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In this paper, we have proposed a hybrid bio-inspired algorithm which takes the merits of whale optimization algorithm (WOA) and adaptive particle swarm optimization (APSO). The proposed algorithm is referred as the hybrid WOA_APSO algorithm. We utilize a convolutional neural network (CNN) for classification purposes. Extensive experiments are performed to evaluate the performance of the proposed model. Here, pre-processing and segmentation are performed on 120 lung CT images for obtaining the segmented tumored and non-tumored region nodule. The statistical, texture, geometrical and structural features are extracted from the processed image using different techniques. The optimized feature selection plays a crucial role in determining the accuracy of the classification algorithm. The novel variant of whale optimization algorithm and adaptive particle swarm optimization, hybrid bio-inspired WOA_APSO, is proposed for selecting optimized features. The feature selection grouping is applied by embedding linear discriminant analysis which helps in determining the reduced dimensions of subsets. Twofold performance comparisons are done. First, we compare the performance against the different classification techniques such as support vector machine, artificial neural network (ANN) and CNN. Second, the computational cost of the hybrid WOA_APSO is compared with the standard WOA and APSO algorithms. The experimental result reveals that the proposed algorithm is capable of automatic lung tumor detection and it outperforms the other state-of-the-art methods on standard quality measures such as accuracy (97.18%), sensitivity (97%) and specificity (98.66%). The results reported in this paper are encouraging; hence, these results will motivate other researchers to explore more in this direction. [ABSTRACT FROM AUTHOR]

This paper proposed an ensemble methodology comprising neural networks, modified differential evolution algorithm and nonlinear autoregressive network with exogenous inputs (NARX) (called neuro-evolutionary NARX or NE-NARX model) for time series forecasting. In NE-NARX, the structure is designed by connecting the neural model and NARX model, and the weight value connection is optimized by a modified differential evolution algorithm. The effectiveness of the proposed NE-NARX model is tested on two well-known benchmark datasets, including the Canadian lynx and the Wolf sunspot. The proposed model is compared to other models, including the classical backpropagation algorithm, particle swarm optimization, differential evolution (DE) and DE variants. Additionally, an ARIMA model is employed as the benchmark for evaluating the capacity of the proposed model. And then, NE-NARX model is used for hourly energy consumption prediction through comparison with other machine learning models including gated recurrent units, convolutional neural networks (CNN), long short-term memory (LSTM), a hybrid CNN-LSTM and sequence-to-sequence learning. Results show convincingly the superiority of the proposed NE-NARX model over other models. [ABSTRACT FROM AUTHOR]

Choosing the optimal portfolio is an ongoing challenging research area and a complex process involving selecting the best investment plan according to various factors, such as investor preferences for expected return, risk, and duration of investments. Although various methods have been presented so far, they failed to obtain a holistic approach to the existing data, and the need for a comprehensive mechanism based on the investor's time preferences is felt. In this paper, by considering the fundamental characteristics, technical indicators, time-series data, and budget constraints, we developed a comprehensive and time-varying methodology to forecast stock prices and form an optimal portfolio. The proposed method consists of recurrent neural networks and multi-objective mathematical programming (MOMP). In this regard, the bidirectional long short-term memory model is adopted and optimized by the particle swarm optimization (PSO) algorithm, called PSO-BiLSTM. Furthermore, the hybrid MOMP models are developed based on long-, mid-, and short-term strategies to provide the optimal portfolio of the stocks with investment constraints. The main objectives of this research were to address the following issues: (1) developing a precise and efficient model to forecast the stocks prices, taking account of fundamental characteristics, technical indicators, time-series data appropriate to the period considered by the investor, (2) providing an optimized time-varying portfolio through developing the hybrid MOMP models, and generally (3) proposing a holistic step-by-step methodology considering three groups of market data and deep learning to apply investment constraints as well as investor's time preferences in the process of building more realistic portfolios. The results highlight that the tuned PSO-BiLSTM method performs better than the conventional methods in all three constructed models using fundamental characteristics, technical indicators, and time-series data. Compared to the conventional methods, the proposed methodology outperforms in generalization power, is more precise in forecasting prices, and provides portfolios with more profit. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is a crucial preprocessing step that aims to eliminate irrelevant and redundant features, reduce the dimensionality of the feature space, and enhance clustering efficiency and effectiveness. FS is categorized as NP-Hard due to the high number of existing solutions. Various metaheuristic methods have been developed to address the FS problem, yielding promising results. Particularly, particle swarm optimization (PSO), an evolutionary computing (EC) approach guided by swarm intelligence, has gained widespread adoption owing to its implementation simplicity and potential for global search. This paper analyzes several variants of PSO algorithms and introduces a new FS method called HPSO. The proposed approach utilizes an asynchronously adaptive inertia weight and an improved constriction factor. Additionally, it incorporates a chaotic map and a MAD fitness function with a feature count penalty to tackle the clustering FS problem. The efficiency of the developed method is evaluated against the genetic algorithm (GA) and well-known variants of PSO algorithms, including PSOs with fixed inertia weights, PSOs with improved inertia weights, PSOs with fixed constriction factors, PSOs with improved constriction factors, PSOs with adaptive inertia weights, and PSO's includes advanced learning exemplars and sophisticated structure topologies. This paper assesses two different reference text data sets, Reuters-21578 and Webkb. In comparison with competitive methods, the proposed HPSO method achieves higher clustering precision and selects a more informative feature set. [ABSTRACT FROM AUTHOR]

In order to improve the effect of rural financial ecological environment evaluation, this paper combines machine learning and improved neural network algorithms to construct a rural financial ecological environment evaluation system. First, this paper optimizes the input layer structure and its initial weight random assignment. The input layer structure is processed by factor analysis, and the initial weight random assignment is optimized by particle swarm optimization. Secondly, this paper constructs a rural financial ecological environment evaluation model based on factor analysis method and PSO-SOM to avoid the defects of traditional SOM algorithm used in financial ecological environment evaluation research. Finally, this paper constructs a system framework based on actual needs and designs experiments to verify the performance of the evaluation system constructed in this paper. The research results show that the system constructed in this paper meets the basic needs of rural financial ecological environment evaluation. [ABSTRACT FROM AUTHOR]

Motor imagery (MI) brain–computer interface (BCI) systems have broad application prospects in rehabilitation and other fields. However, to achieve accurate and practical MI-BCI applications, there are still several critical issues, such as channel selection, electroencephalogram (EEG) feature extraction and EEG classification, needed to be better resolved. In this paper, these issues are studied for lower limb MI which is more difficult and less studied than upper limb MI. First, a novel iterative EEG source localization method is proposed for channel selection. Channels FC1, FC2, C1, C2 and Cz, instead of the commonly used traditional channel set (TCS) C3, C4 and Cz, are selected as the optimal channel set (OCS). Then, a multi-domain feature (MDF) extraction algorithm is presented to fuse single-domain features into multi-domain features. Finally, a particle swarm optimization based support vector machine (SVM) method is utilized to classify the EEG data collected by the lower limb MI experiment designed by us. The results show that the classification accuracy is 88.43%, 3.35–5.41% higher than those of using traditional SVM to classify single-domain features on the TCS, which proves that the combination of OCS and MDF can not only reduce the amount of data processing, but also retain more feature information to improve the accuracy of EEG classification. [ABSTRACT FROM AUTHOR]

Electricity, a kind of clean energy, has been widely used in people's production and daily life. However, it is very difficult to estimate the electricity energy production in advance and store the rest of the electric energy due to the climate, environment, population and other factors. Based on data preprocessing and artificial intelligence optimization algorithm, this paper introduces a combined forecasting method. The proposed method contains six individual methods, and each individual method has its own usage. Singular spectrum analysis (SSA) is adopted to reduce noise from the original data; three individual forecasting methods, Jordan neural network, the echo state network, least squares support vector machine, are applied to obtain the intermediate forecasting results; two optimization algorithms, particle swarm optimization and simulated annealing, are used to optimize the parameters of the combined model. This paper not only validates the superiority of the combined model compared to the single predictive model through the simulation experiments of power load data and electricity price data. The mean absolute percent error (MAPE) of the combined power load and electricity price forecast results are 1.14% and 7.58%, respectively, which are higher than the MAPE error of the corresponding single models prediction results. It has also been verified that the process of eliminating noise by the SSA plays a positive role in the accuracy of the combined forecasting model. In addition, two series of experiments on the power load data lead to two very interesting conclusions. One of the conclusions is that as the size of the test data increases, the prediction accuracy of the model decreases; the other is that the predicted result calculated through the optimized combined weight is better than the combined result calculated using the average weight, and the average weight is used. Weighted combination does not improve the prediction accuracy of a single model. [ABSTRACT FROM AUTHOR]

Various applications of control charts in the field of health-care monitoring and surveillance can be found in the literature. As one of the major categories, monitoring binary outcomes of cardiac surgeries with the aim of logistic regression model for the patients' death probability has been extended by different researchers. For this aim, statistical control charts, such as cumulative sum (CUSUM) chart, are applied as a risk-adjusted method to monitoring patients' mortality rate. However, employing machine learning techniques such as artificial neural network (ANN) has not been paid attention. So, this paper proposes a novel ANN-based control chart with a heuristic training approach to monitor binary surgical outcomes by control charts. Performance of the proposed approach is investigated and compared with existing studies, based on the average run lengths (ARL) criterion and the results demonstrated a superior performance of the proposed approach. Nevertheless, to demonstrate the application of the proposed approach, some real-life applications are also provided in this paper. Furthermore, robustness of the proposed method is investigated by considering Beta distribution for the death rate in addition to the logistic model. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel hybrid heuristic algorithm, particle swarm optimization, and whale optimization algorithm (PSO–WOA), to solve a multi-objective optimization problem relating to point-to-point trajectory planning of space robots. First of all, the kinematics of the space robot is introduced, and the motion of each revolute joint of the manipulator is parameterized by Bézier curve. Then, contradictory objective functions are proposed, and the trajectory planning problem is transformed into a multi-objective optimization problem. The pose of the end-effector at the end of motion is set as the primary objective. The base disturbance, execution time, and manipulability of the end-effector are also taken into account. Furthermore, self-collision avoidance during the motion is also considered. The trajectory planning problem finally comes down to finding an optimal parameter of the Bézier curve for each joint. We propose a novel hybrid PSO–WOA, which is supposed to take advantages of the best of both methods: the exploration feature of the WOA and exploitation feature of the PSO. In order to enhance the performance of the PSO–WOA, the good point set and lévy flight stochastic steps are employed for the initialization and updating process, respectively. The proposed method is applied to generate an optimal trajectory for a redundant free-floating space robot. The simulation results demonstrate the effectiveness of the PSO–WOA. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to present an improved neural network algorithm (INNA) for solving the reliability-redundancy allocation problem (RRAP) with nonlinear resource constraints. In this RRAP, both the component reliability and the redundancy allocation are to be considered simultaneously. Neural network algorithm (NNA) is one of the newest and efficient swarm optimization algorithms having a strong global search ability that is very adequate in solving different kinds of complex optimization problems. Despite its efficiency, NNA experiences poor exploitation, which causes slow convergence and also restricts its practical application of solving optimization problems. Considering this deficiency and to obtain a better balance between exploration and exploitation, searching procedure for NNA is reconstructed by implementing a new logarithmic spiral search operator and the searching strategy of the learner phase of teaching–learning-based optimization (TLBO) and an improved NNA has been developed in this paper. To demonstrate the performance of INNA, it is evaluated against seven well-known reliability optimization problems and finally compared with other existing meta-heuristics algorithms. Additionally, the INNA results are statistically investigated with the Wilcoxon sign-rank test and Multiple comparison test to show the significance of the results. Experimental results reveal that the proposed algorithm is highly competitive and performs better than previously developed algorithms in the literature. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective version of the artificial vultures optimization algorithm (AVOA) for a multi-objective optimization problem called a multi-objective AVOA (MOAVOA). The inspirational concept of the AVOA is based on African vultures' lifestyles. Archive, grid, and leader selection mechanisms are used for developing the MOAVOA. The proposed MOAVOA algorithm is tested oneight real-world engineering design problems and seventeen unconstrained and constrained mathematical optimization problems to investigates its appropriateness in estimating Pareto optimal solutions. Multi-objective particle swarm optimization, multi-objective ant lion optimization, multi-objective multi-verse optimization, multi-objective genetic algorithms, multi-objective salp swarm algorithm, and multi-objective grey wolf optimizer are compared with MOAVOA using generational distance, inverted generational distance, maximum spread, and spacing performance indicators. This paper demonstrates that MOAVOA is capable of outranking the other approaches. It is concluded that the proposed MOAVOA has merits in solving challenging multi-objective problems. [ABSTRACT FROM AUTHOR]

In project portfolio selection (PPS) management, one of the main goals is the optimal management of projects with the least risk and the highest commercial value under risk considerations. Hence, this study considers the weight of each decision criterion, their impacts, and also the uncertainty in decision making. By taking into account all those assumptions, this paper seeks to conduct a PPS with aiming of maximizing the average value as the performance of each project, the rate of development of each project and minimizing the risk of interruption in the implementation of selected projects. The strategic goal of this study is to select robust project portfolios in the long run for less replacement. Accordingly, for attaining all goals, a combined method developed in three stages of PPS; first the weight of criteria from the F-AHP method is determined, next the F-TOPSIS method is used to calculate the relative scores for the projects, and finally a scenario-based robust multi-objective mathematical programming model is considered. This paper has been encountered with two challenges and complexity which is solved by the hybrid method based on the Multi-Choice Goal Programming with Utility Function (MCGP-UF) and the particle swarm optimization (PSO) algorithm (hybrid PSO-MCGP-UF). The results show an improvement in the solution time and the quality of the responses of the proposed method, which helps decision-makers at all stages of the PPS to achieve robustness portfolios in less time. [ABSTRACT FROM AUTHOR]

Due to the characteristics of time-varying traffic and nonlinearity, the short-term traffic flow data are difficult to predict accurately. The purpose of this paper is to improve the short-term traffic flow prediction accuracy through the proposed improved wavelet neural network prediction model and provide basic data and decision support for the intelligent traffic management system. In view of the extremely strong nonlinear processing power, self-organization, self-adaptation and learning ability of wavelet neural network (WNN), this paper uses it as the basic prediction model and uses the particle swarm optimization algorithm for the slow convergence rate and local optimal problem of WNN prediction algorithm. With the advantages of fast convergence, high robustness and strong global search ability, an improved particle swarm optimization algorithm is proposed to optimize the wavelet neural network prediction model. The improved wavelet neural network is used to predict short-term traffic flow. The experimental results show that the proposed algorithm is more efficient than the WNN and PSO–WNN algorithms alone. The prediction results are more stable and more accurate. Compared with the traditional wavelet neural network, the error is reduced by 14.994%. [ABSTRACT FROM AUTHOR]

The aim of this Topical Collection is to present the latest advances in Computational Intelligence either performed in Latin America, or with the important participation of Latin-American researchers. Three different algorithms are tuned: an ant colony optimization algorithm for solving the multidimensional knapsack problem, a genetic algorithm for solving landscapes that follow the NK model (N components and degree K), and a particle swarm optimization algorithm for solving continuous optimization problems. A hybrid feature selection method is employed that integrates a population-based meta-heuristic model, called Grey Wolf optimization, and a single solution-based meta-heuristic model, called the vortex search algorithm. [Extracted from the article]

Machine learning neural network (NN) algorithms are being applied for the past few years in all engineering and science domain, economic sectors, image processing synthesis and analysis, and so on. Due to this, this paper work considered employing these machine learning neural models for forecasting application in respect of renewable energy applications and in particular focused on the forecasting of wind speed. It is crucial for power grid dispatchability, stability, and controllability, and its precision is necessary for making the most use of wind resources. This article describes the development of a novel hybrid forecasting system to anticipate the wind speed of real-time wind farm datasets using a hybrid probabilistic neural network (PNN) model and optimization method. The particle swarm optimization (PSO)–ant lion optimization technique is utilized to modify the designed adaptive PNN in order to optimize the weight parameters. The machine learning model employed in this study is an adaptive PNN with a probability density function and a decision-making function that adhere to Bayes' rule to achieve faster convergence and higher prediction accuracy. The obtained simulation results show that the recommended hybrid optimized PNN model outperforms the other techniques that were evaluated and compared from the literature. This establishes that the built optimized adaptive PNN model is applicable and suitable to serve as a predictor, as shown by the outcome of the statistical analysis. [ABSTRACT FROM AUTHOR]

Over the past decade, the Internet of Things (IoT) has become a necessary technology increasingly applied in many fields of research and development, such as smart cities and homes, health, industry, agriculture, security, and surveillance. In IoT systems, the use of sensors is considered an important common manner in which all devices communicate with a wireless sensor network to form an information system that comprises a massive number of sensor nodes performing accurately to create a smart decision-making method. However, these sensor nodes might be employed in severe environments, where replacing or recharging their batteries is considered an impossible mission. Simultaneously, the limitation of energy resources in sensor nodes presents a challenging issue that reduces the lifespan of individual nodes and the overall network system as a result of energy depletion. These obstacles necessitate energy-efficient routing protocols. According to the literature review, various routing protocols have been introduced, especially those that use clustering techniques. However, they have many drawbacks due to the way of selecting the cluster head (CH), which results in consuming energy dramatically, and consequently shortening the network lifetime. Additionally, instead of using the static sink, which was inefficient in collecting data, many researchers studied the behavior of the mobile sink (MS), which also has many downsides that negatively impact network performance. This paper presents a novel energy-conscious protocol for clusters that incorporates an adaptive movement for mobile stations and utilizes particle swarm optimization (PSO). The circular network area is divided into clusters, each of which has an elected CH based on the PSO technique. The MS aims to distribute energy among nodes to prevent hotspot issues. To achieve better coverage, it moves in a circular pattern with a constant angular velocity, starting from the center of the network area and moving forward and backward along the radius of the network. This research conducts intensive simulations, which run on MATLAB R2018, to assess the performance of our proposed protocol and compare its results with those of pertinent works. The results obtained are encouraging and demonstrate that the protocol we proposed surpasses its counterparts by significantly extending the lifespan of the network. [ABSTRACT FROM AUTHOR]

As a common data-driven method, artificial neural networks have been widely used in electricity spot price forecasting. To improve the accuracy of short-term forecasts, this paper proposes an optimized artificial neural network model for monthly electricity spot prices forecasting. A genetic algorithm is applied to regulate the weights and biases parameters of the artificial neural network structure. This study uses various historical dataset at monthly periods selected from Nordic electricity spot prices. For efficiency comparison, one-step ahead forecast method based on Schwartz-Smith stochastic model and two other prediction models, artificial neural network trained by Levenberg–Marquardt and particle swarm optimization algorithms are also presented. The comparison results show that the prediction model based on the genetic optimization algorithm is more accurate than the other prediction models. The proposed forecasting model can be considered as an alternative technique for the electricity spot price forecasting in the Nordic regions. [ABSTRACT FROM AUTHOR]

Digital data plays a paramount part in various fields of life, in which multimedia data represent the majority of transferred digital data via communication networks. Consequently, cryptographers and researchers have paid attention to the security of multimedia. Chaotic models are utilised intensely with optimisation techniques for designing modern multimedia cryptosystems, but the role of optimisation approaches is to improve experimental outcomes rather than complex computations in designing cryptosystems. Amidst the rapid evolution of quantum resources, current multimedia cryptosystems may be cracked because their construction relies on mathematical paradigms. This paper introduces a robust image cryptosystem that leverages bit-level permutation, particle swarm optimization, the 3D logistic system, and quantum walk. In the presented cryptosystem, the probability distribution generated by quantum walk and the chaotic sequences produced by the 3D logistic function are utilized as inputs for a customized particle swarm optimization algorithm. The resulting keystreams are then employed in both the permutation phase (at the pixel and bit levels) and substitution phase of the image cryptosystem. Experimental results indicate that the suggested cryptosystem has high-security contra different raids such as differential, statistical, and occlusion attacks. The average outcome for local entropy, global entropy, NPCR (Number of Pixel Change Rate), UACI (Unified Average Changed Intensity), Chi-square, and encryption speed are measured at 7.9025, 7.99985, 99.61306%, 33.4657%, 256.960725, and 1.7081 megabits per second, respectively. [ABSTRACT FROM AUTHOR]

Assembly sequence planning is one of the key issues in DFA and computer-aided assembly process planning research for concurrent engineering. The purpose of this paper is to solve the problem of insufficient individual intelligence in evolutionary algorithms for assembly sequence planning, and a evolutionary algorithm for assembly sequence planning is designed. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the hybrid assembly sequence planning and assembly line balance problems. According to the assembly sequence problem, the number of assembly tool changes and the number of assembly orientation changes are transformed into the operation time of the assembly line. At the same time, the transportation of heavy parts in the assembly balance problem is considered. Then, by extracting the connection relationship and information of the parts, the disassembly method is used to inversely obtain the disassembly support matrix, and then, it is used to obtain the priority relationship diagram of the assembly operation tasks that indicate the order constraints of the job tasks on the assembly line. Aiming at the shortcoming that particle swarm optimization algorithm is easy to fall into local optimum, a various population strategy is adopted to shorten the evolution stagnation time, improve the evolution efficiency of particle swarm optimization algorithm, and enhance the optimization ability of the algorithm. Combined with the three evaluation indicators of assembly geometric feasibility, assembly process continuity, and assembly tool change times, a fitness function is constructed to achieve multi-objective optimization. Finally, experiments show that the multi-agent evolutionary algorithm is incorporated into the planning process to obtain an accurate solution through the various population strategy–particle swarm optimization algorithm, which proves the feasibility of the compound algorithm and has better performance in solving assembly sequence planning problems. [ABSTRACT FROM AUTHOR]

Recently, Drones and UAV research were becoming one of the interest topics for academia and industry, where it has been extensively addressed in the literature back the few years. Path planning of drones in an area with complex terrain or unknown environment and restricted by some obstacles is one of the most problems facing the operation of drones. The problem of path planning is not only limited to searching for an appropriate path from the starting point to the destination but also related to how to choose an ideal path among all available paths and provide a mechanism for collision avoidance. By considering how to construct the best path, several related issues need to be taken into account, that relate to safety, obstacle avoidance, response speed to overtake obstacles, etc. Swarm optimization algorithms have been used to provide intelligent modeling for drone path planning and enable to build the best path for each drone. This is done according to the planning and coordination dimensions among the swarm members. In this paper, we have discussed the features and characteristics of different swarm optimization algorithms such as ant colony optimization (ACO), fruit fly optimization algorithm (FOA), artificial bee colony (ABC), and particle swarm optimization (PSO). In addition, the paper provides a comprehensive summary related to the most important studies on drone path planning algorithms. We focused on analyzing the impact of the swarm algorithm and its performance in drone path planning. For that, the paper presented one of the most used algorithms and its models employed to improve the trajectory of drones that rely on swarm intelligence and its impact on the optimal path cost of drones. The results of performance analysis for the ACO algorithm in a 3D and 2D-dimensional environment are illustrated and discussed, and then the performance evaluation of the ACO is compared to the enhanced ACO algorithm. The proposed algorithm achieves fast convergence, accelerating the process of path planning. [ABSTRACT FROM AUTHOR]

The changes in China's stock market are inseparable from the country's economic development and macroeconomic regulation and control and have far-reaching significance in promoting China's national economic growth. Compared with the Western developed capital market, China's current stock market's main smart investment strategy still has certain defects. Based on the SVM model, this paper establishes a predictive model that combines kernel parameters and parameter optimization to model. The mesh search method, genetic algorithm, and particle swarm optimization algorithm are used to optimize the parameters of the SVM under various kernel functions such as radial basis kernel function. The algorithm and particle swarm optimization algorithm optimize the parameters of the SVM to strengthen the applicability of the model in practice. The empirical results show that under the three-parameter optimization algorithms, the prediction results are higher than the random prediction accuracy, which indicates that it is effective to optimize the model by adjusting the parameters of the SVM. Among them, the SVM using the genetic algorithm parameter optimization under the radial basis kernel function shows the better prediction effect, which is the closest to the real value in the stock market forecast. The particle swarm algorithm supports the vector machine to predict the effect is slightly lower than the grid. Search method. In addition, through comparison experiments, the guess accuracy of BP neural network is worse than that of the support vector machine model before the adjustment. Finally, this paper uses the well-trained model to plan the stock smart investment plan. [ABSTRACT FROM AUTHOR]

Feature extraction, feature clustering, feature selection are suitable to enhance learning performance, reduce computational complexity, create better generalizable models, and reduce required storage. Although there are several feature reduction techniques, still it remains one of the research hot spots in the field of data mining and machine learning groups. Owl search algorithm (OSA) is one of the recent metaheuristic optimization algorithms that mimic the hunting mechanism of owls in dark. However, OSA suffers from the same problem faced by many other optimization algorithms and tends to fall in local optima and premature convergence. To overcome these problems, two improvements for OSA algorithm are proposed in this paper. The first improvement uses a fuzzy system which is responsible to tune a control parameter in the updating phase of OSA. The second improvement includes the fuzzy system along with modifying the updating equation of OSA to enhance the exploration activity. In addition, this paper presents a new feature extraction technique for regression problems based on the improved OSA, called Fuzzy Owl Clustering Dimension Reduction (FOCDR). We apply a method that uses three weighting methods (i.e., soft, hard, and mixed) to extract new features based on the generated clusters. The experiment is divided into two parts. In the first part, the performance of the OSA algorithm and two improvement versions are analyzed with ten benchmark functions. The results show that the proposed versions on average can improve the convergence rate by 6.75% and 14.2% compared to OSA in solving complex problems. The second part is conducted to show FOCDR's ability for feature selection problems. The effectiveness of FOCDR has been evaluated using four benchmark datasets and a real-world case study. [ABSTRACT FROM AUTHOR]

The application of soft computing techniques can be largely found in engineering sciences. These include the design and optimization of navigation systems for use in land, sea, and air transportation systems. In this paper, an attempt is made to leverage on novel metaheuristic optimization approaches for designing integrated navigation systems. For this purpose, a simplified version of the inclined planes system optimization (called SIPO) algorithm alongside its two standard and modified versions are used in comparison with the two conventional methods of genetic algorithm and particle swarm optimization. Considerations are made on an INS/GNSS problem with IMU MEMS modules. Outputs are presented in terms of statistical and performance indicators, such as runtime, fitness, convergence, navigation accuracy (velocity, latitude, longitude, altitude, roll, pitch, yaw), and routing along with the ranking of algorithms. Competitive performance and relative superiority of the standard IPO over other methods in evaluating results have been confirmed. So that compared to other state-of-the-art algorithms (GA, PSO, IPO, and MIPO), the best runtime rank with a value of 6/4 by SIPO and the best performance rank of fitness, navigation accuracy for the two assumed IMU modules, and the total rank with values of 4/4, 149/60, 165/60, and 332/128 obtained by IPO, respectively. [ABSTRACT FROM AUTHOR]

The evolutionary game theory aims to simulate different decision strategies in populations of individuals and to determine how the population evolves. Compared to strategies between two agents, such as cooperation or noncooperation, strategies on multiple agents are rather challenging and difficult to be simulated via traditional methods. Particularly, in a knowledge spillover problem (KSP), cooperation strategies among more than hundreds of individuals need to be simulated. At the same time, the brain storm optimization (BSO) algorithm, which is a data-driven and model-driven hybrid paradigm, has the potential to simulate the complex behaviors in a group of simple individuals. In this paper, a modified BSO algorithm has been used to solve KSP from the perspective of evolutionary game theory. Knowledge spillover (KS) is the sharing or exchanging of knowledge resources among individuals. Firstly, the KS and evolutionary game theory were introduced. Then, the KS model and KS optimization problems were built from the evolutionary game perspective. Lastly, the modified BSO algorithms were utilized to solve KS optimization problems. Based on the applications of BSO algorithms for KSP, the properties of different swarm optimization algorithms can be understood better. More efficient algorithms could be designed to solve different real-world evolutionary game problems. [ABSTRACT FROM AUTHOR]

Computations that mimic nature are known as nature-inspired computing. Nature presents a wealthy source of thoughts and ideas for computing. The use of natural galvanized techniques has been found to provide machine solutions to complex problems. One of the challenging issues among researchers is high-dimensional data which contains a large number of unwanted, redundant, and irrelevant features. These redundant or unwanted features reduce the accuracy of machine learning models. Therefore, to solve this problem nowadays metaheuristic techniques are being used. The paper presents both surveys as well as comparison of five metaheuristic algorithms for feature selection. A wrapper-based feature selection approach using five nature-inspired techniques for feature selection has been applied. The binary version of the five swarm-based nature-inspired algorithms (NIAs), namely particle swarm optimization, whale optimization algorithm (WOA), grey wolf optimization (GWO), firefly algorithm, and bat algorithm. WOA and GWO are recent algorithms used for finding optimal feature subsets when there is no empirical information. The S-shape transfer function has been used to convert the continuous value to binary form and K-nearest neighbor is used to calculate the classification accuracy of selected feature subsets. To validate the results of the selected NIAs eleven benchmark datasets from the UCI repository are used. The strength of each NIA has been verified using a nonparametric test called the Friedman rank and Holm test. p value obtained shows that WOA is statistically significant and performs better than other models. [ABSTRACT FROM AUTHOR]

This paper proposes the multi-objective variant of the recently-introduced fitness dependent optimizer (FDO). The algorithm is called a multi-objective fitness dependent optimizer (MOFDO) and is equipped with all five types of knowledge (situational, normative, topographical, domain, and historical knowledge) as in FDO. MOFDO is tested on two standard benchmarks for the performance-proof purpose: classical ZDT test functions, which is a widespread test suite that takes its name from its authors Zitzler, Deb, and Thiele, and on IEEE Congress of Evolutionary Computation benchmark (CEC-2019) multi-modal multi-objective functions. MOFDO results are compared to the latest variant of multi-objective particle swarm optimization, non-dominated sorting genetic algorithm third improvement (NSGA-III), and multi-objective dragonfly algorithm. The comparative study shows the superiority of MOFDO in most cases and comparative results in other cases. Moreover, MOFDO is used for optimizing real-world engineering problems (e.g., welded beam design problems). It is observed that the proposed algorithm successfully provides a wide variety of well-distributed feasible solutions, which enable the decision-makers to have more applicable-comfort choices to consider. [ABSTRACT FROM AUTHOR]

Researchers have adapted the conventional deep learning classification networks to generate Fully Conventional Networks (FCN) for carrying out accurate semantic segmentation. However, such models are expensive both in terms of storage and inference time and not readily employable on edge devices. In this paper, a compressed version of VGG16-based Fully Convolution Network (FCN) has been developed using Particle Swarm Optimization. It has been shown that the developed model can offer tremendous saving in storage space and also faster inference time, and can be implemented on edge devices. The efficacy of the proposed approach has been tested using potato late blight leaf images from publicly available PlantVillage dataset, street scene image dataset and lungs X-Ray dataset and it has been shown that it approaches the accuracies offered by standard FCN even after 851× compression. [ABSTRACT FROM AUTHOR]

In this paper, we present two metaheuristic evolutionary algorithms-based approaches to position the customer order decoupling point (CODP) in smart mass customization (SMC). SMC tries to autonomously mass customize and produce products per customer needs in Industry 4.0. SMC shown here is from the perspective of arriving at a CODP during manufacturing process flow designs meant for fast moving and complex product variants. Learning generally needs several repetitive cycles to break the complexity barrier. We make use of fruit fly and particle swarm optimization (PSO) evolutionary algorithms with the help of MATLAB programming to constantly search better fitting consecutive process modules in manufacturing chain. CODP is optimized by increasing modularity and reducing complexity through evolutionary concept. Learning-based PSO iterations are performed. The methods shown here are recommended for process flow design in a learning-oriented supply chain organization which can involve in-house and outsourced manufacturing steps. Finally, a complexity reduction model is presented which can aid in deploying this concept in design of supply chain and manufacturing flows. [ABSTRACT FROM AUTHOR]

Discovering a hearing disorder at an earlier intervention is critical for reducing the effects of hearing loss and the approaches to increase the remaining hearing ability can be implemented to achieve the successful development of human communication. Recently, the explosive dataset features have increased the complexity for audiologists to decide the proper treatment for the patient. In most cases, data with irrelevant features and improper classifier parameters causes a crucial influence on the audiometry system in terms of accuracy. This is due to the dependent processes of these two, where the classification accuracy performance could be worsened if both processes are conducted independently. Although the filter algorithm is capable of eliminating irrelevant features, it still lacks the ability to consider feature reliance and results in a poor selection of significant features. Improper kernel parameter settings may also contribute to poor accuracy performance. In this paper, an ensemble filters feature selection based on Information Gain (IG), Gain Ratio (GR), Chi-squared (CS), and Relief-F (RF) with harmonize optimization of Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is presented to mitigate these problems. Ensemble filters are utilized so that the initial top dominant features relevant for classification can be considered. Then, PSO and SVM are optimized simultaneously to achieve the optimal solution. The results on a standard Audiology dataset show that the proposed method produces 96.50% accuracy with optimal solution compared to classical SVM, which signifies the proposed method is effective in handling high dimensional data for hearing disorder prediction. [ABSTRACT FROM AUTHOR]

Protecting water resources from pollution is one of the most important challenges facing water management researchers. The governing equation for river pollution is mostly the advection–dispersion equation, with considering the longitudinal dispersion coefficient as its most important effective parameter. The purpose of this paper is to develop a new framework for accurate prediction of the longitudinal dispersion coefficient of rivers based on artificial intelligence (AI) methods. To do this, we used a combination of multilayer perceptron (MLP), one of the most robust neural networks, and a novel metaheuristic algorithm, namely Harris hawk optimization (HHO). Besides, two optimized MLP models with particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) were utilized to demonstrate the accuracy of the proposed model. To evaluate the developed models, 164 series of data collected from previous studies, including hydraulic and geometric parameters of rivers, were used. The indicated results proved the efficiency of the HHO to improve the optimum auto-selection of the AI models. Thus, the recorded results show very high accuracy of the newly developed model, MLP-HHO compared to others. Furthermore, to increase the prediction accuracy, a K-means clustering technique is coupled with MLP-HHO model during dividing the data to train and test categories. The proposed hybrid K-means-MLP-HHO model with coefficient of determination (R2) and root mean square error (RMSE), of 0.97 and 30.94 m2/s, respectively, significantly outperformed all existing and AI-based models. Furthermore, the sensitivity analysis showed that the flow width is the most influential factor in predicting the longitudinal dispersion coefficient. [ABSTRACT FROM AUTHOR]

Breast cancer, which is also the leading cause of death among women, is one of the most common forms of the disease that affects females all over the world. The discovery of breast cancer at an early stage is extremely important because it allows selecting appropriate treatment protocol and thus, stops the development of cancer cells. In this paper, a new patients detection strategy has been presented to identify patients with the disease earlier. The proposed strategy composes of two parts which are data preprocessing phase and patient detection phase (PDP). The purpose of this study is to introduce a feature selection methodology for determining the most efficient and significant features for identifying breast cancer patients. This method is known as new hybrid feature selection method (NHFSM). NHFSM is made up of two modules which are quick selection module that uses information gain, and feature selection module that uses hybrid bat algorithm and particle swarm optimization. Consequently, NHFSM is a hybrid method that combines the advantages of bat algorithm and particle swarm optimization based on filter method to eliminate many drawbacks such as being stuck in a local optimal solution and having unbalanced exploitation. The preprocessed data are then used during PDP in order to enable a quick and accurate detection of patients. Based on experimental results, the proposed NHFSM improves the efficiency of patients' classification in comparison with state-of-the-art feature selection approaches by roughly 0.97, 0.76, 0.75, and 0.716 in terms of accuracy, precision, sensitivity/recall, and F-measure. In contrast, it has the lowest error rate value of 0.03. [ABSTRACT FROM AUTHOR]

Due to the growing demand for clean and sustainable energy sources, there has been an increasing interest in solar cells and photovoltaic panels. Nevertheless, determining the right design parameters to achieve the most efficient energy output that aligns with the energy system's needs can be quite challenging. This complexity arises from the intricate models and the inherent inaccuracies in the available information. To tackle this challenge, this paper introduces the adaptive sine–cosine particle swarm optimization algorithm (ASCA-PSO) as a method for estimating the parameters of solar cells and photovoltaic modules. The ASCA-PSO approach combines the strengths of the SCA and PSO algorithms in a two-tier process. In this process, SCA search agents explore the search space, while the PSO search agents leverage the outcomes derived from SCA exploration. This study evaluates the effectiveness of ASCA-PSO in accurately estimating the parameters of single- and double-diode models using data from two commercial solar cells. The findings are compared with those of cutting-edge methods. It is demonstrated that ASCA-PSO can identify global solutions for multifaceted and intricate objective functions. Furthermore, it proves to be a viable option for designing solar cells even in the presence of noise. [ABSTRACT FROM AUTHOR]

Intelligent swarm optimization algorithms have become increasingly common due to their success in solving real-world problems. Dwarf Mongoose Optimization (DMO) algorithm is a newly proposed intelligent swarm optimization algorithm in recent years. It was developed for continuous optimization problem solutions in its original paper. But real-world problems are not always problems that take continuously variable values. Real-world problems are often problems with discrete variables. Therefore, heuristic algorithms proposed for continuous optimization problems need to be updated to solve discrete optimization problems. In this study, DMO has been updated for binary optimization problems and the Binary DMO (BinDMO) algorithm has been proposed. In binary optimization, the search space consists of binary variable values. Transfer functions are often used in the conversion of continuous variable values to binary variable values. In this study, twelve different transfer functions were used (four Z-shaped, four U-shaped, and four Taper-shaped). Thus, twelve different BinDMO variations were obtained (BinDMO1, BinDMO2, ..., BinDMO12). The achievements of BinDMO variations were tested on thirteen different unimodal and multimodal classical benchmark functions. The effectiveness of population sizes on the effectiveness of BinDMO was also investigated. When the results were examined, it was determined that the most successful BinDMO variation was BinDMO1 (with Z1-shaped transfer function). The most successful BinDMO variation was compared with three different binary heuristic algorithms selected from the literature (SO, PDO, and AFT) on CEC-2017 benchmark functions. According to the average results, BinDMO was the most successful binary heuristic algorithm. This has proven that BinDMO can be chosen as an alternative algorithm for binary optimization problems. [ABSTRACT FROM AUTHOR]

The chimp optimization algorithm (ChOA) is a nature-inspired algorithm that imitates chimpanzees' individual intelligence and hunting behaviors. In this algorithm, the hunting process consists of four steps: driving, blocking, chasing, and attacking. Because of the novelty of ChOA, the steps of the hunting process have been modeled in the simplest possible way, leading to slow and premature convergence similar to other iterative algorithms. This paper proposes six spiral functions and introduces two novel hybrid spiral functions (SEB-ChOA) to rectify the abovementioned deficiencies. The SEB-ChOAs' performance is evaluated on 23 standard benchmarks, 20 benchmarks of IEEE CEC-2005, 10 cases of IEEE CEC06-2019 test-suite, and 12 constrained real-world engineering problems of IEEE CEC-2020. The SEB-ChOAs are compared with three groups of optimization algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as the most well-known optimization algorithms, Slime Mould Algorithm (SMA), Marine Predators Algorithm (MPA), Ant Lion Optimization (ALO), Henry Gas Solubility Optimization (HGSO), as almost novel optimization algorithms, and jDE100 and DISHchain1e+12, as winners of IEEE CEC06-2019 competition, and also EBOwithCMAR and CIPDE as superior secondary optimization algorithms. The SEB-ChOAs reached the first rank among almost all benchmarks and demonstrated very competitive results compared to jDE100 and DISHchain1e+12 as the best-performing optimizers. Statistical evidence shows that the SEB-ChOA outperforms the PSO, GA, SMA, MPA, ALO, and HGSO optimizers while producing results comparable to those of the jDE100 and DISHchain1e+12 algorithms. [ABSTRACT FROM AUTHOR]

Document classification is becoming increasingly essential for the vast number of documents available in digital libraries, emails, the Internet, etc. Textual records frequently contain non-discriminative (noisy and irrelevant) terms that are also high-dimensional, resulting in higher computing costs and poorer learning performance in Text Classification (TC). Feature selection (FS), which tries to discover discriminate terms or features from the textual data, is one of the most effective tasks for this issue. This paper introduces a novel multi-stage term-weighting scheme-based FS model designed for the single-label TC system to obtain the optimal set of features. We have also developed a hybrid deep learning fine-tuning network based on Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) for the classification stage. The FS approach is worked on two-stage criteria. The filter model is used in the first stage, and the multi-objective wrapper model, an upgraded version of the Whale Optimization Algorithm (WOA) with Particle Swarm Optimization (PSO), is used in the second stage. The objective function in the above wrapper model is based on a tri-objective principle. It uses the Pareto front technique to discover the optimal set of features. Here in the wrapper model, a novel selection strategy has been introduced to select the whale instead of the random whale. The proposed work is evaluated on four popular benchmark text corpora, of which two are binary class, and two are multi-class. The suggested FS technique is compared against classic Machine Learning (ML) and deep learning classifiers. The results of the experiments reveal that the recommended FS technique is more effective in obtaining better results than the other results. [ABSTRACT FROM AUTHOR]

In recent years, the hyper-competitive marketplace has led to a drastic enhancement in the importance of the supply chain problem. Hence, the attention of managers and researchers has been attracted to one of the most crucial problems in the supply chain management area called the supply chain network design problem. In this regard, this research attempts to design an integrated forward and backward logistics network by proposing a multi-objective mathematical model. The suggested model aims at minimizing the environmental impacts and the costs while maximizing the resilience and responsiveness of the supply chain. Since uncertainty is a major issue in the supply chain problem, the present paper studies the research problem under the mixed uncertainty and utilizes the robust possibilistic stochastic method to cope with the uncertainty. On the other side, since configuring a supply chain is known as an NP-Hard problem, this research develops an enhanced particle swarm optimization algorithm to obtain optimal/near-optimal solutions in a reasonable time. Based on the achieved results, the developed algorithm can obtain high-quality solutions (i.e. solutions with zero or a very small gap from the optimal solution) in a reasonable amount of time. The achieved results demonstrate the negative impact of the enhancement of the demand on environmental damages and the total cost. Also, according to the outputs, by increasing the service level, the total cost and environmental impacts have increased by 41% and 10%, respectively. On the other hand, the results show that increasing the disrupted capacity parameters has led to a 17% increase in the total costs and a 7% increase in carbon emissions. [ABSTRACT FROM AUTHOR]

This paper presents a physics-guided residual recurrent neural network (PGRRNN) and graphics processing unit (GPU)-accelerated model predictive control (MPC) framework to combat two specific challenges in artificial neural network (ANN)-based nonlinear MPC of high-rate dynamics systems, i.e., low control latency and insufficient model accuracy or generalization ability. Different from traditional ANN models, PGRRNN utilizes approximate physics-based (PB) models (with parameter uncertainty) as a backbone to impose physical constraints/guidance for future state prediction, and reconciles the difference between PB model approximation and data collected from actual systems by propagating their residuals through a multilayer recurrent neural network, hence improving its accuracy and generalization and alleviating data volume requirement. For computing acceleration, both PGRRNN and particle swarm optimization (PSO) are implemented on a GPU platform to make use of its massive parallel processing threads. Numerical experiments for MPC trajectory tracking of a quadcopter are used to examine accuracy and robustness of PGRRNN, and its performance is compared with other ANN models and approximate PB models. PGRRNN outperforms the other models in both ideal and realistic environments, exhibiting 2–3 times lower tracking error than the pure data-driven model. Furthermore, it is demonstrated that GPU-based PSO is able to synthesize control signals at a rate of greater than 50 Hz and can be a promising approach for ANN-based nonlinear MPC. [ABSTRACT FROM AUTHOR]

This paper proposes a method for training Takagi–Sugeno fuzzy systems using sparse regressions and particle swarm optimization. The fuzzy system is considered with Gaussian fuzzy sets in the antecedents and high-order polynomials in the consequents of the inference rules. The proposed method can be applied in two variants: without or with particle swarm optimization. In the first variant, ordinary least squares, ridge regression, or sparse regressions (forward selection, least angle regression, least absolute shrinkage and selection operator, and elastic net regression) determine the polynomials in the fuzzy system in which the fuzzy sets are known. In the second variant, we have a hybrid method in which particle swarm optimization determines the fuzzy sets, while ordinary least squares, ridge regression, or sparse regressions determine the polynomials. The first variant is simpler to implement but less accurate, the second variant is more complex, but gives better results. A new quality criterion is proposed in which the goal is to make the validation error and the model density as small as possible. Experiments showed that: (a) the use of sparse regression and/or particle swarm optimization can reduce the validation error and (b) the use of sparse regression may simplify the model by zeroing some of the coefficients. [ABSTRACT FROM AUTHOR]

In the current paper, we have developed two bio-inspired fuzzy clustering algorithms by incorporating the optimization techniques, namely differential evolution and particle swarm optimization. Both these clustering techniques can detect symmetrical-shaped clusters utilizing the established point symmetry-based distance measure. Both the proposed approaches are automatic in nature and can detect the number of clusters automatically from a given dataset. A symmetry-based cluster validity measure, F-Sym-index, is used as the objective function to be optimized in order to automatically determine the correct partitioning by both the approaches. The effectiveness of the proposed approaches is shown for automatically clustering some artificial and real-life datasets as well as for clustering some real-life gene expression datasets. The current paper presents a comparative analysis of some meta-heuristic-based clustering approaches, namely newly proposed two techniques and the already existing automatic genetic clustering techniques, VGAPS, GCUK, HNGA. The obtained results are compared with respect to some external cluster validity indices. Moreover, some statistical significance tests, as well as biological significance tests, are also conducted. Finally, results on gene expression datasets have been visualized by using some visualization tools, namely Eisen plot and cluster profile plot. [ABSTRACT FROM AUTHOR]

Based on the traditional boring bar, a boring bar with friction damper is proposed in the paper. Firstly, the frequency response under different pressures is computed primarily based on the theory, which shows that the proposed boring bar has a certain vibration reduction effect. Secondly, the finite element model of the boring bar is built, and the first 6-order modes are computed, whose results are compared with the experimental value. As a result, the virtual reality of the boring bar is achieved. They are consistent with each other, which show that the finite element model is reliable. Then, the experimental cutting process of the boring bar is researched, which is compared with the simulation model with good coincidence. It is found from the result that the cutting simulation model of the boring bar is effective. Later, based on the verified simulation model, the positive pressure between the friction vibrator and boring bar, cutting speed, feed rate, back cutting depth and other parameters are changed to study the vibration reduction effects of the boring bar with friction damper. PSO (particle swarm optimization)-BP (backpropagation) neural network is then used to optimize the cutting process of the boring bar, and the optimal cutting parameters can be obtained. Finally, these optimized parameters are applied in the boring bar, the vibration reduction effect of the boring bar is verified by means of experiments, and the corresponding result shows that the proposed optimization in this paper is feasible. We can obtain higher quality work piece when we use this boring bar in the actual engineering. [ABSTRACT FROM AUTHOR]

Deep large excavations in urban areas are an important engineering challenge, whereas secant piling techniques are among the best solutions to have a safe workplace environment. Optimal design of these structures will increase efficiency as well as reduce costs. In this paper, the optimum design of secant pile walls as a retaining system of a deep excavation pit is evaluated. For this purpose, an on-going Tabriz metro station project is investigated as the case study. The structural piles are made of steel material with a hollow pipe section. A layer of struts is also considered for the horizontal bracing of the excavation pit. A detailed finite element model is developed in the OpenSees platform in order to perform static analyses. The optimization of the retaining system is conducted by the mean of four different metaheuristic algorithms including genetic, particle swarm optimization, bee, and biogeography-based optimization algorithms. The total cost of retaining structures is considered as an objective function, which should be minimized in the design space of the variables. The results highlight the excellence of the bees algorithm in achieving a minimum cost, lower dispersion, and rapid convergence rate. The optimum placement of the bracing system and its effect on the soil shear stress are also investigated based on the obtained optimal results. [ABSTRACT FROM AUTHOR]

The ability to accurately estimate energy consumption in the medium and long term based on actual indications is critical for countries to plan and prioritize their futures and take the appropriate actions. This paper proposes a new modified artificial bee colony (M-ABC) method that can adaptively select an optimal search equation to more accurately estimate Turkey's energy consumption. In the study, linear (M-ABCL) and quadratic (M-ABCQ) mathematical models were developed, and gross domestic product (GDP), population, import, and export data were used as input parameters for energy demand estimation. The weight values in the regression models are calculated according to the objective function with the proposed M-ABC. In this way, the weight values that will produce estimations with the lowest error according to the selected years are found, and then the most appropriate energy demand estimations are made. We compared the performance of our proposed M-ABC algorithm with ant colony optimization (ACO), particle swarm optimization (PSO), and hybrid ACO and PSO (HAP) algorithms. In addition, various estimation suggestions are presented under four different scenarios using input parameters. According to the results, the models suggested with the M-ABC algorithm were more successful in estimating the energy demand. According to the results of the presented four scenarios, the energy demand in 2025 is 145.26, 139.85, 126.26, and 144.17 million tons of oil equivalent (Mtoe) for the M-ABCL model, and 185.62, 161.94, 118.96, and 159.71 Mtoe for the M-ABCQ model, respectively. Thus, it is predicted that average consumption will increase by 51.65% in the linear model and 70.94% in the quadratic model. [ABSTRACT FROM AUTHOR]

Recent growth of metaheuristic search strategies has brought a huge progress in the domain of computational optimization. The breakthrough started since the well-known Particle Swarm Optimization algorithm had been introduced and examined. Optimization technique presented in this contribution mimics the process of flower pollination. It is build on the foundation of the first technique of this kind—known as Flower Pollination Algorithm (FPA). In this paper, its simplified and improved version, obtained after extensive performance testing, is presented. It is based on only one natural phenomena—called flower constancy—the natural mechanism allowing pollen carrying insects to remember the positions of the best pollen sources. Modified FPA, named as Biotic Flower Pollination Algorithm (BFPA) and relying solely on biotic pollinators, outperforms original FPA, which itself proved to be very effective approach. The paper first presents a short description of original FPA and the changes leading to Biotic Flower Pollination Algorithm. It also discusses performance of the modified algorithm on a full set of CEC17 benchmark functions. Furthermore, in that aspect, the comparison between BFPA and other optimization algorithms is also given. Finally, brief exemplary application of modified algorithm in the field of probabilistic modeling, related to physics and engineering, is also presented. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid optimization algorithm, consisted of weighted vertices-based optimizer (WVO) and particle swarm optimization (PSO) algorithm, is proposed to solve three economic frameworks for scheduling of power sources in order to meet the required power demand in power systems. These frameworks are economic power dispatch, economic emission power dispatch and combined heat and economic power dispatch problems. The basic idea of weighted vertices optimizer (WVO) is given from the bisection root-finding method in mathematics. It uses swarm intelligence and evolutionary strategy to efficiently find the optimum solution. However, the original WVO algorithm has some flaws in complex problems with a high number of variables and constraints. Therefore, this paper presents hybrid WVO–PSO algorithm which solved the mentioned flaws and also improved its speed and accuracy. In this algorithm, varying speed is defined for each vertex by using PSO which helps better exploration through the search space. To evaluate the performance of WVO–PSO, it is applied to some of well-known and complex emission/economic dispatch (EED), combined heat and power economic dispatch (CHPED) and combined heat and power emission/economic dispatch (CHPEED) problems and then the driven results are compared with other recent methods which demonstrates better performance of the proposed method in solving non-convex and constrained EED, CHPED and CHPEED problem in terms of minimizing costs and emissions. [ABSTRACT FROM AUTHOR]

Recently, a multitude of researchers have considered the fully connected topology (Gbest) as a default communication topology in particle swarm optimization (PSO). Despite many earlier studies of this issue indicating that the Gbest might favor unimodal problems, the topology with fewer connections, e.g., Lbest, might perform better on multimodal problems. It seems that different topologies make PSO a problem-related algorithm, while in this paper a problem-free PSO which integrates a reinforcement learning method has been proposed, referred to as QLPSO. In the new proposed algorithm, each particle acts as an agent independently, selecting the optimal topology under the control of Q-learning (QL) during each iteration. Two variants of QLPSO consider the different dimensions of the communication topology, respectively. In order to investigate the performance of QLPSO, experiments on 28 CEC 2013 benchmark functions are carried out when comparing with static and dynamic topologies. The reported computational results show that the proposed QLPSO is more superior compared with several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Juvenile diabetes or a type-1 diabetic can be seen in 5% of the patients, who affected by this form of the disease. The type-1 diabetic can be seen mostly in children and young adults, which continue to spread all over the world. The developments of the artificial pancreas give hope to develop glucose monitoring sensors and insulin pump for those who suffer from severe lack of insulin generation. On the other hand, taking control of blood sugar is a challenging task in which specific factors of the body will limit the ability of closed-loop systems to perform well. This paper presents an investigation of the optimized control strategy to deal with the closed-loop artificial pancreas, which is based on the proportional–integral–derivative (PID). The primary objective of this investigation is to find the best optimized model to maintain the best glucose monitoring and insulin delivery. In order to tune the PID controller to decide on the efficient insulin injection, an investigation was conducted for an optimization algorithm [such as genetic algorithm, gravitational search algorithm, particle swarm optimization, sequential randomized algorithm, brain storm optimization algorithm, class topper optimization, and gray wolf optimization algorithm (GWOA)]. Among these, it is found that the GWOA gives a promising result compare to the other. [ABSTRACT FROM AUTHOR]

In this paper, weighted differential evolution algorithm (WDE) has been proposed for solving real-valued numerical optimization problems. When all parameters of WDE are determined randomly, in practice, WDE has no control parameter but the pattern size. WDE can solve unimodal, multimodal, separable, scalable, and hybrid problems. WDE has a very fast and quite simple structure, in addition, it can be parallelized due to its non-recursive nature. WDE has a strong exploration and exploitation capability. In this paper, WDE's success in solving CEC' 2013 problems was compared to 4 different EAs (i.e., CS, ABC, JADE, and BSA) statistically. One 3D geometric optimization problem (i.e., GPS network adjustment problem) and 4 constrained engineering design problems were used to examine the WDE's ability to solve real-world problems. Results obtained from the performed tests showed that, in general, problem-solving success of WDE is statistically better than the comparison algorithms that have been used in this paper. [ABSTRACT FROM AUTHOR]

The bottom hole pressure (BHP) of managed pressure drilling (MPD) is a typically unstable object with hysteresis that is difficult to be directly controlled. However, at the present stage, BHP control still focuses on conventional PID control and simple intelligent control, requiring repeated data alignment. There are some related problems, such as lack of control over BHP, longer working hours and high cost of drilling. In order to increase economic effects of MPD, this paper analyzes the MPD system and utilizes wellhead back pressure as the controlled variable. According to throttle valve features, basic parameters and boundary conditions of MPD, a mathematical model of throttle valve is also calculated. Besides, this paper focuses on studying the control model and proposes an improved particle swarm algorithm to optimize PID neural network (IPSOPIDNN) model. This model is improved based on inertia weight and fitness function of conventional particle swarm algorithm. Moreover, the particle swarm algorithm is used to optimize the initial weight value of PID neural network, shorten the search time for optimal value of particle swarm, and reduce the chance of local minimum. The real-time control results of IPSO-PIDNN are compared with results of traditional particle swarm optimization PID neural network (PSO-PIDNN) and particle swarm optimization PID neural network (PSO-PIDNN). IPSO-PIDNN control system has some advantages, including favorable self-learning, optimization quality, high levels of control precision, no overshoot, rapid response and short setting time. In this way, advanced automation control of BHP is conducted during managed pressure drilling process, thus providing technical support for the well control safety of managed pressure drilling. [ABSTRACT FROM AUTHOR]