers to lawmakers and businesses would benefit from an accurate forecast of coconut production. Internet of Things (IoT) sensors are strategically positioned to continuously monitor the environment and gather production statistics to obtain accurate agricultural output predictions. To effectively estimate coconut prediction, this study presents an enhanced deep learning classifier called Bi-directional Long Short-Term Memory (BILSTM) with the integrated Lévy Flight and Seagull Optimization Algorithm (LFSOA). LASSO feature selection is applied to eliminate the superfluous characteristics in the yield estimation. To further enhance the coconut yield estimate, the optimal set of hyperparameters for BILSTM is tuned by the LFSOA, which helps to avoid the overfitting issue. For the results, the BILSTM is compared against different classifiers such as Recurrent Neural Network (RNN), Random Forest Classifier (RFC), and LSTM. Similarly, LFSOA-based hyperparameter tuning is contrasted with different optimization algorithms. The outputs show that LFSOA-based hyperparameter tuning in BILSTM achieved accuracy, precision, recall, and f1-score of 98.963%, 99.026%, 99.155%, and 95.758%, respectively, which are higher when compared to existing methods. Similarly, the BILSTM-LFSOA accomplished better results in statistical measures, including the Root Mean Square Error (RMSE) of 0.105, Mean Squared Error (MSE) of 0.011, Mean Absolute Error (MAE) of 0.094, and coefficient of determination (R2) of 0.954, respectively. From the overall analysis, the proposed BILSTM-LFSOA improves coconut yield prediction by achieving better results in all the performance measures when compared with existing models. The results of this study are important to many stakeholders, including but not limited to policymakers, farmers, banks, and insurance companies. As coconuts are an important crop in developing countries, accurate coconut yield forecasting will lead to greater financial and food security in these regions. [ABSTRACT FROM AUTHOR]

This study focuses on the delivery routing problem faced by a transport company located in Phuket, Thailand. The goal of this study is to find a daily optimum route in order to minimize the total transportation cost, which comprises fixed costs associated with vehicle rental and variable costs calculated based on factors of travel distance, fuel prices, and fuel consumption. The complexity of this problem is compounded by the fact that customer demand often exceeds a vehicle capacity, in terms of weight and volume. In addition, delivery must be made within specific time windows. To tackle this issue, the delivery routing problem is classified as a multi-trip capacitated vehicle routing problem with time window (MTCVRPTW). Since the problem is NP-hard, an application of metaheuristic is more practical to determine the delivery routing of the company within a reasonable computing time. In this study, Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithm are applied to solve MTCVRPTW. The numerical results show that DE provides better solution quality compared to those obtained from PSO and company current practices. [ABSTRACT FROM AUTHOR]

To enhance the performance of meta-heuristic algorithms, the development of new operators and the efficient combination of various optimization techniques are valuable strategies for discovering global optimal solutions. In this research endeavor, we introduce a novel optimization algorithm called PGS (Particle Swarm Optimization-GA-Sliding Surface). PGS combines the strengths of particle swarm optimization (PSO), genetic algorithm (GA), and sliding surface (SS) to tackle both mathematical test functions and real-world optimization problems. To achieve this, we adaptively tune the weighting function and learning coefficients of the PSO algorithm using the sliding mode control's SS relation. The global best particle discovered through the PSO method serves as one of the parents in the GA's crossover operation. This new crossover operator is then probabilistically integrated with an improved particle swarm optimization algorithm, enhancing convergence speed and facilitating escape from local optima. We evaluate the proposed algorithm's performance on both uni-modal and multi-modal mathematical test functions, considering un-rotated and rotated cases, thereby testing its effectiveness and efficiency against other prominent optimization techniques. Furthermore, we successfully implement the PGS algorithm in optimizing the state feedback controller for a nonlinear quadcopter system and determining the cross-section for an inelastic compression member. [ABSTRACT FROM AUTHOR]

Anne Tyng's notion of metamorphology is introduced in relation to three of the architect's residential space structures. The interpretation of this body of work is viewed through the lens of the octetruss to examine the evolution of Tyng's design approach where the interplay between Platonic geometries and their transformations demonstrates exponential formal and functional opportunity. This analysis provides a foundation for a constructive theory on Tyng's space structures. The theory is constructive in two directions. First, in that it defines a lattice of spatial relations and their transformations to reconceptualize the houses, and second, in that these same conceptualizations motivate a shape grammar that produces prototypes for space structures rooted in Tyng's examples. Sample reconfigurations generated with the grammar are offered to support discussion on the value of a metamorphological design approach and how it recalibrates understanding of Tyng's work to inspire ongoing reinvention. [ABSTRACT FROM AUTHOR]

A location‐allocation problem faced by a company that aims to locate warehouses to supply products to a set of customers is addressed in this paper. The company's objective is to minimize the total cost of locating the warehouses and the cost due to inventory policies. However, these inventory decisions are made by a different decision‐maker. In other words, once the company makes the location decisions, the decision‐maker associated with each warehouse must determine its own order quantity. Warehouses are allowed to have a certain maximum number of backorders, which represents an extra cost for them. This situation can be modeled as a bilevel programming problem, where the upper level is associated with the company that needs to minimize the costs related to location‐allocation and the total orders of each warehouse. Each warehouse is associated with an independent lower level, in which a warehouse manager aims to minimize the total inventory cost. The bilevel problem results in a single‐objective upper‐level problem with non‐linear, multiple independent lower‐level problems, making it generally challenging to find an optimal solution. A population‐based metaheuristic under the Brain Storm Optimization algorithm scheme is proposed. To solve each non‐linear problem associated with the lower level, the Lagrangian method is applied. Both decision levels are solved in a nested manner, leading to obtaining bilevel feasible solutions. To validate the effectiveness of the proposed algorithm, an enumerative algorithm is implemented. A set of benchmark instances has been considered to conduct computational experiments. Results show that optimality is achieved by the proposed algorithm for small‐sized instances. In the case of larger‐sized instances, the proposed algorithm demonstrates the same efficiency and consistent results. Finally, interesting managerial insights deduced from the computational experimentation and some proposals for future research directions are included. [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) is an advanced method of calculating power flow that involves constraints in the calculation process. OPF is an important tool in planning and operating a power system. Snake Optimization (SO), a metaheuristic optimization method that mimics the natural behavior of snakes in finding prey, surviving, and reproducing, was introduced in 2022. Despite its widespread use in various application fields, OPF calculations have never incorporated this method before. This paper proposes SO-based OPF and assets it on IEEE 30 buses, 41 lines, and 6 generators under five distinct scenarios, and compares it with Devolved PSO (DPSO), Bird Swarm Algorithm (BSA), Jaya, Hybrid Jaya BSA (HJBSA), Aquila Optimizer-Arithmetic Optimization (AO_AOA), and Teaching Learning Based Optimization (TLBO) methods. The SO-based OPF method demonstrates its superiority throughout the simulations by offering better results in retrenching fuel costs, lowering the maximum voltage stability index (Lindex), minimizing power losses, and reducing emissions to 784.9200 $/h, 0.0856, 2.7671 MW, and 1126 ton/h, respectively, while lessening the voltage deviation to 0.4088 p.u. Hence, the SO method contributes a new and excellent solution for solving OPF problems. [ABSTRACT FROM AUTHOR]

This paper presents a novel bio-inspired metaheuristic algorithm termed the Fossa Optimization Algorithm (FOA), which emulates the natural hunting behavior of the fossa in its habitat. FOA draws its core inspiration from the two-stage hunting technique of the fossa, involving an initial attack on a spotted lemur followed by a pursuit through the trees. The theoretical framework of FOA is elaborated, and its implementation is mathematically modeled in two distinct phases: (i) exploration, which simulates the fossa's positional adjustments during the initial attack on the lemur, and (ii) exploitation, which models the positional changes of the fossa during the chase. The efficacy of FOA is tested against twenty-two constrained optimization problems from the CEC 2011 test suite, as well as four engineering design challenges. The optimization results demonstrate FOA's strong capabilities in both exploration and exploitation, maintaining a balance that facilitates convergence to optimal solutions. FOA's performance is benchmarked against twelve established algorithms, showing that it consistently outperforms its competitors by delivering superior results and ranking as the top optimizer in most of the evaluated functions. These findings indicate that FOA is highly effective in addressing optimization tasks in real-world scenarios. [ABSTRACT FROM AUTHOR]

Economic dispatch (ED) problem is a popular optimization problem in electrical engineering. Many studies that handled ED problem employed metaheuristics as the optimization technique. Unfortunately, these metaheuristics are old or the existing ones. Meanwhile, ED problem is not the favourite constrained case in studies that introduced a novel metaheuristic. This research introduces a novel metaheuristic called quadratic time optimization (QTO). QTO comprises three sequential searches where both diversification and intensification are blended in every search. But the portion of both orientations changes by following the quadratic time. The appraisal of QTO is performed by employing it to solve both unconstrained and constrained problems. The 23 traditional functions represent the unconstrained problem while economic load dispatch (ELD) problem with valve point effect and spinning reserve problem represents the constrained problem. In both appraisals, there are five novel metaheuristics represent the benchmarks, including: walrus optimization algorithm (WaOA), coati optimization algorithm (COA), total interaction algorithm (TIA), addax optimization algorithm (AOA), and language education optimization (LEO). The result shows the competitiveness of the proposed QTO in handling both unconstrained and constrained problems. QTO becomes the first best in 8 functions out of 23 functions including five high dimension unimodal functions, two high dimension multimodal functions, and one fixed dimension multimodal functions. The result also shows that QTO is competitive in three use cases in ELD problems as the ratio between the range and average total cost among metaheuristics for the first, second, and third cases is 0.72, 0.04, and 0.02 percent consecutively. [ABSTRACT FROM AUTHOR]

AC model is known as the most accurate model for transmission expansion planning (TEP) problem. However, finding an optimal solution is still a difficult task due to the complex, non-linear, and non-convex nature of the ACTEP. In this study, a new hybridization of Differential Evolution (DE) and Zebra Optimization Algorithm (ZOA) algorithms, called DE-ZOA, is proposed to solve the ACTEP problem. The main idea of the hybrid algorithm is to improve the exploitation strategy of the DE algorithm based on the best individual in the population, which is inspired by the ZOA algorithm. The results given by the proposed DE-ZOA algorithm are compared to the original DE, ZOA, and other existing algorithms in both Graver 6 bus and IEEE 24 bus systems. The results show that the total cost obtained by the proposed DE-ZOA algorithm is smaller than 44.33% and 0.66% compared to the ZOA and DE algorithms in IEEE 24 bus systems. The proposed method illustrates the effectiveness of utilizing DE-ZOA algorithm to find optimal solution for the ACTEP problem. [ABSTRACT FROM AUTHOR]

Traveling has become an essential need for people to fulfill their psychological needs. Generally, tourists want to visit a new destination for several days. To get route guidance (visiting schedule), tourists usually use the services of a travel agent, but this service cannot be tailored to the tourist's wishes. In previous research, many have concluded that one-day and multi-day tourist routes are analogous to the Traveling Salesman Problem (TSP). However, this study has yet to emphasize daily optimization for multi-day routes because daily routes are only cut based on time constraints. One possible approach to optimize tourist routes per day is the analogy of solving Vehicle Routing Problem (VRP). Therefore, in this research, we propose a new model that combines the Whale Optimization Algorithm (WOA) with a Variable Neighborhood Search (VNS) strategy known as WOA-VNS to recommend multi-day tourist routes, which is analogous to the VRP to overcome deficiencies with the TSP analogy. The number of vehicles corresponds to the number of days tourists visit, thus ensuring optimal daily routes. The system considers user preferences for popularity, ratings, and time using the concept of Multi-Attribute Utility Theory (MAUT). The MAUT value are used as WOA-VNS fitness values. Five metrics (fitness value, number of Point of Interest (POI)s, trip duration, cost, and rating attributes) were tested on five random POIs. Results show the VRP analog is more suitable for multi-day routes, with WOA-VNS-VRP outperforming WOA-VNS-TSP and conventional algorithms such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Bat Algorithm (BA), achieving value average fitness of 0.8570, on the tourist location dataset in Yogyakarta. [ABSTRACT FROM AUTHOR]

Cardio Vascular Diseases (CVD) has become a serious concern for humans as fatalities rate due to CVD are increasing at an alarming pace. With the aid of machine learning techniques, heart illnesses can be predicted much earlier, and therapy or dietary changes can prevent deaths. By combining predictions from various individual models, the machine learning technique known as ensemble learning improves forecasting accuracy and resiliency. In this work, a Super Learner Ensemble Model is used where the base learners are a diverse combination of linear, probabilistic, bagging, boosting and stacking models. To improve the performance of the Super Learner Ensemble Model, an Optimized Super Learner Ensemble Model (OSLEM) is proposed, where optimal selection of base learners in the ensemble is done based on the pairwise disagreement accuracy diversity measure of classifiers in each best fitness whale obtained by different iterations of Whale Optimization Algorithm (WOA). ModifiedBoostARoota (MBAR), a wrapper feature selection technique is used to choose the most significant features of six different heart datasets and the proposed OSLEM modelled on the selected features exhibits high performance compared to other existing ensemble models. [ABSTRACT FROM AUTHOR]

Supply Chain Management (SCM) applications represent real-world optimization tasks that require handling using appropriate optimization techniques. Metaheuristic algorithms are powerful optimization tools that are effective for solving complex optimization problems such as SCM. In this article, a new metaheuristic algorithm named Potter Optimization Algorithm (POA) is introduced to deal with optimization problems, especially in SCM applications. POA is mathematically modelled by the inspiration of the human process of pottery in two phases of exploration and exploitation. The exploration phase is designed based on mathematical modeling of making extensive changes to the clay (or other pottery materials) according to the given pattern. The exploitation phase is designed based on mathematical modelling of making precise and limited changes on the made pottery with the aim of creating more similarity to the given pattern. The effectiveness of the proposed POA approach to address real-world applications in SCM has been evaluated on sustainable lot size optimization. The optimization results show that POA has been able to provide effective solutions for sustainable lot size optimization case studies by managing exploration, exploitation, and balancing them during the search process at both global and local levels. In addition, the results obtained from the implementation of POA have been compared with the performance of twelve well-known metaheuristic algorithms. The analysis of the optimization results shows that POA has 100% superior performance compared to competing algorithms by providing better results in all ten case studies. [ABSTRACT FROM AUTHOR]

In this paper, to synthesize grade uncertainty into the strategic mine schedule, a stochastic mixed-integer programming framework (SMIP) is presented for the long-term production scheduling optimization problem (LTPSOP) in open-pit mines. The objective function maximizes the net present value and minimizes the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. The current study has proposed a hybrid model to solve the LTPSOP with the incorporation of augmented Lagrangian relaxation (ALR) and the human mental search metaheuristic algorithm (HMS). The ALR function is formulated and the HMS is used to update the Lagrange multipliers. Besides, a Gaussian process machine learning method is applied to estimate the grade in a mineral deposit. The results obtained from a supercomputer have indicated that the ALR–HMS method conducted on real case studies generates 18.29% and 31.56% more cumulative net present value; 9.75% and 13.98% larger pit size than the conventional method, respectively. Finally, while improving the solvability and optimality gap, the analysis of the outcomes has demonstrated that the proposed model yields a near-optimal solution with a rational time. [ABSTRACT FROM AUTHOR]

Economic dispatch is an important issue in the management of power systems and is the current focus of specialists. In this paper, a new metaheuristic optimization algorithm is proposed, named Social Small Group Optimization (SSGO), inspired by the psychosocial processes that occur between members of small groups to solve real-life problems. The starting point of the SSGO algorithm is a philosophical conception similar to that of the social group optimization (SGO) algorithm. The novelty lies in the introduction of the small group concept and the modeling of individuals' evolution based on the social influence between two or more members of the small group. This conceptual framework has been mathematically mapped through a set of heuristics that are used to update the solutions, and the best solutions are retained by employing a greedy selection strategy. SSGO has been applied to solve the economic dispatch problem by considering some practical aspects, such as valve-point loading effects, sources with multiple fuel options, prohibited operating zones, and transmission line losses. The efficiency of the SSGO algorithm was tested on several mathematical functions (unimodal, multimodal, expanded, and composition functions) and on power systems of varying sizes (ranging from 10-units to 1280-units). The SSGO algorithm was compared with SGO and other algorithms belonging to various categories (such as: evolution-based, swarm-based, human behavior-based, hybrid algorithms, etc.), and the results indicated that SSGO outperforms other algorithms applied to solve the economic dispatch problem in terms of quality and stability of the solutions, as well as computation time. [ABSTRACT FROM AUTHOR]

Accurate estimation of reference evapotranspiration (ET0) is a crucial parameter in implementing precise irrigation strategies and managing regional water resources effectively. While various methods have been proposed to obtain accurate ET0, the conventional approach is complex, uneconomical, and unable to contend with the rising variability and unpredictable weather patterns. Meanwhile, the lack of meteorological data limits the accurate estimation of ET0 via empirical models. Considering the recent approach in coupling ML techniques with optimisation algorithms to enhance the accuracy and robustness of ET0 estimation, this study was conducted to explore the performance of optimised hybrid Support Vector Regression (SVR) models integrated with meta-heuristic algorithms for daily ET0 estimation in Malaysia. Four hybrid SVR models, including SVR-Particle Swarm Optimisation (SVR-PSO), SVR-Whale Optimisation Algorithm (SVR-WOA), SVR-Differential Evolution (SVR-DE), and SVR-Covariance Matrix Adaptation Evolution Strategy (SVR-CMAES), were developed and assessed using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2), and Global Performance Index (GPI). The accuracy of the hybrid SVR models was then compared against standalone Machine Learning (ML) and empirical models using limited meteorological data. Accordingly, the findings highlighted the superior accuracy of the SVR-PSO model in estimating ET0, followed closely by the SVR-DE and SVR-CMAES models. The outstanding performance of the SVR-PSO model was attributed to the inherent versatility and robustness of PSO, as well as its core social behaviour and swarm intelligence principles that allow for an exhaustive exploration of the solution space, thus enhancing the model's accuracy and reliability. In conclusion, the integration of SVR with the meta-heuristics algorithm represents a significant advancement in ET0 estimation models with enhanced accuracy. The study underlines the critical role of advanced hybrid models in enhancing ET0 prediction accuracy, thereby supporting the implementation of efficient water resource management and strategic planning across Malaysia. [ABSTRACT FROM AUTHOR]

Metaheuristic methods have emerged as powerful tools for solving complex optimization problems in various domains, including the sustainability of water distribution systems. They provide efficient and effective solutions by mimicking natural processes and searching for the optimal option within a large solution space. Despite the existence of these methods in the water distribution field for several years, a direct comparison between the various proposed solutions often proves challenging, due to the different parameter definitions used by the authors. The present review presents the solutions proposed by a total of 36 research papers taken from the Web of Science, Scopus and Google Scholar databases focusing on the application of metaheuristic methods for leakage reduction and energy saving in water distribution networks. The review is intended to facilitate comparative analysis among the solutions proposed by authors concerning key aspects of the optimization process. These aspects include the definition of the algorithm, the specification of the objective function, and the strategies employed for reducing the search space. The characteristics of the networks used as case studies by the reviewed papers are also presented to allow the reader to evaluate the applicability of the solutions to specific networks. [ABSTRACT FROM AUTHOR]

Outdoor air pollution causes a lot of health problems for humans. Particulate Matter 2.5 (PM2.5), due to its small size, can enter the human respiratory system with ease and cause significant health effects on humans. This makes PM2.5 significant among the various air pollutants. Hence, it is important to measure the value of PM2.5 accurately for better management of air quality. Algorithms for deep learning and machine learning can be used to forecast air quality data. A model that minimizes the prediction error of the PM2.5 forecast is needed. In this paper, a PM2.5 concentration estimation model using Bi-LSTM (Bidirectional Long Short-Term Memory) with meteorological data as predictor variables is proposed. For a better estimation of PM2.5 values, the hyperparameters of the Bi-LSTM model used are tuned using the Osprey Optimization Algorithm (OOA), a recent meta-heuristic algorithm. The model that works with the optimal values of hyperparameters identified by OOA performed better than the other models when they are compared based on evaluation metrics like Mean-Squared Error and R². [ABSTRACT FROM AUTHOR]

Concrete is the most commonly used construction material. However, its production leads to high carbon dioxide (CO2) emissions and energy consumption. Therefore, developing waste-substitutable concrete components is necessary. Improving the sustainability and greenness of concrete is the focus of this research. In this regard, 899 data points were collected from existing studies where cement, slag, fly ash, superplasticizer, coarse aggregate, and fine aggregate were considered potential influential factors. The complex relationship between influential factors and concrete compressive strength makes the prediction and estimation of compressive strength difficult. Instead of the traditional compressive strength test, this study combines five novel metaheuristic algorithms with extreme gradient boosting (XGB) to predict the compressive strength of green concrete based on fly ash and blast furnace slag. The intelligent prediction models were assessed using the root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), and variance accounted for (VAF). The results indicated that the squirrel search algorithm-extreme gradient boosting (SSA-XGB) yielded the best overall prediction performance with R2 values of 0.9930 and 0.9576, VAF values of 99.30 and 95.79, MAE values of 0.52 and 2.50, RMSE of 1.34 and 3.31 for the training and testing sets, respectively. The remaining five prediction methods yield promising results. Therefore, the developed hybrid XGB model can be introduced as an accurate and fast technique for the performance prediction of green concrete. Finally, the developed SSA-XGB considered the effects of all the input factors on the compressive strength. The ability of the model to predict the performance of concrete with unknown proportions can play a significant role in accelerating the development and application of sustainable concrete and furthering a sustainable economy. [ABSTRACT FROM AUTHOR]

Traditional health care relies on biomedical image categorization to identify and treat various medical conditions. In machine learning and medical imaging, biomedical image classification for colon and lung cancer diagnosis is significant. The work focuses on building novel models and algorithms to accurately detect and categorize tumorous lesions using computer tomography (CT) scans and histopathology slides. These systems use image processing, deep learning (DL), and convolutional neural networks (CNN) to assist medical professionals diagnose cancer sooner and improve patient outcomes. Biomedical image classification using seagull optimization with deep learning (BIC-SGODL) addresses colon and lung cancer diagnosis. The BIC-SGODL method improves cancer diagnosis using hyperparameter optimized DL model. BIC-SGODL utilizes DenseNet to learn complicated features. The convolutional long short-term memory (CLSTM) standard captures spatiotemporal information in sequential picture data. Finally, the SGO method adjusts hyperparameters to improve model performance and generalization. BIC-SGODL performs well with biomedical image dataset simulations. Thus, medical picture cancer diagnosis may be automated using BIC-SGODL. [ABSTRACT FROM AUTHOR]