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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]

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]

The integration of cloud and edge computing, along with machine learning, plays a vital role in the development of efficient healthcare systems in smart cities. However, machine and deep learning (DL) models are prone to delayed convergence and Type-I and Type-II errors due to data vastness and high degree imbalance. To overcome the shortcomings of previous frameworks, this work aims to propose an optimization method with DL, 'Deep-Chaotic Nature Whale Optimization' (Deep-CNWO) for early prediction of Blood Pressure disorders among patients under at-home supervision. A simplex search algorithm is integrated to improve the update mechanism of whale optimization algorithm (WOA), thereby creating a CNWO algorithm. The purpose of this hybrid optimization is to increase the accuracy and efficiency of DL models. Leveraging the power of DL and CNWO, this method (Deep-CNWO) provides an effective solution for early detection and proactive management of a chronic disease in at-home healthcare settings. We collected relevant data from clinical studies, including vital signs and patient contextual information, to train and evaluate the deep-CNWO model. The CNWO optimization approach has been used to improve the predictive performance and convergence of DL models. Experiments performed on imbalanced datasets using deep-CNWO have given 99.90% accuracy. The average F-score for emergency cases has improved by 22%, while the average accuracy has increased by 5.72% across all three classes, compared to the results reported in previous related work. Deep-CNWO improves the convergence of DL and reduces Type-I and Type-II errors. The experimental results demonstrate the efficacy of our proposed method for remote patient monitoring and highlight its potential for quick intervention during emergencies. [ABSTRACT FROM AUTHOR]

This study explores crop yield forecasting through weight agnostic neural networks (WANN) optimized by a modified metaheuristic. WANNs offer the potential for lighter networks with shared weights, utilizing a two-layer cooperative framework to optimize network architecture and shared weights. The proposed metaheuristic is tested on real-world crop datasets and benchmarked against state-of-the-art algorithms using standard regression metrics. While not claiming WANN as the definitive solution, the model demonstrates significant potential in crop forecasting with lightweight architectures. The optimized WANN models achieve a mean absolute error (MAE) of 0.017698 and an R-squared ( R 2 ) score of 0.886555, indicating promising forecasting performance. Statistical analysis and Simulator for Autonomy and Generality Evaluation (SAGE) validate the improvement significance and feature importance of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, a new algorithm named the improved transient search optimization algorithm (ITSOA) is utilized to solve classical test functions, optimize the consumption of building energy, and optimize hybrid energy system production. The conventional TSOA draws inspiration from the fleeting behavior of electrical circuits with energy storage components. Rosenbrock's direct rotation technique is used to improve the traditional TSOA performance against exploration and exploitation unbalance. First, the ITSOA performance is investigated in solving 23 classical benchmark functions, and the outcomes have shown the superior capability of the recommended algorithm in comparison with the conventional TSOA, DMO, SHO, GA, MRFO, and PSO methods. Also, the ITSOA proficiency is verified in solving the building energy optimization (BEO) problem for minimizing the energy usage of two simple and detailed buildings. The optimization results showed that the optimized solutions of ITSOA in single and multi-objective optimizations compared to conventional TSOA, DMO, SHO, GA, MRFO, and PSO obtained a lower value of the cost function. Also, the superiority of ITSOA has been confirmed to solve the BEO compared to previous methods. Moreover, the multi-objective optimization results have shown that ITSOA is able to determine the ultimate solution among the Pareto front set based on the fuzzy decision-making approach and building energy utilization decisions. [ABSTRACT FROM AUTHOR]

High-utility itemsets mining (HUIM) is an important research topic in data mining that consists of extracting efficient itemsets that generate high profit in databases. Metaheuristic algorithms are good candidates for finding some sufficiently good high-utility itemsets (HUIs) while reducing the prohibitive computational cost incurred by such mining. However, metaheuristic algorithms suffer from crucial issues: runtime and memory consumption, entailing scalability issues. To contribute in alleviating these drawbacks, we explore a hybrid approach combining metaheuristics with an optimization technique, and experimentally show the obtainment of better quality solutions. The exploration component in our approach relies on the crow search algorithm (CSA) which is a method that presents a strong capacity in the global search process. The exploitation component is based on the particle swarm optimization (PSO) which is an established stochastic optimization technique known to have a good convergence capability. This article proposes the HUIM-ICSO algorithm, coupling CSA and PSO, as a hybrid method for mining HUIs. In addition to the proposed main CSA-based algorithm, two additional novel enhanced variants are incorporated and integrated with PSO. Experiments are undertaken on 12 publicly available datasets and compared with state-of-the-art algorithms, such as mHUIMiner, HUIM-SPSO and HUIM-AF. Experiments show that, as the number of transactions or items grows, the suggested approach remains scalable and delivers favorable outcomes in terms of runtime, memory usage, and the quantity of HUIs mined, all while maintaining a good convergence rate. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) is a population-based metaheuristic search algorithm that exhibits excellent performance. However, it is sensitive to mutation strategies and control parameters.To mitigate the impact of these factors, this paper proposes refining differential evolution with mutation rate and neighborhood weight local search(MRNLDE). The algorithm guides individuals in selecting an appropriate mutation mode through the mutation rate, effectively utilizing the evolutionary information inherent in the individuals themselves, thereby enhancing the search efficiency of the algorithm. Furthermore, the neighborhood individual information is utilized to re-explore the solution space, allowing for a comprehensive exploration of individual evolutionary potential and enhancing the algorithm's diversity. The performance of MRNLDE was validated under two sets of benchmark problems at the Institute of Electrical and Electronics Engineers (IEEE) Conference on Computing in Evolution (CEC), and the results show that MRNLDE performs well overall. [ABSTRACT FROM AUTHOR]

This paper proposes a jellyfish search algorithm (JSA)-based offline method to design a DC voltage controller of AC/DC front-end converter (FEC) in a doubly fed induction generator (DFIG)-based wind energy conversion system (WECS). First, a multi-objective tuning problem is formulated using a full-order converter model to achieve maximum utilization of converter parameters in controller design. Next, a normalized mono-objective problem is developed using a utopia-tracking approach to reduce the computational complexity in design. Finally, the mono-objective problem is solved using JSA to enhance the controller performance by obtaining optimal gain values for the controller. A comparative study is performed with other metaheuristic optimization techniques to justify the use of JSA in this application. The results confirm that the accuracy of the JSA-based approach is higher, whereas the execution time of the method is lower than the other methods. Furthermore, to validate the performance of the proposed approach, a DFIG-based WECS is modeled and simulated using OPAL-RT real-time simulator. A grid voltage-oriented vector control is used as the overall control algorithm for the FEC. Unlike the conventional pole-zero cancellation method, the proposed approach provides no voltage overshoot during voltage development. Compared to the published literature, it also ensures 40.65% lesser voltage deviation without any overcurrent, irrespective of wind speed and load variations. [ABSTRACT FROM AUTHOR]

Enhancing aerodynamic efficiency is vital for optimizing aircraft performance and operational effectiveness. It enables greater speeds and reduced fuel consumption, leading to lower operating costs. Hence, the implementation of Gurney flaps represents a promising avenue for improving airfoil aerodynamics. The optimization of Gurney flaps holds considerable ramifications for improving the lift and stall characteristics of airfoils in aircraft and wind turbine blade designs. The efficacy of implementing Gurney flaps hinges significantly on its design parameters, namely, flap height and mounting angle. This study attempts to optimize these parameters using a design optimization framework, which incorporates training a Radial Basis Function surrogate model based on computational fluid dynamics (CFD) data from two-dimensional (2D) Reynolds-Averaged Navier–Stokes (RANS) simulations. The Cuckoo search algorithm is then employed to obtain the optimal design parameters and compared with other competing optimization algorithms. The optimized Gurney flap configuration shows a notable improvement of 10.28% in C l / C d , with a flap height of 1.9%c and a flap mounting angle of −58 ∘ . The study highlights the effectiveness of the proposed design optimization framework and furnishes valuable insights into optimizing Gurney flap parameters. The comparison of metaheuristic algorithms serves to enhance the study's contribution to Gurney flap design optimization. [ABSTRACT FROM AUTHOR]

Many new metaheuristic algorithms prioritize their search strategy phase, often neglecting equally critical stages like initialization. Latin hypercube sampling (LHS) is one technique that stands out in this context. LHS selects representative samples through permutations in a multidimensional space, effectively preventing the clustering of points in specific areas. However, its limitations become apparent in high-dimensional problems, as it fails to provide crucial search space information and struggles to determine samples across all dimensions. Addressing these challenges, this paper introduces an innovative population initialization approach, combining the strengths of LHS with evolutionary behaviors. This technique is divided into two main sections: spatial and quality. The spatial section divides the search space into equal intervals across each dimension to establish initial solutions. Meanwhile, the quality section employs evolutionary strategies like mutation and crossover. These strategies serve a dual purpose: they explore the search space thoroughly and refine solutions, bringing them closer to the objective function. To validate the effectiveness of this method, these principles have been integrated it into the classic Differential Evolution algorithm. We conducted extensive tests using 30 representative benchmark functions to assess its performance. The experimental results are encouraging; our methodology not only speeds up convergence but also enhances solution quality, outperforming other similar techniques. [ABSTRACT FROM AUTHOR]

Digital twin network (DTN) as an emerging network paradigm, have garnered growing attention. For large-scale networks, a crucial problem is how to effectively map physical networks onto the infrastructure platform of DTN. To address this issue, we propose a heuristic method of the adaptive boundary whale optimization algorithm (ABWOA) to solve the digital twin network construction problem, improving the efficiency and reducing operational costs of DTN. Extensive comparison experiments are conducted between ABWOA and various algorithms such as genetic algorithm, particle swarm optimization, artificial bee colony, differential evolution algorithm, moth search algorithm and original whale optimization algorithm. The experimental results show that ABWOA is superior to other algorithms in terms of solution quality, convergence speed, and time cost. It can solve the digital twin network construction problem more effectively. [ABSTRACT FROM AUTHOR]

Arithmetic optimization algorithm (AOA) is a meta-heuristic optimization method based on mathematical operators proposed in recent years. Although it has good performance, it can also lead to insufficient local search ability and falling into local optima when solving complex optimization problems. In order to make up for the above shortcomings, the optimization performance of AOA is further improved. This paper proposes a hybrid algorithm based on AOA and particle swarm optimization (PSO) called HAOAPSO. Firstly, a compound opposition-based learning (COBL) strategy is introduced to broaden the scope of finding optimal solutions to help the algorithm better jump out of local optima. Secondly, PSO is combined with AOA that integrates COBL to improve the algorithm's local search ability, so as to improve the overall search efficiency of the algorithm. In addition, experiments are performed on 23 classical benchmark functions with different characteristics and five engineering design optimization problems, and the experimental results of HAOAPSO are compared with those of other well-known optimization algorithms to comprehensively evaluate the performance of the proposed algorithm. The simulation results show that HAOAPSO can provide better solutions in most cases when solving global optimization problems such as engineering, with better convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

The stochastic fractal search (SFS) algorithm, among population-based metaheuristic automation algorithms, is a robust optimization algorithm for solving optimization problems in different fields of science, inspired by the diffusion feature and natural growth phenomenon seen regularly in random fractals. However, as in population-based optimization algorithms, it is a great challenge to effectively design the selection process in the SFS method. To imitate the selection process in nature effectively and accurately, the dynamic-fitness-distance-balance (dFDB) selection method has been used in the SFS algorithm in six different versions. In this way, the dFDB-SFS algorithm has been developed, which more effectively mimics nature with exploitation, exploration, and balanced search capabilities. Firstly, the performance of the proposed dFDB-SFS algorithm was investigated in CEC 2020 benchmark test functions. Wilcoxon and Friedman statistical analyses of the results obtained from the test functions were made and according to these analysis results, the best version of the proposed approach was determined. Secondly, the performance of the proposed algorithm was investigated in determining photovoltaic module parameters, which is one of the real-world engineering problems. In this article, the dFDB-SFS algorithm uses the root mean square error (RMSE) as the objective function to estimate the unknown parameters of the single diode model (SDM), double diode model (DDM), and PV module models. In terms of a quantitative and qualitative performance evaluation, it reveals that the proposed algorithm provides better results than other proposed algorithms in terms of accuracy and robustness when obtaining PV parameters. [ABSTRACT FROM AUTHOR]

In a local search algorithm, one of its most important features is the definition of its neighborhood which is crucial to the algorithm's performance. In this paper, we present an analysis of neighborhood combination search for solving the single-machine scheduling problem with sequence-dependent setup time with the objective of minimizing total weighted tardiness (SMSWT). First, We propose a new neighborhood structure named Block Swap (B1) which can be considered as an extension of the previously widely used Block Move (B2) neighborhood, and a fast incremental evaluation technique to enhance its evaluation efficiency. Second, based on the Block Swap and Block Move neighborhoods, we present two kinds of neighborhood structures: neighborhood union (denoted by B1⋃B2) and token-ring search (denoted by B1 → B2), both of which are combinations of B1 and B2. Third, we incorporate the neighborhood union and token-ring search into two representative metaheuristic algorithms: the Iterated Local Search Algorithm (ILSnew) and the Hybrid Evolutionary Algorithm (HEAnew) to investigate the performance of the neighborhood union and token-ring search. Extensive experiments show the competitiveness of the token-ring search combination mechanism of the two neighborhoods. Tested on the 120 public benchmark instances, our HEAnew has a highly competitive performance in solution quality and computational time compared with both the exact algorithms and recent metaheuristics. We have also tested the HEAnew algorithm with the selected neighborhood combination search to deal with the 64 public benchmark instances of the single-machine scheduling problem with sequence-dependent setup time. HEAnew is able to match the optimal or the best known results for all the 64 instances. In particular, the computational time for reaching the best well-known results for five challenging instances is reduced by at least 61.25%. [ABSTRACT FROM AUTHOR]

Several optimization-based population search methods have been proposed; they use various operators that permit exploring the search space. These methods typically suffer from local search (LS) problems and are unbalanced between exploration and exploitation. Consequently, recent researchers sought to modify the algorithms to avoid search problems using local search techniques to intensify the exploitation when is necessary. This paper proposes a novel single-based local search optimization algorithm called the non-monopolize search (NO). The NO is a single-solution metaphor-free algorithm, and its operators are designed based to explore and exploit along the iterative process. The NO works only with a candidate solution, and the operators modify the dimension to move the current solution along the search space. The NO is an effective LS method that combines the benefits of exploration with exploitation. Different from other LS, the NO can escape from suboptimal solutions thanks to the randomness incorporated into its operators. This is the main advantage of the NO. Experiments are conducted on standard benchmark functions to validate the performance of the proposed non-monopolize search optimization technique. The results are compared with other well-known methods, and the proposed NO got better results. Moreover, the proposed NO can be considered a powerful alternative to improve the optimization algorithms' performance and help avoid local search problems. Source codes of NO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/156154-the-non-monopolize-search-no. [ABSTRACT FROM AUTHOR]

UWB positioning can be modeled as an optimization problem for a fitness function, which is resolved through the conventional whale optimization algorithm. The fitness function used in the conventional whale positioning algorithm does not use the range measurement data between labels, and the range measurement data is incomplete, which leads to a limited improvement in its positioning accuracy. Consequently, a novel whale cooperative positioning algorithm that can increase positioning accuracy is presented in this paper. First, a fitness function is established during the positioning process that contains the range measurement data between the labels to improve positioning accuracy. Then, the conventional whale positioning method provides a good initial position for the labels, enabling the proposed algorithm to quickly search for the optimal position. Finally, Levy flight mode is adopted to prevent the positioning result from converging to the local optimal solution. According to simulation results, the proposed algorithm can provide better positioning accuracy than the conventional whale positioning algorithm while realizing simultaneous positioning of all the labels. [ABSTRACT FROM AUTHOR]

Multi-label text classification (MLTC) is a popular method for organizing electronic documents, which is crucial for accessing and processing data. As the number of classes increases, learning multi-label data will be challenging. The number of possible states for various labels increases exponentially, and learning algorithms in single-label data cannot be used to solve these problems. In the meantime, using single-label data algorithms could be very time-consuming. In MLTC, complexity costs should be reduced. Deep-learning neural networks that can learn intricate patterns are used in many real-world problems because of their high power and accuracy. This paper proposed a hybridization of the long short-term memory (LSTM) neural network and the convolutional neural network (CNN) method for MLTC. The proposed model uses LSTM to enhance CNN to improve the proposed model's accuracy. Also, the competitive search algorithm (CSA) is used to improve the LSTM hyperparameters. The LSTM hyperparameters play an important role in increasing the detection accuracy. The CSA algorithm finds the best values for the hyperparameters by searching the problem space. It was tested on four different datasets of multi-label texts: Reuters-21578, RCV1-v2, EUR-Lex, and Bookmarks. The result showed that the proposed model performed better than CNN and LSTM-CSA in terms of accuracy percentage and that it has improved by an average of more than 10%. Also, the results show that the LSTM-CSA model has higher detection accuracy compared to LSTM—Gradient-based optimizer (GBO) and LSTM—whale optimization algorithm (WOA). [ABSTRACT FROM AUTHOR]

Power supply from renewable energy is an important part of modern power grids. Robust methods for predicting production are required to balance production and demand to avoid losses. This study proposed an approach that incorporates signal decomposition techniques with Long Short-Term Memory (LSTM) neural networks tuned via a modified metaheuristic algorithm used for wind power generation forecasting. LSTM networks perform notably well when addressing time-series prediction, and further hyperparameter tuning by a modified version of the reptile search algorithm (RSA) can help improve performance. The modified RSA was first evaluated against standard CEC2019 benchmark instances before being applied to the practical challenge. The proposed tuned LSTM model has been tested against two wind production datasets with hourly resolutions. The predictions were executed without and with decomposition for one, two, and three steps ahead. Simulation outcomes have been compared to LSTM networks tuned by other cutting-edge metaheuristics. It was observed that the introduced methodology notably exceed other contenders, as was later confirmed by the statistical analysis. Finally, this study also provides interpretations of the best-performing models on both observed datasets, accompanied by the analysis of the importance and impact each feature has on the predictions. [ABSTRACT FROM AUTHOR]

We study the recent metaheuristic search algorithm for the multidimensional assignment problem (MAP) using fitness landscape theory. The analyzed algorithm performs a very large-scale neighborhood search on a set of feasible solutions to the problem. We derive properties of the landscape graphs that represent these very large-scale search algorithms acting on the solutions of the MAP. In particular, we show that the search graph is a generalization of a hypercube. We extend and generalize the original very large-scale neighborhood search to develop the variable neighborhood search. The new search is capable of searching even larger large-scale neighborhoods. We perform numerical analyses of the search graph structures for various problem instances of the MAP and different neighborhood structures of the MAP algorithm based on a very large-scale search. We also investigate the correlation between fitness (i.e., objective values) and distance (i.e., path lengths) of the local minima (i.e., sinks of the landscape). Our results can be used to design improved search-based metaheuristics for the MAP. [ABSTRACT FROM AUTHOR]

Vortex search (VS) algorithm is a recently proposed swarm intelligence or evolutionary algorithm for solving continuous optimization problems inspired by the behavior of whirlpool. In this study, an approach based on VS algorithm is proposed to deal with uncapacitated facility location problem (UFLP) which is a pure problem in binary domain. The update mechanism of VS algorithm is not sufficiently useful for solving the binary optimization problems; therefore, a binary form of VS method called modified binary vortex search (for short MBVS) is proposed for solving UFLPs. Three important changes have been carried out on basic VS algorithm such as (1) converting continuous values to binary values; (2) using genetic mutation operators for enhancing the exploration ability and (3) a local search mechanism for extending the exploitation ability. Based on these changes, MBVS has been tested on fifteen different UFLP instances. The UFLPs dealt with in this study is one of the famous binary optimization problems. It is widely used for comparing the performance of superior algorithms. Once an analysis of 10 different transfer functions, a genetic mutation operator, a local search parameter and population size have been made on proposed method; then, it has been compared with some binary metaheuristic methods and their variants: genetic algorithm (GA)-based approaches such as GA-SP, GA-TP and GA-UP; binary particle swarm optimization algorithm (BPSO); binary versions of artificial bee colony (ABC) algorithm such as binABC, DisABC and ABCbin; binary versions of differential evolution (DE) algorithm such as DisDe/rand and binDE and binary variants of the artificial algae algorithm (AAA) such as AAA-Tanh, AAA-Sig and binAAA methods. The experimental results and comparisons reveal that MBVS algorithm is highly competitive and robust optimizer for the problem addressed in this study. [ABSTRACT FROM AUTHOR]

Jaya is a metaheuristic algorithm that uses a pair of random internal parameters to adjust its exploration and exploitation search behaviors. Such a random setting can negatively affect the search performance of the algorithm by causing inappropriate search behavior in some iterations. To tackle this issue, the present study deals with developing a new fuzzy decision-making mechanism for dynamic adjusting the trade-off between the exploration and exploitation search behaviors of the Jaya method. The new algorithm is named Fuzzy Reinforced Jaya (FRJ) method. The search capability of the FRJ is evaluated in solving a suite of unconstrained mathematical benchmarks and constrained mechanical and structural optimization problems with buckling and natural frequency constraints. Also, the relevant decision variables are selected from both continuous and discrete domains. To provide a deeper insight into the effect of the defined auxiliary fuzzy module, the performance of the algorithm is evaluated and discussed using normalized diversity concept and behavioral diagrams. Also, employing different statistical analyses (e.g., Q–Q diagrams, Wilcoxson and Friedman tests), the significance of the outcomes is evaluated. Also, the numeric achievements are compared with six other well-stablished techniques. Attained outcomes indicate that the proposed FRJ, as a self-adaptive and parameter-free method, provides superior and promising results in the terms of stability, accuracy, and computational cost in solving mathematical and structural optimization problems. [ABSTRACT FROM AUTHOR]

This paper introduces the Golf Sport Inspired Search (GSIS) algorithm as an evolutionary search method for numerical optimization. Each solution is generated with the aid of the step-length and search direction. The step-length is determined with the aid of the Tait's model of the trajectory of the golf ball, which is a physical model. The search direction is from the current position in the search space toward the position of a different individual or its reflected position. Such a direction determines the movement direction in the optimization process. A crossover operator is introduced to increase exploration at the starting and exploitation at the ending stages of the search. Performance of the GSIS is compared with many algorithms on 23 + 14 unconstrained classic functions, 29 functions of CEC 2017 benchmark suite and six constrained engineering design problems. Experiments indicate that with the aid of its cleverly designed operators, GSIS is able to produce promising results. Besides a cooperative game theoretic approach is introduced, which is able to measure the effectiveness of different operators in reducing the search cost. Such an approach can be used to measure the effectiveness of different operators that an evolutionary or swarm-intelligence algorithm owns. [ABSTRACT FROM AUTHOR]

Artificial neural networks are widely used for solving engineering design problems of various disciplines due to its simplicity, efficiency, and adaptability. It predicts promising and accurate results. Artificial neural network solves these problems with weights and biases obtained in the training process. In training, the weights and biases have to be updated such that the difference between predicted and actual values has to be minimized. The artificial neural network uses stochastic gradient steepest descent methods to update the weights and biases for optimizing it. These methods are good at finding the optimum solution. However, they suffer from the drawbacks of vanishing gradient at local minima and critical points and are sensitive to initial weights and biases. As a result, it falls into local minima, the training time becomes high, and accuracy becomes low. One of the best solutions to overcome these problems is to use metaheuristics algorithms instead of stochastic gradient descent methods. Among metaheuristics, the cuckoo search algorithm is widely used in many applications due to its simplicity and efficiency. In this work, we proposed an improved Cuckoo search algorithm by incorporating Voronoi diagram with Cuckoo search to strengthen the weak areas of Cuckoo search and to overcome the addressed problems of the artificial neural network. The proposed Cuckoo search algorithm performance is tested on higher dimensional benchmark functions and on benchmark data sets. Moreover, its performance is compared with variants of Cuckoo search and other metaheuristic algorithms. The proposed algorithm has shown better results in terms of the number of generations, accuracy, cross-entropy, and root mean square error (RMSE). [ABSTRACT FROM AUTHOR]

A population-based metaheuristic algorithm that takes its cues from the foraging strategy of sparrows is called the sparrow search algorithm (SSA). While SSA is competitive when compared to other algorithms, it nevertheless has a propensity to carry out imbalanced exploitation and exploration and find the local optimum. Therefore, the modified adaptive sparrow search algorithm (MASSA), an SSA modification, is created to address these problems. To increase population variety, the MASSA uses a chaotic reverse learning technique. Second, to balance the exploitation and exploration capacities, a dynamic adaptive weight is added. In the end, an adaptive spiral search technique improves algorithm performance. Among 23 classical test functions, of which 13 are multidimensional and the other 10 are fixed dimensional, the best chaotic operator is found. It is proven that MASSA is superior. Simulation studies demonstrate that the MASSA described in this study is superior to previous algorithms in terms of stability, convergence speed, and convergence accuracy. Finally, a sample robot path planning problem is resolved using MASSA, and the experimental outcomes confirmed the viability and usefulness of MASSA. [ABSTRACT FROM AUTHOR]

The knapsack problem is an NP-hard combinatorial optimization problem for which it is difficult to find a polynomial-time solution. Many researchers have used metaheuristic algorithms that find a near-optimal solution in a reasonable amount of time to solve this problem. Discreteness is required in order to use metaheuristic algorithms in solving binary problems. The Artificial Jellyfish Search (AJS) algorithm is a recently proposed metaheuristic algorithm. The algorithm was created by modeling the foraging behavior of jellyfish in the ocean. AJS has been used mostly for the solution of continuous optimization problems in the literature, and studies on its performance on binary problems are limited. While this study aims to contribute to the literature by proposing a binary version of AJS (Bin_AJS) for the solution of knapsack problems, the effects of eight different transfer functions and five different mutation ratios were examined, and the ideal mutation ratio and transfer function were determined for each dataset. It was found that Bin_AJS, which was examined for two different datasets consisting of a total of forty knapsack problems, reached the optimal value in 97.5% of the problems. According to the Friedman test results, Bin_AJS ranked first in Dataset 1 and second in Dataset 2 when compared to other algorithms in the literature. All the comparisons and statistical tests showed that the algorithm is a successful, competitive, and preferable binary algorithm for knapsack problems. [ABSTRACT FROM AUTHOR]

Directional sensor networks (DSNs) are ad-hoc networks which are utilized in different industrial applications. Their usual engagements are to monitor and to perform the coverage of all specific targets in the observing fields permanently. These kinds of networks include numerous configurable directional sensors in which they can be utilized in one of the possible directions along with the one of their adjustable ranges. Although the energy harvesting methodology is being applied for these battery-hungry applications, the battery management and network lifetime maximization are still prominent challenges. In this paper, the network lifetime extension is formulated to a discrete optimization problem which is a famous non-deterministic polynomial time hardness (NP-Hard) problem. To solve this combinatorial problem, a discrete cuckoo search algorithm (D-CSA) is designed and is called in several rounds. A cover is a sub set of configured sensors capable of monitoring all targets in the observing field. In each round, the most efficient cover is constituted along with its activation time. In the determined activation time, the sensors in the cover are scheduled in wakeup mode whereas others are set in sleep mode to save energy. Despite other meta-heuristic algorithms, this proposed algorithm utilizes the novel defined discrete walking around procedures that makes to reach a good balance between exploration and exploitation in this complex search space. The proposed algorithm has been tested in different scenarios to be evaluated. The simulation results in the variety circumstances prove the superiority of the proposed algorithm is about 20.29%, 19.55%, 14.40%, 14.51%, 7.70% and 8.03% in term of average lifespan improvement against H-MNLAR, Hm-LifMax-BC, GA, ACOSC, H-GATS, and HDPSO algorithms, respectively. The results also show the high potential scalability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Cuckoo Search Algorithm (CSA) is a nature-inspired metaheuristic optimization algorithm. The optimization algorithm was inspired by the life story of a family of birds called the Cuckoo. In this paper, an application of the CSA, is implemented to estimate model parameters of deep-seated faults by using gravity anomalies. To increase the efficiency of the algorithm, parameter tuning studies are carried out for the optimum algorithm-based control parameters. In addition, the reliability and possible uncertainties of the obtained solutions were examined using probability density function analysis. The model parameters (z1, z2, α, x0 and ρ) for noisy and noise-free synthetic models were obtained by using CSA. These obtained parameters are very compatible with the true model parameters for noise-free data, similarly well-nigh compatible results for noisy data. As a result we can say that the method works successfully when applied to synthetic data. The technique is tested on three widely studied benchmark field data including gravity anomaly [Garber Fault (USA), Gazal Fault (Egypt), Mersa Matruh Fault (Egypt)]. In previous studies, various solution techniques have been used for the inversion of these field data and the compatibility of the model parameters obtained in this study with the values obtained from previous studies was compared. The results demonstrate that the CSA is a very effective and robust approach for the optimization of gravity anomalies. [ABSTRACT FROM AUTHOR]

Over recent decades, research in Artificial Intelligence (AI) has developed a broad range of approaches and methods that can be utilized or adapted to address complex optimization problems. As real-world problems get increasingly complicated, this requires an effective optimization method. Various meta-heuristic algorithms have been developed and applied in the optimization domain. This paper used and ameliorated a promising meta-heuristic approach named Crow Search Algorithm (CSA) to address numerical optimization problems. Although CSA can efficiently optimize many problems, it needs more searchability and early convergence. Its positioning updating process was improved by supporting two adaptive parameters: flight length (fl) and awareness probability (AP) to tackle these curbs. This is to manage the exploration and exploitation conducts of CSA in the search space. This process takes advantage of the randomization of crows in CSA and the adoption of well-known growth functions. These functions were recognized as exponential, power, and S-shaped functions to develop three different improved versions of CSA, referred to as Exponential CSA (ECSA), Power CSA (PCSA), and S-shaped CSA (SCSA). In each of these variants, two different functions were used to amend the values of fl and AP. A new dominant parameter was added to the positioning updating process of these algorithms to enhance exploration and exploitation behaviors further. The reliability of the proposed algorithms was evaluated on 67 benchmark functions, and their performance was quantified using relevant assessment criteria. The functionality of these algorithms was illustrated by tackling four engineering design problems. A comparative study was made to explore the efficacy of the proposed algorithms over the standard one and other methods. Overall results showed that ECSA, PCSA, and SCSA have convincing merits with superior performance compared to the others. [ABSTRACT FROM AUTHOR]

In today's data-driven digital culture, there is a critical demand for optimized solutions that essentially reduce operating expenses while attempting to increase productivity. The amount of memory and processing time that can be used to process enormous volumes of data are subject to a number of limitations. This would undoubtedly be more of a problem if a dataset contained redundant and uninteresting information. For instance, many datasets contain a number of non-informative features that primarily deceive a given classification algorithm. In order to tackle this, researchers have been developing a variety of feature selection (FS) techniques that aim to eliminate unnecessary information from the raw datasets before putting them in front of a machine learning (ML) algorithm. Meta-heuristic optimization algorithms are often a solid choice to solve NP-hard problems like FS. In this study, we present a wrapper FS technique based on the sparrow search algorithm (SSA), a type of meta-heuristic. SSA is a swarm intelligence (SI) method that stands out because of its quick convergence and improved stability. SSA does have some drawbacks, like lower swarm diversity and weak exploration ability in late iterations, like the majority of SI algorithms. So, using ten chaotic maps, we try to ameliorate SSA in three ways: (i) the initial swarm generation; (ii) the substitution of two random variables in SSA; and (iii) clamping the sparrows crossing the search range. As a result, we get CSSA, a chaotic form of SSA. Extensive comparisons show CSSA to be superior in terms of swarm diversity and convergence speed in solving various representative functions from the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC) benchmark set. Furthermore, experimental analysis of CSSA on eighteen interdisciplinary, multi-scale ML datasets from the University of California Irvine (UCI) data repository, as well as three high-dimensional microarray datasets, demonstrates that CSSA outperforms twelve state-of-the-art algorithms in a classification task based on FS discipline. Finally, a 5%-significance-level statistical post-hoc analysis based on Wilcoxon's signed-rank test, Friedman's rank test, and Nemenyi's test confirms CSSA's significance in terms of overall fitness, classification accuracy, selected feature size, computational time, convergence trace, and stability. [ABSTRACT FROM AUTHOR]

Generation expansion planning (GEP) is a complex, highly constrained, non-linear, discrete and dynamic optimization task aimed at determining the optimum generation technology mix of the best expansion alternative for long-term planning horizon. This paper presents a new framework to study the GEP in a multi-stage horizon with reliability constrained. GEP problem is presented to minimize the capital investment costs, salvage value cost, operation and maintenance, and outage cost under several constraints over planning horizon. Added to that, the spinning reserve, fuel mix ratio and reliability in terms of Loss of Load Probability are maintained. Moreover, to decrease the GEP problem search space and reduce the computational time, some modifications are proposed such as the Virtual mapping procedure, penalty factor approach, and the modified of intelligent initial population generation. For solving the proposed reliability constrained GEP problem, a novel honey badger algorithm (HBA) is developed. It is a meta-heuristic search algorithm inspired from the intelligent foraging behavior of honey badger to reach its prey. In HBA, the dynamic search behavior of honey badger with digging and honey finding approaches is formulated into exploration and exploitation phases. Added to that, several modern meta-heuristic optimization algorithms are employed which are crow search algorithm, aquila optimizer, bald eagle search and particle swarm optimization. These algorithms are applied, in a comparative manner, for three test case studies for 6-year, 12-year, and 24-year of short- and long-term planning horizon having five types of candidate units. The obtained results by all these proposed algorithms are compared and validated the effectiveness and superiority of the HBA over the other applied algorithms. [ABSTRACT FROM AUTHOR]

This paper presents an effective approach for solving economic load dispatch problems contemplating the scheduling a set of thermal generating units to produce a specific power at low consumption costs. These problems can be thought of as nonlinear, non-convex, and highly constrained optimization problems with a large number of local minima. To cope with the above issues in solving such problems, a new meta-heuristic named capuchin search algorithm was adopted. To boost the search performance of this algorithm as well as to mitigate its early convergence and regression to the local optimum, it was hybridized with another algorithm and improved using several positive amendments. First, a memory element was added to this algorithm to ameliorate its position and velocity update mechanisms in order to exploit the most encouraging candidate solutions. Second, two adaptive parametric functions were used to manage the exploration and exploitation features of this algorithm and balance them appropriately. Finally, the hybridization was made using the gradient-based optimizer to strengthen the intensification ability of this algorithm and balance its searching ability to fulfill sensible search performance. The proficiency of the proposed algorithm was divulged by assessing it on computationally difficult economic load dispatch problems under 6 different tests with a generator of 3, 13, 40, 80, and 140 units, each with different constraints and load conditions. The proposed algorithm provided the best performance among many other competitors. Its superiority and practicality were revealed by obtaining optimal solutions for large-scale test cases such as 40-unit and 140-unit test systems. [ABSTRACT FROM AUTHOR]

Due to the users' mobility, new online applications, as well as low processing power and limited energy of smart devices, traditional cloud computing models could not provide new required services. Cloud service providers improve the quality of their services by moving some servers to the edge of the network and closer to mobile users. Considering the moving nature of users and the heterogeneous service demands in different areas, the optimal placement of servers plays an important role in increasing the quality of service provided to users. However, because of the large number of servers, finding the optimal location of these resources is a serious challenge. In the proposed method of this paper (MES-WG), in the first step, the geographical area of server deployment is divided into smaller sub-regions to reduce the complexity of the problem. Then, by using the WOA algorithm the search agent finds the optimal location of the servers. In the next step, a neural network is used for the local placement of all servers in each area. Finally, game theory is deployed for the convergence of resource placement in all sub-regions. The experimental results show that the proposed method reduces the network latency by 33.5% and also improves the load balance on servers by 28.2%, compared to some of the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Precipitation is a key constituent of the water cycle and its accurate measurement is essential for a wide range of hydroclimatic studies. Although advances in technology have provided new sources of precipitation data (e.g., satellite data), gauge measurements are still considered as the most reliable source of information. Accuracy of ground measurements highly depends on the network density and positions of the rain gauges and thus, many studies have attempted to re-design the rain gauge networks around the globe. This is while, a limited number of researches have investigated the potential of utilizing global gridded precipitation products in network optimization context. In this study, a two-step framework was proposed to remove redundant rain gauge stations of the Central Plateau watershed of Iran. In the first step, number of excess rain gauges was detected based on transinformation–distance (T–D) analysis. In the second step, exact location of the extra rain gauges was determined through high degree of freedom and low degree of freedom (LDF) scenarios by a combination of harmony search optimization algorithm and a geostatistical model. According to the results, 58 out of 580 rain gauges were detected to be removed. Results of the proposed scenarios differed in detecting 13 out of 58 gauges. While LDF scenario required less computational time, network re-designed by this scenario was about 32% less accurate than the other scenario. Performance of the both optimized networks was equally similar to the initial network in terms of developing precipitation rasters. [ABSTRACT FROM AUTHOR]

Solar power generation (SPG) is uncertain and less efficient under unequal shading conditions. It has a highly nonlinear power versus voltage (P–V) curve that has multiple peaks which are categorized as a global maximum power point (GMPP) and local maximum power point (LMPP). It requires a better tracking method that continuously tracks the GMPP instead of the LMPP. Many conventional methods are unable to track the GMPP. The meta-heuristic methods are the better solutions for this problem. However, still there is a need to improve the performance in terms of tracking speed, tracking efficiency and better exploration and exploitation phases, which are the essential parameters in intelligent optimization. This paper proposed a new hybrid meta-heuristic technique, arithmetic-based grey wolf optimization (AGWO), a combination of arithmetic optimization algorithm (AOA) and grey wolf optimization (GWO). The first one is mainly for the exploration phase, and the other is for the exploitation phase; together, the combination enhances the search behaviour of the algorithm. The proposed AGWO was executed in MATLAB/Simulink and evaluated with a real-time simulator to validate its performance. This is compared with other relevant methods like AOA, GWO, particle swarm optimization (PSO) and perturb and observe (P&O) methods. The results indicated that the suggested AGWO technique shows better improvement in tracking efficiency of about 16.41% more than P&O during pattern 1. Also, it shows an excellent tracking speed as compared with PSO is 60.42% raise during pattern 3. [ABSTRACT FROM AUTHOR]

Photovoltaic panels (PVs) are solar panels that turn sunlight into electricity. Tracking the maximum power point (MPP) of PVs is especially important for economic issues. The most popular maximum power point tracking techniques are perturb and observation, hill climbing, constant voltage, parasitic capacitance, and incremental conductance (INC). However, these techniques give oscillated results about the MPP that causes low accuracy, especially in partial shading conditions. This paper is discussing the enhancement of photovoltaic energy system performance using several metaheuristic optimization algorithms. Using MATLAB SIMULINK, a comparative analysis of several algorithms for tracking MPP of PV systems under partially shadowed conditions was conducted. The metaheuristic optimization algorithms that are used in this paper are particle swarm optimization (PSO), cuckoo search algorithm (CSA), grey wolf optimization (GWO), and whale optimization algorithm (WOA). The results show that using WOA and GWO achieved the best efficiency in tracking MPP, whereas, using PSO and CSA achieved lower efficiency in tracking MPP. The MPP of the PV system was not tracked by INC under the partial shaded conditions. [ABSTRACT FROM AUTHOR]

Forecasting Solar Power is an important aspect for power trading companies. It helps in energy bidding, planning, and control. The challenge in forecasting is to predict nonlinear data, which can be fulfilled by the computation technique and machine learning model. ML models have high accuracy for time-series forecasting, but their accuracy is poor for nonlinear forecasting. To enhance the ML model for nonlinear prediction, an optimization algorithm is used for training. This paper presents how the computation technique is incorporated into the machine learning model and compared it with the conventional model. CSA-ANN and SOA-ANN models are developed and forecast solar power for a-day-ahead, three-day-ahead, and a week-ahead solar power generation by considering time, irradiation, and temperature as input parameters for the model. The models are compared with ANN, DE-ANN, and PSO-ANN since these models are widely used. Upon comparison, ANN gives the best result for short-term prediction but is unable to predict midterm and long-term predictions, whereas this problem is overcome by SOA-ANN, which is done by changing its training algorithm, and its performance is measured via statistical parameters such as MAE, MSE, MAPE, and R2. The percentage improvement of SOA-ANN is obtained with these statistical parameters as 6.54%, 16.05%, 1.67%, and 3.61%. Hence, SOA-ANN gives best result as compared to other models. [ABSTRACT FROM AUTHOR]

Mobile ad hoc network (MANET) is a set of mobile nodes that communicate via wireless networks while moving from one place to another. Numerous studies have been done on increasing reliable between routing nodes, trust management, the use of cryptographic systems, and centralized routing decisions and so on. However, the majority of routing methods are challenging to execute in real-world scenarios, because it is challenging to determine the malicious behaviors of routing nodes. There is still no reliable method to prevent malicious node attacks. Due to these networks' dynamic and decentralized character, packet routing in MANET is difficult. To overcome this problem, this manuscript proposes a Dropout Ensemble Extreme Learning Neural Network (DrpEnXLNN) optimized with Metaheuristic Anopheles Search routing algorithm(MASA) based Token fostered Block chain Technology for trusted distributed optimal routing in Mobile adhoc networks. The aim of this work is to provide the most efficient method for data transmission and generates tokens for packet stream admittance with a secret key that goes to each routing mobile node. Subsequently, the trusted routing information is distributed by proposed block chain(BC) based mobile ad hoc network utilizing DrpEnXLNN optimized with MASA. The proposed technique is simulated in NS-2(Network Simulator) tool. The performance metrics, such as average delay, average latency, average energy consume, throughput of block chain token transactions are evaluated. Finally, the proposed TDRP-MASA-DrpEnXLNN-BCMANET method attains 22% and 14% less delay during 25% spiteful routing environment, 15% and 8% less delay during 50% spiteful routing environment when analyzed to the existing models. [ABSTRACT FROM AUTHOR]

In this work, we intend to propose multiple hybrid algorithms with the idea of giving a choice to the particles of a swarm to update their position for the next generation. To implement this concept, Cuckoo Search Algorithm (CSA), Grey Wolf Optimization (GWO), Harris Hawks Optimization (HHO), and Whale Optimization Algorithm (WOA) have been utilized. Exhaustive possible combinations of these algorithms are developed and benchmarked against the base algorithms. These hybrid algorithms have been validated on twenty-four well-known unimodal and multimodal benchmarks functions, and detailed analysis with varying dimensions and population size is discussed for the same. Further, the efficacy of these algorithms has been tested on short-term electricity load and price forecasting applications. For this purpose, the algorithms have been combined with Artificial Neural Networks (ANNs) to evaluate their performance on the ISO New Pool England dataset. The results demonstrate that hybrid optimization algorithms perform superior to their base algorithms in most test cases. Furthermore, the results show that the performance of CSA-GWO is significantly better than other algorithms. [ABSTRACT FROM AUTHOR]

The Harris hawks optimization (HHO) algorithm is a new metaheuristic algorithm proposed in recent years. Due to the shortcomings of this algorithm in solving complex high-dimensional optimization problems with a slow convergence speed, low accuracy, and the high likelihood to fall into local optimum, a mixed Harris hawks optimization (MHHO) algorithm based on the pinhole imaging strategy is proposed, including four strategies to improve the optimization performance. Firstly, the pinhole imaging strategy is used to enable the Harris' hawks to approach the optimal solution faster and accelerate convergence. Secondly, the spiral parameter is introduced into the exploration phase to help the searching paths of the Harris' hawks more diverse and improve the global search ability of the algorithm. Finally, the greedy strategy of the aquila optimization algorithm and the position update strategy of the flower pollination optimization algorithm are embedded in the exploitation stage to make the algorithm jump out of local optimum effectively. To verify the effectiveness of the proposed MHHO algorithm, it is compared with the classical HHO algorithm and 16 other state-of-the-art algorithms, and extensively tested on 23 well-known benchmark functions, the IEEE CEC2017 test sets, and three complex constrained engineering optimization problems. The test results show that MHHO achieves the top ranking on both the benchmark functions and the CEC2017 test sets, demonstrating its superior performance in terms of faster convergence speed and higher accuracy. [ABSTRACT FROM AUTHOR]

Back propagation (BP) is widely used for parameter search of fully-connected layers in many neural networks. Although BP has the potential of quickly converging to a solution, due to its gradient-based nature, it tends to fall into a local optimum. Metaheuristics such as evolutionary computation (EC) techniques, as gradient-free methods, may have excellent global search capability due to their stochastic nature. However, these techniques tend to perform worse than BP in terms of convergence speed. In this paper, a hybrid gradient descent search algorithm (HGDSA) is proposed for training the parameters in fully-connected neural networks. HGDSA initially searches the space extensively by means of an ensemble of gradient descent strategies in the early stage and then uses BP as an exploitative local search operator. Moreover, a self-adaptive method which selects strategies and updates the learning rates of strategies has been designed and embedded in the global search operators to prevent stagnation in local optima. To verify the effectiveness of HGDSA, experiments were performed on eleven classification datasets. Experimental results demonstrate that the proposed HGDSA possesses both powerful global and local search abilities. Furthermore, the proposed approach appears to be promising also on high-dimensional datasets. [ABSTRACT FROM AUTHOR]

In recent years, WSNs are acquiring popularity due to small-sized and flexible implementation; many applications require quick data transfer with minimal energy consumption of nodes in the midst of the ubiquitous use of WSNs. These sensor nodes cover large regions according to application needs and choose the best optimal path. The main issue with WSN is how to cover the neighborhood correctly and send data to sink without falling into the trap of a single node and single route. Therefore, a recently researched approach namely the swarm-based dragonfly, which has been effectively used in miscellany applications is exploited for this work. The dragonfly method is based on the exploration phase using global search and exploitation phase using local search. The implicit swarming behaviors are thought to be the fundamental drive for routing algorithms. This paper introduce a Meta-heuristic based Optimized Opportunistic Routing Protocol for WSNs (MOORP) based upon the best optimal forwarder node selection and dragonfly route optimization. The forwarder node selection is optimized by residual energy and eucledian distance of the node. The path between forwarder and destination is identified by using the Dragon-fly algorithm. MOORP employs a route searching algorithm (RSA) and a Energy Level Matrix (ELM) update is used to enhancing the routing decision. The RSA finds an optimal path and selects the optimal forwarder node with the help of a heuristic update or ELM. MOORP performance is compared with other opportunistic routing protocols on important parameters such as the number of alive nodes, throughput, packet delivery ratio, message success rate, and average energy consumption,and also compare with pre-existing cluster based routing protocol. The simulation results show that the MOORP considerably outperforms its competitive techniques in terms of energy efficiency. [ABSTRACT FROM AUTHOR]

We study a home healthcare routing and scheduling problem, where multiple healthcare service provider teams should visit a given set of patients at their homes. The problem involves assigning each patient to a team and generating the routes of the teams such that each patient is visited once. When patients are prioritized according to the severity of their condition or their service urgency, the problem minimizes the total weighted waiting time of the patients, where the weights represent the triage levels. In this form, the problem generalizes the multiple traveling repairman problem. To obtain optimal solutions for small to moderate-size instances, we propose a level-based integer programming (IP) model on a transformed input network. To solve larger instances, we develop a metaheuristic algorithm that relies on a customized saving procedure and a general variable neighborhood search algorithm. We evaluate the IP model and the metaheuristic on various small-, medium- and large-sized instances coming from the vehicle routing literature. While the IP model finds the optimal solutions to all the small- and medium-sized instances within three hours of run time, the metaheuristic algorithm achieves the optimal solutions to all instances within merely a few seconds. We also provide a case study involving Covid-19 patients in a district of Istanbul and derive insights for the planners by means of several analyses. [ABSTRACT FROM AUTHOR]

Unsupervised clustering methods are used as data-driven models for mapping of mineral prospectivity (MPM). One of the challenging problems for modeling high potential mineral prospects using these algorithms is to select proper cluster centroids in order to locate the same geospatial objects into reasonable clusters. To overcome this kind of problems arise from traditional clustering algorithms (e.g. K-means), the combination of harmony search (HS) and artificial bee colony (ABC) metaheuristic optimization algorithms with K-means have been firstly developed in this study which can be used for selection of optimum cluster centroids for highlighting favorable targets in prospecting stage of mineral explorations. This stage is critically important for exploration of undiscovered mineral deposits, because the time and cost are two substantial factors in regional-scale exploration programs. This study stated that the applying hybrid data-driven clustering models (e.g., HS-KM and ABC-KM) are operational tools for identifying high-favorable areas linked to porphyry Cu systems in prospecting stage. Moreover, the results demonstrated that the properly selection of geospatial exploration features has strong impact on the modeling of mineral prospectivity in Kerman porphyry belt, Iran and cause significant decrease in systemic uncertainty linked to MPM. [ABSTRACT FROM AUTHOR]

The use of metaheuristics is currently on the rise for solving real problems due to their complexity and uncertainty management. Most of the current existing metaheuristic algorithms have the problem of local minima and fixed parameters. Fuzzy logic has contributed to solving this problem. It has been shown that the use of type-1 and type-2 fuzzy theory applied in parameter adaptation has contributed to effectively solve this problem. The advantage of utilizing fuzzy theory in parameter adaptation is the uncertainty management that offers a significant enhancement in finding solutions. The main goal is to utilize type-3 fuzzy theory in parameter adaptation of harmony search. Type-3 membership functions can use vertical slices for their construction. A new type-3 fuzzy harmony search approach is utilized to find the antecedent and consequent parameters of a fuzzy controller problem. Experiments with different lower scale parameters were carried out to verify the benefits of modeling the uncertainty domain with the type-3 fuzzy approach. A level of disturbance was applied to the control process to validate the performance of the method with respect to those existing in the literature. [ABSTRACT FROM AUTHOR]

In a variety of image processing applications, multilevel thresholding image segmentation has gotten a lot of interest. When traditional approaches are utilised, however, the process of obtaining the ideal threshold values takes time. Despite the fact that Hybrid metaheuristic methods can be used to overcome these limits, such approaches may be ineffective when dealing with a local solution. The present study proposes a multi-level image thresholding based hybridization strategy based Sine-Cosine Crow Search Algorithm(SCCSA) to make more efficient image segmentation. The main limitation of the classical Crow Search Algorithm (CSA) is that search agents sometimes do not produce the best solutions. To update a solution to the best solution, each search agent can use Sine-Cosine Algorithm (SCA) movements to update its position accordingly. This ensures a good balance between two goals (exploration and exploitation) would improve the efficiency of the search algorithm. The optimal threshold values are searched by the chosen objective functions of the otsu's and kapur's entropy approaches. The hybrid algorithm is evaluated in 12 standard image sets and then compared with the performance of other state-of-the-art algorithms such as ICSA, SCA, CSA and ABC. Experimental results showed that, in different metrics of the output such as objective function values, PSNR, STD values, Mean, SSIM, FSIM and CPU time, the proposed algorithm is consistently higher than other algorithms. In addition, the wilcoxon test is performed using the proposed algorithm to detect the significant differences between the other algorithms. The findings indicated that the proposed SCCSA succeeds with other well-known algorithms and has dominance over robust, accurate and convergent values. [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]

Cooperation search algorithm (CSA) is a new metaheuristic algorithm inspired from the team cooperation behaviors in modern enterprises and is characterized by fast convergence. However, for complex multimodal problems, it may get trapped into local optima and suffer from premature convergence for the shortcoming of population updating guided only by leading individuals. In this paper, the issue of low convergence efficiency and convergence accuracy of the CSA algorithm on complex multimodal problems is dramatically alleviated by integrating the mutation and crossover operators in DE algorithm. Experimental results demonstrate the better performance of CCSA on convergence speed and accuracy as compared to other existing optimizers. Furthermore, aiming at the problem that there is no universal approach for the multi-degree reduction in Ball Bézier surfaces under different interpolation constrains, we propose a new method to solve this problem by introducing metaheuristic methods, where the change of interpolation constrains is treated as the change of decision variables. The modeling examples show that the proposed method is effective and easy to implement under different interpolation constrains, which can achieve the multi-degree reduction in Ball Bézier surfaces at one time and can simplify the degree reduction procedure significantly. [ABSTRACT FROM AUTHOR]

In this study, we proposed a new metaheuristic algorithm called Electrical Search Algorithm (ESA). The proposed algorithm is based on the movement of electricity in high-resistive areas such as wood, glass, and gases. ESA has a unique initialization scheme that only one agent initializes at the lower and upper bounds of the search space, which creates structures called poles. After that, ESA uses unique exploration and exploitation strategies to search. The search mechanism is based on electrons moving to opposite poles. ESA differs from other metaheuristics compared to its initialization scheme, pole search mechanism, and update strategy of the best solutions. ESA was tested with the "100-Digit Challenge" benchmark functions in the IEEE-CEC-2019, four well-known benchmark functions, and an np-hard clustering problem. For the clustering problem, we used four well-known datasets: Iris, Wine, Seeds, and Hepatitis C Virus. ESA was compared with seven different metaheuristic algorithms on these well-known benchmark functions, and the results of the clustering problem were compared with the K-Means algorithm. Additionally, Friedman Signed Rank and post hoc Wilcoxon Test were run to show the significance of the results. In all of the well-known benchmark functions, ESA either offered the best results or similar results to other compared algorithms. The score of the ESA on the IEEE-CEC-2019 benchmark functions shows us that even with the minor evaluation numbers, ESA can achieve similar results to the competing algorithms. Results show that ESA has a robust mechanism for not trapping in local points and moves slow but persistent rate. [ABSTRACT FROM AUTHOR]

This article develops an evolutionary nature inspired algorithm based on the social behavior of the goat, a pet of a farmer in a village life. In village life, we generally see the shepherds keep their goats free/untie from collar thread for grazing in the early morning and receives them at the end of the day when they come back into the home with their own efforts. But some day the goats did not come back in due time because of overfeeding of grass causing unable to move any more after meeting their grasp and began to get rest there. The shepherd feels more tempted and began to search for his/her goat. After untie, the goat began to graze herself through the walk on the path of the cultivated land and bank of the village ponds. The search process is going on through that path until it is not finally got. To characterize this problem some definitions like false walk, uniform and non-uniform steps, goat's jump, periodic walk and goodness of fit for various walk functions have been discussed here rigorously. Inspiring from this fact novel metaheuristic algorithms along with pseudocode and hardware specification have been discussed to optimize a benchmark multi-modal objective function having some singularity zones explicitly. Numerical results have been compared with some of the existing state- of -arts under 95% confidence intervals. Also, graphical illustrations are performed to validate the proposed approach. Finally, a conclusion is made followed by scope of future work. [ABSTRACT FROM AUTHOR]

The whale optimization algorithm (WOA) is a simple structured and easily implemented swarm-based algorithm inspired by the unique bubble-net feeding method of humpback whales. Past studies have shown that WOA performs well in a number of optimization problems. However, it is difficult for WOA to completely free itself from the problems of insufficient convergence accuracy and premature convergence when solving global optimization problems. To address these issues, a hybrid whale optimization algorithm based on symbiotic strategy (HWOAMS) is proposed in this paper. The main idea of the proposed method is to combine the improved symbiotic organisms search algorithm (SOS) with the whale optimization algorithm thus enhancing the search ability of WOA. First, an improved symbiotic phase based on Lévy flight and chaos strategy is introduced into the exploration process to enhance the global search capability; Second, an improved mutualism phase based on Brownian motion is used instead of the original shrinking encircling phase to achieve better local exploitation. Third, an improved parasitic phase based on a modified global optimal spiral operator strategy is embedded in the spiral updating position phase to help the algorithm further improve the exploitation efficiency and convergence accuracy. Finally, a global search strategy is proposed to help the algorithm better balance exploration and exploitation. To establish the effectiveness of the new algorithm, extensive simulation experiments are conducted on HWOAMS using the classical function test set, the CEC 2019 function set and four classical engineering problems. Numerical evaluation results indicate that HWOAMS outperforms 18 other algorithms in terms of local optimum avoidance ability and convergence accuracy in a majority of cases, and has better search performance. [ABSTRACT FROM AUTHOR]