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Optimizing aircraft maintenance, repair and overhaul (MRO) planning, i.e., reviewing aircraft history based on when and where the aircraft should be serviced, is essential to ensuring aviation safety and operations. This paper examines a method for optimizing the scheduling of MRO operations for complex products. It has been shown in the literature that scheduling optimization of MRO operations is an NP-hard problem. In this study, the planning problem for MRO service resources is first analyzed, a mathematical model for the MRO process planning problem is established, and a multi-objective mathematical model for minimizing makespan time, maintenance time, and cost is proposed to represent the planning problem. Then, to optimize the makespan time, maintenance time, and cost, we first generate the maintenance, repair and overhaul transaction path (MROTP) data to utilize the PLM system efficiently, which ensures the effective operation of MRO actions throughout the aircraft lifecycle. MROTP data are obtained with engineering studies in our proposed research, and received data are retained in the PLM system. Finally, the modified ACO algorithm that performs scheduling optimization of the MRO process and the integration process of PLM with MRO operations is presented. The proposed algorithm is tested and validated by simulations in MATLAB software with the data obtained from the leading MRO company in Turkey. The Gurobi Solver is used to obtain the optimal solution by converting the problem into mixed-integer linear programming (MILP). The experimental setup is run using Gurobi with a 1-hour time limit, and the best solution is considered with an average run time calculated in seconds. When solving complicated instances, the MILP model-based approach is challenging. The study and comparison of the findings demonstrate that the proposed ACO-based algorithms have an advantage in planning MRO operations. The proposed ACO algorithm performs better than the other metaheuristic algorithms and the MILP model regarding maintenance time and cost. [ABSTRACT FROM AUTHOR]

This study presents a boxing match algorithm (BMA) as a new efficient meta-heuristic method to solve numerical optimization and NP-hard problems, such as knapsack. The proposed algorithm finds the best position (i.e., best solution) in the ring during the match (i.e., feasible solutions) through a simulation of the boxer's behavior, who can attract good support from his coach and fans to defeat his rival. The proposed algorithm is seeking good solutions by dividing the solution space into different sections and generating new solutions in each section through a semi-zigzag search. This division causes a proper and targeted search in the solution space to reach an efficient solution. Also, the unique boxer's movement in the ring (generating new solutions) enables the proposed BMA to give an acceptable level of performance in exploring the problem space. Several unconstrained mathematical, knapsack, and engineering benchmark problems undergo a standard test designed to confirm the powerful performance of the proposed BMA. A wide variety of complex numerical problems are solved and analyzed by a comparison between the results obtained from the test and the other well-known optimization methods. Quantitative data also indicate the proper performance of the proposed algorithm compared to the other ones. The results show that this algorithm can achieve 95% and 100% optimal solutions in 20 mathematical benchmark functions and eight examples of the knapsack problem, respectively. [ABSTRACT FROM AUTHOR]

Segmentation refers to the process of dividing an image into multiple regions based on some criteria such as intensity and color. In recent years, color image segmentation has received considerable attention from the researchers. However, it is still a highly complicated task due to the presence of more attributes or components as compared to monochrome images. Numerous meta-heuristics algorithms are developed to determine the optimal threshold value for segmenting color images efficiently. This paper presents an enhanced sine cosine algorithm (ESCA) to seek threshold for segmenting color images. Sine cosine algorithm (SCA) is a population-based optimization algorithm which has the ability of preventing local minima problem. First an input image is transformed to CIE L*a*b* color reduced space. ESCA is applied to determine the optimal threshold values for segmentation. The performance of the proposed method is tested on color images from Berkeley database, and segmentation results are compared with two metaheuristic algorithms, namely particle swarm optimization (PSO) and standard SCA. Experimental results are validated by measuring peak signal–noise ratio (PSNR), structural similarity index and computation time for all the images investigated. Results revealed that the proposed method outperforms the other methods like PSO and SCA by achieving PSNR of 23 dB and SSIM of 0.93 and also require less time for finding optimal threshold values than PSO and SCA. [ABSTRACT FROM AUTHOR]

The fusion of visible and infrared (IR) images is considered as one of the most efficient image fusion methods so far, as more valuable information can be obtained from the fused images overcoming the sensor limitations. Even in poor lighting conditions, by utilizing the thermal (IR) radiation information, the details can be obtained from the IR sensors. Visual images provide better spectral, spatial, and texture details. Thus, integrating the IR and visual images will provide more information about an object in a scene, thus enhancing the decision-making process in surveillance applications. The proposed work is aimed to be an object detection framework that utilizes this image fusion technique to identify an object. However, when the query image has more features, the computational complexity for extraction and processing of those features for identification of an object increases. This reduces the detection accuracy of the system. Hence, an optimal set of features ought to be identified before proceeding into the classification and detection processes. To achieve this, the research work has proposed a nature-inspired optimal feature selection methodology using ant colony optimization. Finding the optimal features to reduce the computational complexity and thereby increase the accuracy of detection is the research objective. The fusion of IR and visible images is progressed using pulse-coupled neural network. The rest of the processes such as classification and detection are performed using the convolutional neural network model. Experimental results have been compared with conventional techniques in terms of accuracy and computation time. [ABSTRACT FROM AUTHOR]

The knapsack problem is one of the combinational optimization issues. This problem is an NP-hard problem. Soft computing methods, including the use of metaheuristic algorithms, are one way to deal with these types of problems. The standard Giza Pyramids Construction (GPC) algorithm is the first ancient-inspired algorithm that is published recently. In this paper, a binary version of the GPC algorithm for solving the 0-1 knapsack problem is proposed. For this purpose, this study uses both accumulative and multiplicative penalty functions as the objective function to determine infeasible solutions. To compare the performance, thirty different datasets have been created and the proposed algorithm has been compared with four popular and state-of-the-art algorithms. Statistical analysis has been used to find a significant difference in the performance of algorithms. The results and statistical analysis show that the proposed algorithm performs better than other metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Determination of the parameters of metaheuristic methods, such as particle swarm optimization (PSO) is a vital issue. Determining the optimal values of these parameters is a very difficult task and sometimes impossible so we focus on identifying the most important PSO parameters. For this purpose, statistical analysis of the relationship between the performance of the PSO algorithm with the proper selection of the PSO parameters should be done. Thus, unlike the earlier studies, here the most important PSO parameters are statistically determined. In the related literature, PSO parameters are traditionally determined by using trial and error method or specified intuitively. In this study, a new approach for identifying the most important PSO parameters for any optimization problem is proposed by using the factorial design which is one of the most popular and widely used statistical technique in experimental studies. Here, the parameters of the Weibull distribution which has a wide usage in the engineering and statistics literature are estimated just for illustration. It is obvious that identifying the most important PSO parameters via the proposed approach is very important to reduce the dimension of the search space for the PSO parameters. Statistical results obtained from the Monte Carlo simulation study are given and discussed at the end of the paper. [ABSTRACT FROM AUTHOR]

A modified Artificial Bee Colony (ABC) metaheuristics optimization technique is applied for cancer classification, that reduces the classifier's prediction errors and allows for faster convergence by selecting informative genes. Cuckoo search (CS) algorithm was used in the onlooker bee phase (exploitation phase) of ABC to boost performance by maintaining the balance between exploration and exploitation of ABC. Tuned the modified ABC algorithm by using Naïve Bayes (NB) classifiers to improve the further accuracy of the model. Independent Component Analysis (ICA) is used for dimensionality reduction. In the first step, the reduced dataset is optimized by using Modified ABC and after that, in the second step, the optimized dataset is used to train the NB classifier. Extensive experiments were performed for comprehensive comparative analysis of the proposed algorithm with well-known metaheuristic algorithms, namely Genetic Algorithm (GA) when used with the same framework for the classification of six high-dimensional cancer datasets. The comparison results showed that the proposed model with the CS algorithm achieves the highest performance as maximum classification accuracy with less count of selected genes. This shows the effectiveness of the proposed algorithm which is validated using ANOVA for cancer classification. [ABSTRACT FROM AUTHOR]

Combined heat and power economic dispatch (CHPED) problem is one of the challenging issues in power system control, and operation, that searches for the optimal values of power and heat generation of different units to minimize the total operation cost. The interdependency of heat and power outputs of CHP units and valve point loading effects of power-only units make this problem very nonlinear and non-convex. So far, a wide variety of optimization algorithms based on simulation of various natural phenomena have been proposed and applied to solve this complex problem, claiming that the proposed algorithm is better than the similar optimization algorithms in terms of accuracy and run-time. In this paper, the CHPED problem is investigated in different case studies by using the imperialist competitive Harris hawks optimization (ICHHO) algorithm, as a combination of the imperialist competitive algorithm and Harris hawks optimization. The effectiveness of the proposed algorithm is first evaluated by testing it on the standard cases of 5, and 7 units as small-scale systems and 48-unit as the large-scale test system, considering the electrical losses. Also for the first time, the multi-zone CHPED (MZCHPED) problem is solved by a metaheuristic algorithm. The obtained results show that ICHHO algorithm reduces the operating costs in the range of 0.02–1.1%, compared to the best-reported results, in different single-zone CHPED (SZCHPED) problems. Also, the MZCHPED results, prove the effective performance of the algorithm compared to the similar SZCHPED problem. [ABSTRACT FROM AUTHOR]

A biomedical application of a novel metaheuristic optimizer is proposed in this paper by constructing an enhanced arithmetic optimization algorithm (AOA). The latter algorithm was constructed using the logarithmic spiral (Ls) search mechanism from the whale optimization algorithm and the greedy selection scheme from the differential evolution algorithm. The proposed algorithm (Ls-AOA) was tested against unimodal and multimodal benchmark functions and demonstrated better capability comparatively using other efficient metaheuristic algorithms reported in the literature. The constructed Ls-AOA algorithm was then proposed to design a proportional-integral-derivative (PID) controller employed in a functional electrical stimulation (FES) system for the first time. The initial statistical and convergence profile assessment showed better performance of the proposed algorithm. The comparative analyses for transient and frequency responses were performed for the PID-controlled FES system using the original AOA, sine–cosine and particle swarm optimization algorithms and the traditional Ziegler-Nichols tuning scheme. Similarly, the FES system tuned with the latter methods was also assessed for disturbance rejection and noise elimination. All the comparative analyses demonstrated that the proposed Ls-AOA has the greater capability for the challenging biomedical FES system. [ABSTRACT FROM AUTHOR]

Multi-verse optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step is using opposition-based learning (OBL) in MVO, called OMVO. The OBL aids to speed up the searching and improving the learning technique for selecting a better generation of candidate solutions of basic MVO. The second stage, called OMVOD, combines the disturbance operator (DO) and OMVO to improve the consistency of the chosen solution by providing a chance to solve the given problem with a high fitness value and increase diversity. To test the performance of the proposed models, fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the enhancement. The second step, known as OMVOD, incorporates the disruption operator (DO) and OMVO to improve the accuracy of the chosen solution by giving a chance to solve the given problem with a high fitness value while also increasing variety. Fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the upgrade to assess the accuracy of the proposed models. [ABSTRACT FROM AUTHOR]

Brain computer interfaces are important for different application domain such as medical, natural interfaces and entertainment. Besides the difficulty of gathering data from the human brain via different channel probs, preprocessing of data is another different and important task that must be solved in order to get better achievement. Selection of the most active channels is an important problem to achieve high classification accuracy. Metaheuristics are good solutions for selecting the optimal subset from the original set, as they have the ability to obtain an acceptable solution in a reasonable time. At the same time, it is necessary to use the correct feature extraction method so that the data can be properly represented. In addition, traditional deep learning methods used for emotion recognition ignore the spatial properties of EEG signals. This reduces the classification accuracy. In this study, we used artificial bee colony optimization algorithm on the seed dataset to increase the classification accuracy. We implemented and tested four different variations of this algorithm. Then, we extracted the features of the obtained channels with the Welch PSD method. We used enhanced capsule network as a machine learning algorithm and showed the best configuration to solve the problem. At the end of the process, 99.98% training and 99.83% test accuracy rates were obtained. [ABSTRACT FROM AUTHOR]

Teaching–learning-based optimization is one of the latest metaheuristic algorithms. TLBO has a simple framework and good global search ability. In addition, TLBO only needs population size and terminal condition for performing search tasks. Given these advantages, TLBO has been used widely since it was proposed. However, TLBO may fall into local optimal solutions in solving complex multimodal optimization problems. This paper reports an improved TLBO, namely competitive teaching–learning-based optimization, for solving multimodal optimization problems. In CTLBO, population is first divided into outstanding group and common group by the designed competitive mechanism. Then outstanding group is updated by the learning strategies of TLBO and common group is guided by outstanding group. In addition, a mutation operator for the optimal individual is introduced to increase the ability of CTLBO to escape from the local optima. The performance of CTLBO is investigated by 45 benchmark test functions from CEC 2014 and CEC 2015 test suites and three challenging real-world engineering problems. Experimental results show that CTLBO is more reliable and efficient on most test cases than TLBO and the other compared algorithms. This supports the effectiveness of the improved strategies and the superiority of CTLBO in solving multimodal optimization problems. [ABSTRACT FROM AUTHOR]

Millimeter-wave (mmWave) non-orthogonal multiple access (NOMA) is a promising transmission scheme to provide the required quality of service in the fifth generation (5G) mobile networks. NOMA improves spectral efficiency and throughput compared with orthogonal multiple access (OMA) in modern high diversity communication networks. Random beamforming deployment facilitates low latency transmission besides the reliable communication in the systems with massive connectivity. In this paper, we introduce a mmWave-NOMA system utilizing a sparse antenna array that substantially improves the system energy efficiency and offer low-weight, cost-effective and small-size antenna apparatus. We also introduce an optimum low complexity PSO-based algorithm to further improve the outage probability. The simulation and analysis results demonstrate 50% reduction in the outage probability in average by performing the PSO-based optimization algorithms. A comparison between the performance of PSO-based algorithm and some metaheuristic algorithms is performed. Due to the time and outage probability performances, our presented PSO-based algorithm gives better results. [ABSTRACT FROM AUTHOR]

Human facial analysis (HFA) has recently become an attractive topic for computer vision research due to technological progress and mobile applications. HFA explores several issues as gender recognition (GR), facial expression, age, and race recognition for automatically understanding social life. This study explores HFA from the angle of recognizing a person's gender from their face. Several hard challenges are provoked, such as illumination, occlusion, facial emotions, quality, and angle of capture by cameras, making gender recognition more difficult for machines. The Archimedes optimization algorithm (AOA) was recently designed as a metaheuristic-based population optimization method, inspired by the Archimedes theory's physical notion. Compared to other swarm algorithms in the realm of optimization, this method promotes a good balance between exploration and exploitation. The convergence area is increased By incorporating extra data into the solution, such as volume and density. Because of the preceding benefits of AOA and the fact that it has not been used to choose the best area of the face, we propose utilizing a wrapper feature selection technique, which is a real motivation in the field of computer vision and machine learning. The paper's primary purpose is to automatically determine the optimal face area using AOA to recognize the gender of a human person categorized by two classes (Men and women). In this paper, the facial image is divided into several subregions (blocks), where each area provides a vector of characteristics using one method from handcrafted techniques as the local binary pattern (LBP), histogram-oriented gradient (HOG), or gray-level co-occurrence matrix (GLCM). Two experiments assess the proposed method (AOA): The first employs two benchmarking datasets: the Georgia Tech Face dataset (GT) and the Brazilian FEI dataset. The second experiment represents a more challenging large dataset that uses Gallagher's uncontrolled dataset. The experimental results show the good performance of AOA compared to other recent and competitive optimizers for all datasets. In terms of accuracy, the AOA-based LBP outperforms the state-of-the-art deep convolutional neural network (CNN) with 96.08% for the Gallagher's dataset. [ABSTRACT FROM AUTHOR]

Field-road trajectory segmentation, which aims to automatically divide a trajectory into a sequence of field-road segments, is one of the important tasks of the agricultural machinery trajectory process. This study aims to enhance the performance of the field-road segmentation problem by introducing a novel parameter optimization strategy that relies on metaheuristic algorithms. The utilization of the metaheuristic algorithm offers precise and efficient techniques for determining parameter combinations in the field-road segmentation model. A novel enhanced optimization algorithm called the chaos sensing slime mould algorithm (CSSMA), aimed at determining out the optimal parameter combination from a large constrained solution space is proposed to further improve the segmentation performance of the model. First, to strengthen the exploration capability of the algorithm, a chaotic strategy is used to initialize the search agents. Furthermore, the CSSMA incorporates the beetle antennae search algorithm and the genetic algorithm to improve its exploitation potential. Ultimately, a proposed technique for updating control parameters in a nonlinear and dynamic manner aims to achieve a balance between exploration and exploitation. This strategy involves adaptively adjusting computing operations based on feedback from the optimization process. In order to assess the effectiveness of the CSSMA, the algorithm is compared to other metaheuristic algorithms using 23 standard benchmark functions. Furthermore, the CSSMA is utilized to address the practical implementation of the parameter optimization approach for field-road segmentation. The goal is to achieve the utmost accuracy in segmentation and compare it with the current method. Experimental results demonstrate the superior convergence and speed of CSSMA, along with its benefits in extracting optimal parameter structures. It surpasses prior techniques in handling the complexities of field-road trajectory processing problems including several models and nonlinearity features. Additionally, it offers a comprehensive solution for optimizing parameters in various field-road segmentation models. [ABSTRACT FROM AUTHOR]

This document is a retraction note for an article titled "A metaheuristic segmentation framework for detection of retinal disorders from fundus images using a hybrid ant colony optimization." The publisher has retracted the article due to concerns about compromised editorial handling and peer review process, inappropriate references, and the article not being in scope of the journal or guest-edited issue. The authors disagree with the retraction. The original article can be found online. [Extracted from the article]

This document is a retraction note for an article titled "Efficient agricultural yield prediction using metaheuristic optimized artificial neural network using Hadoop framework." The publisher has retracted the article due to concerns about compromised editorial handling and peer review process, inappropriate references, and the article not being in scope of the journal or the guest-edited issue. The publisher no longer has confidence in the results and conclusions of the article. The authors disagree with the retraction. The original article can be found online. [Extracted from the article]

This document is a retraction note for an article titled "A metaheuristic optimization model for spectral allocation in cognitive networks based on ant colony algorithm (M-ACO)" published in the journal Soft Computing. The publisher has retracted the article due to concerns about compromised editorial handling and peer review process, inappropriate references, and the article not being in scope of the journal or the guest-edited issue. The authors have not responded to correspondence regarding this retraction. The online version of the original article can be found on the publisher's website. [Extracted from the article]

Encryption of a plaintext involves a secret key. The secret key of classical cryptosystems can be successfully determined by utilizing metaheuristic techniques. Monoalphabetic cryptosystem is one of the famous classical cryptosystems. In this paper, we determine the secret key of the monoalphabetic cryptosystem using a recently proposed metaheuristic technique, namely, red deer algorithm. The existing red deer algorithm framework has been tailored to solve the above considered problem. Performance of the developed red deer algorithm is compared with the following metaheuristic techniques: tabu search, genetic algorithm, and cuckoo search using three criteria, namely, effectiveness, efficiency, and accuracy. The results obtained show that the proposed red deer algorithm can compete with all the above three algorithms with respect to all the criteria. This signifies the importance of the proposed red deer algorithm is that it can be utilized to solve the similar problems effectively, efficiently, and with more accuracy. [ABSTRACT FROM AUTHOR]

Several constrained optimization problems have been adequately solved over the years thanks to the advances in the area of metaheuristics. Nevertheless, the question as to which search logic performs better on constrained optimization often arises. In this paper, we present Dual Search Optimization (DSO), a co-evolutionary algorithm that includes an adaptive penalty function to handle constrained problems. Compared to other self-adaptive metaheuristics, one of the main advantages of DSO is that it is able auto-construct its own perturbation logics, i.e., the ways solutions are modified to create new ones during the optimization process. This is accomplished by co-evolving the solutions (encoded as vectors of integer/real values) and perturbation strategies (encoded as Genetic Programming trees), in order to adapt the search to the problem. In addition to that, the adaptive penalty function allows the algorithm to handle constraints very effectively, yet with a minor additional algorithmic overhead. We compare DSO with several algorithms from the state-of-the-art on two sets of problems, namely: (1) seven well-known constrained engineering design problems and (2) the CEC 2017 benchmark for constrained optimization. Our results show that DSO can achieve state-of-the-art performances, being capable to automatically adjust its behavior to the problem at hand. [ABSTRACT FROM AUTHOR]

Optimization problem involving interval parameters and multiple conflicting objectives are called multi-objective optimization problems with interval parameters (IMOPs), which are common and hard to be solved effectively in practical applications. An interval multi-objective honey badger algorithm (IMOHBA) is proposed to address the IMOPs in this paper. Firstly, the μ\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\mu$$\end{document} metric is employed to assess the Pareto dominance relationship among interval individuals, which reflects the quality of the optimal solutions. Secondly, the crowding distance suitable for the interval objective is utilized to reflect the distribution of the optimal solution. Finally, the candidate solutions are ranked and selected by the non-dominated sorting method. To validate the performance of IMOHBA, it is tested on 19 benchmark IMOPs as well as an interval multi-objective scheduling problem for underwater wireless sensor networks and compared with three state-of-the-art algorithms. The experimental results demonstrate the superiority and strong competitiveness of IMOHBA in addressing IMOPs, exhibiting improved convergence and broader exploration capabilities of the solution space. These findings further validate the effectiveness and feasibility of IMOHBA, highlighting its unique advantage in solving IMOPs. [ABSTRACT FROM AUTHOR]

The kNN classifier is the most popular, supervised machine-learning technique, but the main disadvantage of this algorithm is that it has restricted access to the class distributions in a training point set and treats all the instances equally. In kNN classification, fuzzy sets are used to obtain the membership degrees of each point to the classes known as fuzzy kNN (FkNN) classification. Although the FkNN classifier enhances the performance of the kNN, it does not consider the effect of noisy and redundant instances, which makes it ineffective. Moreover, the performance of kNN is dependent on the value of k (number of nearest neighbours). Considering these issues, we present a novel algorithm that simultaneously tunes the class-dependent feature weights and k value using an effective meta-heuristic algorithm, the Enhanced Equilibrium Optimization technique. Several experiments have been conducted on different biomedical datasets, and the proposed approach has outperformed the other standard classifiers in terms of accuracy. [ABSTRACT FROM AUTHOR]

This paper presents an efficient decoding method (namely SORTED decoding) for the de facto encoding in size optimization of trusses with automatic member grouping. The efficiency of the proposed method is evaluated through numerical experiments driven by two metaheuristic algorithms of different search mechanisms: Colliding Bodies Optimization and Jaya Algorithm. The first group of experiments show that the SORTED decoding method significantly outperforms the de facto and the two other decoding methods introduced in this study (ACCUMULATED and SORTED-ACCUMULATED) in terms of solution quality; moreover, the authors proved that the superiority of the SORTED decoding is not algorithm-dependent. Considerably better member-grouping configurations that provide up to 15% material economy are discovered for some of the well-known pre-grouped benchmark problems in the second group of experiments; surprisingly, the worst member-grouping configuration discovered by the SORTED decoding for one of the problems is better than that of the pre-grouped version. [ABSTRACT FROM AUTHOR]

In order to accurately predict the development of the servo industry in China, this study proposes a Hausdorff fractional reverse accumulated grey power model. Accumulation operation and differential equation modeling are the essential modeling steps of grey models different from other algorithms. Given the importance of information prioritization and the nonlinear characteristics in the data, the proposed model introduces enhancements from two key aspects: data feature extraction and structural representation. Firstly, an improved reverse accumulation operation is introduced into Hausdorff fractional accumulation, which can avoid the defect that Hausdorff fractional accumulation does not have the priority of new information. Secondly, an improved structure-adaptative discrete grey power model is proposed to simulate the nonlinear relationship between temporal factors and system states. Unlike traditional grey power models that only consider nonlinear relationships between historical values or temporal factors, the proposed improved grey model can comprehensively consider the complex characteristics of uncertain systems, providing valuable insights for the further expansion of nonlinear grey models. Furthermore, a cost function is established to adaptively adjust the model's information prioritization and nonlinear features. And the whale optimization algorithm is used to optimize the two hyperparameters of the proposed model. Finally, numerical examples are provided to validate the suitability of the proposed model, which predicts the trajectory of the servo industry and its downstream sectors in China. The forecasting results indicate that by 2025, the market size of China's servo industry is expected to reach 61 billion yuan. The production of computer numerical control cutting machine tools in China is projected to reach 880,000 sets by 2025. Additionally, the production of industrial robots in China is anticipated to reach 557,004 units, with an average annual growth rate of approximately 26%, meeting the government's development target of 20%. [ABSTRACT FROM AUTHOR]

This research uses a new method called the Modified Adolescent Identity Search Algorithm (MAISA) to solve optimization problems. The identity of a teenager is a different combination of thoughts, beliefs, and abilities that are obtained by him. He communicates with other people in society and tries to use the positive or negative feedback from these social interactions to shape his identity and become an independent person. Mathematical simulation of how adolescents’ personality is formed during the mentioned process causes the emergence of this algorithm. The purpose of this research is to improve the performance of the AISA algorithm in the balance between exploration and exploitation. This algorithm isn’t trapped in the optimal local points due to the appropriate convergence rate and reaches the universal optimal answer quickly. One of the features of this algorithm that we can mention is its simplicity and having few setting parameters. To show the effectiveness of this algorithm, results of solving two types of problems of mathematical functions and practical examples such as large-scale truss 120, 244, 942, and 72-bar truss, are measured by other similar algorithms. The results show that this algorithm has a good performance in the optimal solution of such problems, compared to other similar algorithms. [ABSTRACT FROM AUTHOR]

Marine predators algorithm (MPA) is one of the recently proposed metaheuristic algorithms. In the MPA, position update mechanisms are implemented, emphasizing global search in the first part of the search process, balanced search in the middle, and local search in the last part. This may adversely affect the local search capability of the algorithm in the first part of the search process and the global search capability in the last part of the search process. To overcome these issues, an algorithm called MultiPopMPA with a multi-population and multi-search strategy is proposed in this study. Thanks to the proposed algorithm, local, balanced, and global search strategies of the original MPA were utilized from the beginning to the end of the search process. Thus, it is aimed to contribute to a more detailed search of the parameter space. In this study, the proposed algorithm has been applied in training artificial neural networks for 21 different classification datasets. The success of the algorithm has been scored on precision, sensitivity, specificity, and F1-score metrics and compared with eight different metaheuristic algorithms, including the original MPA. In terms of the mean rank of success, the proposed MultiPopMPA has been ranked first in precision, sensitivity, and F1-score metrics and ranked second in the specificity metric. In addition, it has been observed that the proposed algorithm outperforms its competitors in most cases in terms of convergence and stability. Finally, Wilcoxon’s signed-rank test results calculated through the MSE metric showed that the proposed algorithm produced statistically significant results in most cases. [ABSTRACT FROM AUTHOR]

Out of the various types of skin cancers, melanoma is observed to be the most malignant and fatal type. Early detection of melanoma increases the chances of survival which necessitates the need to develop an intelligent classifier that classifies the dermoscopic images accurately as melanoma or non-melanoma. Features extracted from an image have a major impact on the performance of a classifier. In this paper, handcrafted feature extraction techniques are used to extract features from dermoscopic images. It is quite possible that not all the features extracted from dermoscopic images contribute in the process of classification which imposes the need of selection of significant features from the feature set. Here, two binary variants of Harris Hawk Optimization (HHO) algorithm namely BHHO-S and BHHO-V are presented that employ S-shaped and V-shaped transfer functions with time-dependent behavior, respectively, for feature selection. The selected features are given to a classifier that classifies the dermoscopic image as melanoma or non-melanoma. Comparison of the performance of the proposed methods is done with existing metaheuristic algorithms. The results obtained after experimentation show the superiority of classifier that uses features selected using BHHO-S over BHHO-V and the classifiers that use existing state-of-the-art metaheuristic algorithms. The experimental results also reveal that texture features extracted using local binary pattern along with color features provides higher classification accuracy as compared to global and other local texture feature extraction techniques. [ABSTRACT FROM AUTHOR]

Based on the basic principles of system dynamics, the mathematic description for the stock market evolution which is a typical complicated dynamic system is provided. And according to this mathematic description, a new hybrid intelligent method based on multi-layer feed-forward neural network (MFNN) which is the most widely used neural network and new improved black hole algorithm (IBHA) which is a new nature-inspired metaheuristic algorithm is proposed to model the stock market. In this new hybrid intelligent method, the new IBHA whose structure is simple and which can be implemented easily, and the modified back propagation algorithm have been used to optimize the architectural and algorithmic parameters of MFNN simultaneously. This new method is applied to model two data groups of Shanghai stock market in 2015.9–2018.8 of China to show that the computing results of the new method coincide well with the real data and that the forecasting precision is highly accurate in these cases. At last, the new method has been applied to model seven other stock market data sets and compared with five state-of-the-art hybrid algorithms proposed in previous studies. The results show that the performance of the new method (including modeling effect and efficiency) has been demonstrated to be satisfactory by using for different problem settings and market situations and it is also easy to be implemented. [ABSTRACT FROM AUTHOR]

Several population-based techniques have subsequently been proposed. Despite their broad use in a variety of applications, we are still investigating the use of proposed methods to tackle real-world challenges. As a result, researchers must considerably modify and enhance their approaches based on the primary evolutionary processes in order to achieve rapid convergence, consistent equilibrium with high-quality data, and optimization. The paper proposes a fusion method (AOA–GOA) meta-heuristic optimization methods for global optimization tasks. They can be applied to different problems, including image processing, machine learning, wireless networks, power systems, engineering design, etc. The method fusion proposed is analyzed in context with GOA and AOA. To evaluate the performance, each method is tested on the same parameters like population size and number of iteration. The proposed IAOA is evaluated by varying the dimensions. The impact of varying dimensions is a standard test used in previous studies for optimizing test functions that show the effect of varying dimensions on efficiency of IAOA. From this, it is noted that it works efficiently for both high and low dimensional problems. In high dimensional problem, the proposed method gives efficient search results. [ABSTRACT FROM AUTHOR]

In the present paper, a physics-inspired metaheuristic algorithm is presented to solve multi-objective optimization problems. The algorithm is developed based on the concept of Newtonian cooling law that recently has been employed by the thermal exchange optimization (TEO) algorithm to solve single-objective optimization problems efficiently. The performance of the multi-objective version of TEO (MOTEO) is examined through bi- and tri-objective mathematical and engineering problems as well as bi-objective structural design examples. According to the comparisons between the MOTEO and several well-known algorithms, the proposed algorithm can provide quality Pareto fronts with appropriate accuracy, uniformity, and coverage for multi-objective problems. [ABSTRACT FROM AUTHOR]

Image contrast enhancement (ICE) is an important step in image processing and analysis as the quality of an image plays a pivotal role in human understanding. Moreover, contrast is considered a key aspect for the assessment of picture quality. Incomplete beta function (IBF) is one of the widely used transformations and histogram equalization (HE) is also one of the most popular methods used for this task. However, HE has some limitations as the local contrast of an image cannot be uniformly enhanced. In the present work, a contrast enhancement method is proposed for grey-scale images based on a recent socio-inspired meta-heuristic called political optimizer (PO). The PO algorithm follows the multi-phased process of politics. The exploitative capability of PO is improved by combining it with the adaptive β -hill climbing (A β HC) which is regarded as one of the best local search techniques. The hybridization of these two algorithms is then used to find the optimal values of pixels which can intensify the hidden characteristic of the low-contrast images. The proposed algorithm is tested over a publicly available Kodak image dataset along with some standard images and evaluated in terms of standard metrics. The experimental results demonstrate that the proposed method can successfully outperform many existing methods considered here for comparison. [ABSTRACT FROM AUTHOR]

The present investigation includes the performance enhancement concept of discrete meta-heuristics (MHs) for truss design with concurrent size and topology optimization. The five basic MHs, viz. teaching–learning-based optimization (TLBO), whale optimization algorithm (WOA), dragonfly algorithm (DA), heat transfer search (HTS), and ant lion optimization (ALO) algorithm, are investigated. Often these MHs found incompetent in answering complex problems like concurrent topology and sizing optimization of truss structures due to their poor convergence rate, local optima trap, and higher computation time. Also, the balance between diversification and intensification is very significant for MHs efficiency. A mutation is a strong strategy for balancing the diversification and intensification of MHs and can assist in guiding the population towards the global optimum. Moreover, population diversity can be improved by incorporating the selection of simulated annealing that can reduce the chances of local optima tarp. Therefore, an effective search technique based on a random mutation search along with simulated annealing-based selection is developed. Here, five modified MHs, i.e. the modified DA (MDA), modified ALO (MALO), modified WOA (MWOA), modified HTS (MHTS), and modified TLBO (MTLBO) algorithms, using random mutation search phase and SA-based selection are proposed. The developed techniques are implemented on three standard test problems where dynamic and static constraints and multiple load cases are imposed. The comparative performance of the proposed algorithms and their original algorithms is carried out. An empirical evaluation was done using Friedman rank, and the respective algorithms ranks are assigned. The findings reveal that the new technique results in significant performance enhancement of the various MHs by synchronizing the diversification and intensification of search. [ABSTRACT FROM AUTHOR]

The most commonly used analysis method in performance-based design (PBD) is nonlinear static analysis (NSA). In unsymmetrical 2D frames, unlike for the symmetrical state, NSA should be performed in two lateral loading directions. This complicates the process of achieving a feasible optimal design in addition to increasing the volume of calculations. In this study, to overcoming this issue, a two-step approach is proposed for the optimal PBD of unsymmetrical 2D steel moment-resisting frames (SMRFs). In this approach, in two independent steps, the structure is analyzed with a lateral loading pattern based on the first mode shape in the positive and negative directions. The implementation of the second step is conditional on the satisfactory completion of the first step. The objective function takes into account the differences between successful and unsuccessful steps. The constraints considered are based on the acceptance criteria for SMRF according to FEMA-356 at each performance level. The effectiveness of the proposed approach has been investigated by employing four meta-heuristic optimization algorithms to determine the optimum design for case studies of SMRF structures having three and nine stories. [ABSTRACT FROM AUTHOR]

Over the last decade, the size and complexity of real-world problems have grown dramatically, necessitating more effective tools. Nature-inspired metaheuristic algorithms have proven to be a promising tool for solving such problems due to their performance in a variety of fields. JAYA algorithm is a novel population-based algorithm which could have been able to present reliable results. This is because it does not need any parameters to be set other than the population size and the maximum number of iteration. Despite its positive feedbacks, this algorithm should be modified to witness more efficiency. This paper aims to amend the original version of Jaya to present a high-efficiency version named Powerful Enhanced Jaya (PEJAYA). In other words, the methodology of updating position in Jaya is modified to enhance the convergence and search capabilities. This approach is assessed according to solve 20 well-known benchmark functions, feature selection, and statistical tests. The output results of proposed optimization algorithm are then evaluated by comparing it with other recent algorithms including crow search algorithm (CSA), standard version of JAYA, particle swarm optimization (PSO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), moth-flame optimization (MFO) and sine–cosine algorithm (SCA). Solving a real-world problem is another way of checking the efficiency of this approach with other published works. Prompt escape from local minima, superior convergence, and stability demonstrate that the suggested approach is a very powerful instrument that may be employed in a variety of optimization situations. [ABSTRACT FROM AUTHOR]

The weapon target assignment (WTA) problem is an important task to tactical arrangements in military commitment operations. It describes the optimal method to allocate defense in opposition to threats in fighting situations. It is an NP-complete issue in which no accurate outcome for all conceivable situations is known. The time performance of created algorithms is a major challenge in modeling the WTA problem, which has only been lately considered in related papers. This article improves the recently developed algorithm called improved Tunicate Swarm Algorithm (iTSA) which is inspired by the natural behavior of tunicates to solve the WTA problem. The suggested method is compared with well-known metaheuristic approaches. The experimental findings show that the method presented works better than previous competing metaheuristic approaches. [ABSTRACT FROM AUTHOR]

In this article, we are presenting a new nature-inspired metaheuristic optimization algorithm that we call the Mycorrhiza Tree Optimization Algorithm (MTOA) focused on solving optimization problems. The algorithm is inspired by the relationship between trees and the Mycorrhiza Network (MN). In this relationship, there are interactions such as defense, communication, resource exchange and habitat colonization between the Trees and the MN. The Lotka–Volterra continuous systems (Predator–Prey, Cooperative and Competitive Models) were used for the design of the algorithm. To verify the efficiency of the algorithm, experiments with 36 mathematical functions were performed, but we are only presenting results of the functions with which comparisons were made with other methods. Hypothesis tests were also carried out, where in most of the results the MTOA algorithm was better by 70% and only in the experiments in 100 dimensions it was better by 60%. We believe that the algorithm has the potential to solve problems in control and optimization of neural networks, for which experimentation will be done as future work. [ABSTRACT FROM AUTHOR]

Nowadays, a great deal of attention is paid to metaheuristic algorithms to reach the approximate solution in an acceptable computational time. As one of the recent-developed successful metaheuristics, Social Engineering Optimizer (SEO) algorithm is according to the inspiration of the rules of social engineering to solve approximate optimization problems. In this research, a Modified Social Engineering Optimizer algorithm (MSEO) by using an adjustment operator is proposed in which there are some assessment criteria for defender and attacker to determine and calculate the weight simultaneously for the first time. This enhancement comprises adding adjustment operators to improve the performance of SEO in terms of search accuracy and running time. Most notably, this operator is utilized to make a better new generation and improve the interaction between the search phases. The adjustment operator strategy is also applied to a novel division based on the best person. As an extensive comparison, the suggested algorithm is tested on fourteen standard benchmark functions and compared with ten well-established and recent optimization algorithms as well as the main version of the SEO algorithm. This algorithm is also tested for sensitivities on the parameters. In this regard, a set of engineering applications were provided to prove and validate the MSEO algorithm for the first time. The experimental outcomes show that the suggested algorithm produces very accurate results which are better than the SEO and other compared algorithms. Most notably, the MSEO provides a very competitive output and a high convergence rate. [ABSTRACT FROM AUTHOR]

Molecular biology advances in the past few decades have contributed to the rapid increase in genome sequencing of various organisms; sequence alignment is usually considered the first step in understanding a sequence's molecular function. Understanding such a function is made possible by aligning an unknown sequence with known sequences based on evolution. An optimal alignment adjusts two or more sequences in a way that it could compare the maximum number of identical or similar residues. The two sequence alignments types are Pairwise Sequence Alignment (PSA) and Multiple Sequence Alignment (MSA). The MSA enjoys a higher advantage than PSA since it predicts the similarity in a family of similar sequences, providing more biological data. Moreover, while the dynamic programming (DP) technique is used in PSA to provide the optimal method, it will lead to more complexity if used in MSA. So, the MSA mainly uses heuristic and approximation methods. Such methods include progressive alignment, iterative alignment, Hidden Markov Model (HMM), and Metaheuristic algorithms. This paper presents a genetic algorithm and chromosome design to solve a mathematical model for MSA. This model uses a basis for an optimal solution in different ways, and an X-mediated matrix with binary elements is used to model the sequence. Then, the model is implemented using the Genetic Algorithm method on the web, and the final results indicate the success of GA for the MSA approach. [ABSTRACT FROM AUTHOR]

Model-based testing is an automated process in which executable tests are derived from behavioral models of a system. Model checking is a verification technique to reveal errors in which all reachable states of a system can be generated as state space. In the literature, different approaches suggest using model checkers for model-based testing. Model checker explores all possible system states, so utilizing the various paths in the state-space as test cases seems a promising solution. However, these approaches suffer from two main challenges. The first challenge is state space explosion, which prevents generating all reachable states by the model checker. The second one is generating redundant test cases. Recently, several methods using meta-heuristic and evolutionary approaches have been proposed to cope with these problems. Therefore, exploring a portion of state space using an optimization approach to detect the test objectives can be a proper way to manage the state space explosion and generate an optimal test suite with the least redundancy. In this paper, a method is proposed using a Bayesian optimization algorithm (BOA), and a model checker is as a bed to generate test cases for the service-oriented systems. In the proposed approach, the test suite is a set of paths on the state space starting from an initial state and leading to the state in which all the test objectives are satisfied. In this research, we have implemented BOA with three different structures in GROOVE toolset, an open-source toolset for designing and model checking graph transformation. Experimental results show that our solution generates better results in terms of coverage and speed in different case studies than the existing approaches. [ABSTRACT FROM AUTHOR]

The efficiency of handball goalkeepers is a good predictor of team ranking in tournaments, but despite this, very few studies have been carried out into the performance characteristics of elite goalkeepers. This paper provides the criteria for evaluating a handball goalkeeper and applies a variety of soft-computing methodologies for estimating their weights. More specifically, a fuzzy multi-criteria decision-making method, a metaheuristic optimisation algorithm, and statistical and domain-knowledge-based methods were used to evaluate the actions of goalkeepers during the game. Computer experiments were performed for all the proposed methodologies, using data from the 2020 European Men's Handball Championship, in order to estimate the weights of the indicators. Then, these weights were used to identify the best goalkeeper and identify and rank the top five goalkeepers as determined by the tournament organisers. The results obtained show that using the metaheuristic-based method is extremely helpful in quantifying the expert assessments, which are often challenging to express in a disaggregated form. The other two techniques offer a less optimal but more easily interpretable result for coaches and fans. [ABSTRACT FROM AUTHOR]

The present research proposes a new particle swarm optimization-based metaheuristic algorithm entitled "search in forest optimizer (SIFO)" to solve the global optimization problems. The algorithm is designed based on the organized behavior of search teams looking for missing persons in a forest. According to SIFO optimizer, a number of teams each including several experts in the search field spread out across the forest and gradually move in the same direction by finding clues from the target until they find the missing person. This search structure was designed in a mathematical structure in the form of intragroup search operators and transferring the expert member to the top team. In addition, the efficiency of the algorithm was assessed by comparing the results to the standard representations and a problem with the genetic, grey wolf, salp swarm, and ant lion optimizers. According to the results, the proposed algorithm was efficient for solving many numerical representations, compared to the other algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a new U-shaped assembly line balancing problem is studied. For the first time, the criteria such as equipment cost, number of stations and activity performing quality level are considered to be optimized simultaneously by activity to station and worker to station decisions. For this aim, a multi-objective nonlinear formulation is proposed and its linearized version is also presented. Since, according to the literature, the U-shaped assembly line balancing problem with equipment requirements is an NP-hard problem, the problem of this study is NP-hard too. Because of this complexity, the classical algorithms like simulated annealing, variable neighborhood search, and classical genetic algorithm with a novel encoding/decoding scheme are used as solution approaches. As an extension, two hybrid versions of the proposed classical algorithms are proposed according to the characteristics of the problem. In order to evaluate the proposed meta-heuristics, because the problem is new, some test problems are generated randomly. Computational study of the paper, including sensitivity analysis of the proposed meta-heuristics and final experiments on the test problems, proves the superiority of the hybrid versions of the classical algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a novel optimization algorithm is proposed by hybridizing firefly and whale optimization algorithms and it is applied to the problem of determining optimal design parameters of model predictive control. The developed hybrid algorithm is compared with firefly and whale optimization algorithms considering unimodal, multimodal, composite and CEC-C06 2019 benchmark optimization problems in terms of statistical analysis. Comparison results showed that hybrid algorithm has the best performance for approximately 67% of 39 benchmark functions in finding the minimum in an average of independent 100 experiments for each function. For the functions in which the proposed algorithm gives the best results, approximately 64% improvement is achieved compared to FA and 39% improvement compared to WOA. In terms of convergence speed, in average 2% of improvement is obtained. As an application to engineering problems, optimization of parameters of the model predictive control for inverted pendulum and distillation column is considered and the performance of the proposed algorithm is compared with the performances of firefly algorithm and whale optimization algorithm. For the considered applications, a novel objective function is selected. The selected objective function for hybrid firefly–whale optimization algorithm enables finding the optimal prediction horizon and control horizon as well as the Q and R matrices such that the system responses satisfy desired settling time and maximum overshoot criteria. Most of considered comparisons demonstrate that the proposed hybrid algorithm has better performance. [ABSTRACT FROM AUTHOR]

Mutation is the main operator of differential evolution (DE), as it is responsible for combining the information of distinct solutions to generate a donor vector. Aiming at improving the search effectivity of DE, previous research incorporated the calculation of centroids into the DE mutations. In some of the existing methods, the centroids are simply calculated as the center of some selected solutions (or, the entire population); in other cases, one-step clustering is used to perform a local search, or the centroids themselves are used as actual solutions in the population. As opposed to these methods, in this paper we extend some traditional DE mutation strategies to incorporate centroids calculated by deterministic hierarchical clustering. Experimental results on two sets of well-known benchmark problems show that the proposed cluster-centroid-based mutation strategies outperform, in general, the traditional rand/1 strategy, as well as several metaheuristics from the literature. Therefore, the use of clustering is an effective way to improve the search performance that could be exploited also in other population-based metaheuristics. [ABSTRACT FROM AUTHOR]

Researchers from different domains have developed several metaheuristic algorithms that are inspired by the biological phenomenon. In this work, we propose a novel algorithm that is inspired by the prey–predator interaction of animals. In the proposed algorithm, two random populations are considered as a predator and preys. The energy gain for both predators and preys calculated based on their body mass and the interaction between predators and their mutual preys. The best predator (i.e., predator with the highest energy gain) performs a local search (exploitation). The other solution members in prey population facilitate the exploration of the search space. The proposed algorithm is evaluated by three phases. Firstly, a set of 16 mathematical functions are considered to test different characteristics of predator–prey optimization (PPO) algorithm as a optimizer. Secondly, it is evaluated by seven datasets as a solver for feature selection problem. The proposed algorithm as an global optimizer has been compared with other metaheuristic algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), ant lion optimizer (ALO), gray wolf optimizer (GWO), whale optimization algorithm (WOA), differential evolution (DE), sine–cosine algorithm (SCA), moth flame optimization (MFO), and salp swarm algorithm (SSA). The results indicate that PPO improves convergence rate by 33.8%, 24.2%, 37.3%, 10.5%, 12.4%, 22.7%, 18.6%, 30.7%, and 29.1% in average compared to GA, PSO, ALO, GWO, WOA, DE, SCA, MFO, and SSA, respectively. Furthermore, PPO obtains a higher Performance Index around 5–10% than competitor approaches with respect to exploration–exploitation balance and a high degree of stability. Secondly, the performance of PPO for feature selection issues demonstrates the applicability PPO algorithm in solving real problems with unknown search spaces. [ABSTRACT FROM AUTHOR]

This paper concerns with solving nonlinear boundary value problems with Dirichlet boundary conditions by a novel approximation technique based on Bernstein polynomials and meta-heuristic algorithms. A trial solution is expressed as a weighted sum of Bernstein polynomials of degree n . Some weights of this trial solution are determined by enforcing exact satisfaction of the Dirichlet boundary conditions. The remaining weights are determined such that they minimize the sum of squares of the residuals of the differential equation computed at arbitrary set of interior points in the domain of the problem. Error bounds for the approximate solutions for ordinary and partial differential equations are derived. The resulting optimization problem is solved using meta-heuristic algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), and a hybrid PSO–GA algorithm. The accuracy of the proposed approach is demonstrated by solving some nonlinear boundary value problems including Bratu problems in one- and two-dimensional spaces. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel hybrid metaheuristic for the Knapsack Problem with Forfeits (KPF). KPF is a recently introduced generalization of the Knapsack Problem. In this variant, a penalty cost incurs whenever both items composing a so-called forfeit pair belong to the solution. Our proposed algorithm, called GA–CG Forfeits, combines the strengths of the Genetic and Carousel Greedy paradigms. In this work, we define the algorithm and compare it with two previously proposed heuristics on a set of benchmark instances. In these tests, GA–CG Forfeits provided significantly better solutions than the other two algorithms on all instances. [ABSTRACT FROM AUTHOR]

Parameter-free algorithms are of an increasing interest in applications that require out-of-the-box solving techniques. For instance, scheduling in volatile environments or control parameters optimisation often requires flexible solving approaches without the need to tune or retune the parameters. In addition, parameter-free algorithms are usually beneficial in solving previously unexplored real-world problem instances. In this paper, we propose a parameter-free local search strategy to solve graph colouring, which is the underlying problem for a number of scheduling applications. Two variants of parameter-free local search are computationally investigated: PFLS-A and PFLS-B. Both of these algorithms are based on accepting strictly improving moves and operating as a random walk if no strictly improving move is found. PFLS-A and PFLS-B differ in randomised versus systematic exploration of the neighbourhood of the current solution. Their cooperative variant CPFLS is also proposed. We compare the results of these three algorithms to the results obtained by 13 other local search methods from the literature. CPFLS provides better or equal results as the other local search algorithms in 67.25 % of per-instance comparisons. This is without employing any explicit or implicit parameters that are usually used in metaheuristics. This hints the promising nature of parameter-free metaheuristics not only for this problem but also metaheuristics in general. [ABSTRACT FROM AUTHOR]

One of the most common tasks of groundwater engineers is to estimate aquifer parameters from transient time-drawdown data which are measured during the execution of pumping tests. The classical method consists in the manual superimposition of the observed time-drawdown data over a type-curve, and the appreciation of the goodness-of-fit is left to the visual inspection of the engineer. The present paper proposes a computerized method based on a new hybrid algorithm named CSARao-1. It combines two recent metaheuristic algorithms: the crow search algorithm (CSA) and Rao-1 algorithm for the optimal aquifer parameters estimation, which inherits the advantages of both algorithms. The CSARao-1 hybrid algorithm along with CSA and Rao-1 algorithm was applied in conjunction with both the Theis solution to analyze time-drawdown datasets coming from confined aquifers systems and the Hantush and Jacob solution to analyze datasets coming from leaky aquifers systems. The proposed approach was coded in FORTRAN programming language and evaluated on fourteen time-drawdown datasets coming from different confined and leaky aquifers systems. The results obtained using CSARao-1 hybrid algorithm were compared to those obtained by applying CSA and Rao-1 algorithm separately, and to those recently published. Globally, the proposed CSARao-1 hybrid algorithm was found to exhibit more accuracy, better robustness and higher rate of convergence over the analyzed transient time-drawdown datasets. [ABSTRACT FROM AUTHOR]

Power loss reduction is a very important problem in operating the distribution network. This paper presents a method relied on artificial ecosystem-based optimization (AEO) for the network reconfiguration (NR) problem. The considered goal of the NR problem is to reduce power loss. The performance of the proposed AEO method is evaluated on the 14-node, 69-node and 136-node distribution networks. In addition, the NR approach relied on cuckoo search algorithm (CSA) is also implemented to compare with the proposed AEO method. The statistics result comparisons between AEO and CSA demonstrate that AEO outperforms CSA method with the higher success rate and better quality of the obtained solution in finding the optimal network configuration. In addition, AEO is also better than some previous approaches in the literature in term of the obtained network configuration. Consequently, AEO method is a very effective approach for the NR problem. [ABSTRACT FROM AUTHOR]