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Owing to scientific development, a variety of challenges present in the field of information retrieval. These challenges are because of the increased usage of large volumes of data. These huge amounts of data are presented from large-scale distributed networks. Centralization of these data to carry out analysis is tricky. There exists a requirement for novel text document clustering algorithms, which overcomes challenges in clustering. The two most important challenges in clustering are clustering accuracy and quality. For this reason, this paper intends to present an ideal clustering model for text document using term frequency–inverse document frequency, which is considered as feature sets. Here, the initial centroid selection is much concentrated which can automatically cluster the text using weighted similarity measure in the proposed clustering process. In fact, the weighted similarity function involves the inter-cluster, and intra-cluster similarity of both ordered and unordered documents, which is used to minimize weighted similarity among the documents. An advanced model for clustering is proposed by the hybrid optimization algorithm, which is the combination of the Jaya Algorithm (JA) and Grey Wolf Algorithm (GWO), and so the proposed algorithm is termed as JA-based GWO. Finally, the performance of the proposed model is verified through a comparative analysis with the state-of-the-art models. The performance analysis exhibits that the proposed model is 96.56% better than genetic algorithm, 99.46% better than particle swarm optimization, 97.09% superior to Dragonfly algorithm, and 96.21% better than JA for the similarity index. Therefore, the proposed model has confirmed its efficiency through valuable analysis. [ABSTRACT FROM AUTHOR]

Solar energy has been highly researched in the last years due to the great possibilities of exploitation of solar radiation. One relevant factor to ensure that radiation is captured is a solar tracking system, which locates any time a caption structure normal to solar angles. Aligning, the results of the design, modeling, and simulation of a Solar Tracking Control of a Parabolic Trough Collector by Traditional PID, Fuzzy Sets, and Particle Swarm Optimization (PSO) algorithm are presented in this paper. Classical controller strategies as PID and Fuzzy have been implemented to control an angular movement of a single-axis tracker applied to a parabolic concentrator. In addition, an optimization algorithm as PSO has been developed to calculate an optimized parameter for proportional, integral, and derivative constants for a PID. Software as SolidWorks® and MATLAB have been used to model and simulate system operation. In order to simulate solar radiation, a model of light incidence in the function of photo-resistance has been generated. As a result, each controller meets the demand and the requirements of a reference continuously variable. The Fuzzy logic controller has achieved a higher error, the controller with the bigger overshoot has been a PID designed by an equalized polynomial, and the controller with the bigger establishment time has been a PID optimized by PSO algorithm. [ABSTRACT FROM AUTHOR]

Oversampling ratio N and the minority class' nearest neighboring number k are key hyperparameters of synthetic minority oversampling technique (SMOTE) to reconstruct the class distribution of dataset. No optimal default value exists there. Therefore, it is of necessity to discuss the influence of the output dataset on the classification performance when SMOTE adopts various hyperparameter combinations. In this paper, we propose a hyperparameter optimization algorithm for imbalanced data. By iterating to find reasonable N and k for SMOTE, so as to build a balanced and high-quality dataset. As a result, a model with outstanding performance and strong generalization ability is trained, thus effectively solving imbalanced classification. The proposed algorithm is based on the hybridization of simulated annealing mechanism (SA) and particle swarm optimization algorithm (PSO). In the optimization, Cohen's Kappa is used to construct the fitness function, and AdaRBFNN, a new classifier, is integrated by multiple trained RBF neural networks based on AdaBoost algorithm. Kappa of each generation is calculated according to the classification results, so as to evaluate the quality of candidate solution. Experiments are conducted on seven groups of KEEL datasets. Results show that the proposed algorithm delivers excellent performance and can significantly improve the classification accuracy of the minority class. [ABSTRACT FROM AUTHOR]

As a result of the widespread adoption of hovercraft systems, the path finding of such systems has become an essential rule for locating the target with the shortest distance and avoiding collision between the starting point and the target locations. The purpose of this paper is to develop a method for path finding by proposing a hybrid method of intelligent optimization techniques, including two stochastic approaches. The first one is called the Artificial Bee Colony (ABC) algorithm, and it aims to direct the hovercraft toward the target utilizing the behaviour of honey bees to reduce the path length required to reach the target. The second approach makes use of the Self Perception Particle Swarm Optimization (SP-PSO) algorithm, which is designed to improve particle swarm optimization in order to obtain a path that is more effective, smoother, and shorter for the hovercraft to travel in a complicated environment. The developed hybrid method is called the Artificial Bee Colony Self Perception Particle Swarm Optimization (ABC-SPPSO), which enhances the convergence speed and achieves a trade-off between exploration and exploitation in order to generate the shortest path while simultaneously avoiding collisions. The simulation results indicate that the performance of the proposed hybrid algorithm surpasses those of the original techniques. Moreover, the results demonstrate that the proposed hybrid algorithm outperforms the hybrid Firefly Algorithm Modified Chaotic Particle Swarm (HFAMCPSO) by (11.90%) in terms of distance and (56%) in terms of iterations. In addition, it outperforms the Quarter Orbit combined with the Particle Swarm Optimization (QOPSO) algorithm by (1.89%) in terms of distance and (16.66%) in terms of iterations. Furthermore, compared to the Rapid Random Tree Star Particle Swarm Optimization (RRT*PSO), the proposed method achieved enhancement of (0.82%) in terms of distance and (33.33%) in terms of iterations in providing the shortest path while avoiding collisions and reducing the trajectory tracking error to approximately zero. All the considered approaches were simulated in a global environment, utilizing the MATLAB 2022 package. [ABSTRACT FROM AUTHOR]

The beetle antenna search algorithm (BAS) converges rapidly and runs in a short time, but it is prone to yielding values corresponding to local extrema when dealing with high-dimensional problems, and its optimization result is unstable. The artificial fish swarm algorithm (AFS) can achieve good convergence in the early stage, but it suffers from slow convergence speed and low optimization accuracy in the later stage. Therefore, this paper combines the two algorithms according to their respective characteristics and proposes a mutation and a multi-step detection strategy to improve the BAS algorithm and raise its optimization accuracy. To verify the performance of the hybrid composed of the AFS and BAS algorithms based on the Mutation and Multi-step detection Strategy (MMSBAS), AFS-MMSBAS is compared with AFS, the Multi-direction Detection Beetle Antenna Search (MDBAS) Algorithm, and the hybrid algorithm composed of the two (AFS-MDBAS). The experimental results show that, with respect to high-dimensional problems: (1) the AFS-MMSBAS algorithm is not only more stable than the MDBAS algorithm, but it is also faster in terms of convergence and operation than the AFS algorithm, and (2) it has a higher optimization capacity than the two algorithms and their hybrid algorithm. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) networks are becoming increasingly popular for monitoring critical environments of various types. For the communication of IoT devices, several lightweight protocols have been developed. MQTT (message queuing telemetry transport) is a widely used messaging protocol in IoT using the publish–subscribe technique. The openness of publish–subscribe model and the limited built-in authentication capabilities makes it vulnerable to intruders. Hence, an intrusion detection system (IDS) has a vital role in MQTT-IoT security. This paper proposes an efficient intrusion detection mechanism for the MQTT-IoT networks using an enhanced chaotic salp swarm optimization algorithm (ECSSA) and LightGBM classifier. Traditional IDS uses a lot of irrelevant data and undesirable attributes, resulting in long detection times and low performance. To overcome the limitations, the proposed IDS uses ECSSA for feature selection and LightGBM classifier for better detection accuracy. The experimental verification on MC-IoT, MQTT-IoT-IDS2020, and MQTTset datasets demonstrates that ECSSA and LightGBM improve the overall accuracy rate. The proposed technique outperforms the existing approaches with an accuracy of 99.38%, 98.91%, and 98.35% in the three test sets, MC-IoT, MQTT-IoT-IDS2020, and MQTTset, respectively. [ABSTRACT FROM AUTHOR]

To solve the problems of low convergence accuracy, slow convergence speed and easy to fall into local optimum of traditional particle swarm algorithm, this paper proposed an improved particle swarm algorithm with multistrategy fusion. Firstly, in order to accelerate the convergence speed of free particles, the improved algorithm used a method of updating the position of free particles based on the midperpendicular algorithm. secondly, the improved algorithm designed a strategy of generating exploding particles near the optimal particles to enhance the optimizationseeking accuracy and optimization-seeking speed of the algorithm, and the improved algorithm also designed a particle velocity updating strategy relying only on the global optimal particle position to accommodate the first two strategies. Finally, The algorithm also used the inertia weights and particle position update methods of the simplified particle swarm optimization algorithm based on probabilistic hierarchy. This paper designed a few comparison experiments with other five improved particle swarm algorithms, and the results show that the improved algorithm in this paper has a greater advantage and better performance whether dealing with low-dimensional problems or high-dimensional problems. [ABSTRACT FROM AUTHOR]

In this paper, a stochastic-metaheuristic model is performed for multi-objective allocation of photovoltaic (PV) resources in 33-bus and 69-bus distribution systems to minimize power losses of the distribution system lines, improving the voltage profile and voltage stability of the distribution system buses, considering the uncertainty of PV units' power and network demand. The decision-making variables, including installation location and the size of PVs, are determined optimally via an improved human learning optimization algorithm (IHLOA). The conventional human learning optimization algorithm (IHLOA) is improved based on Gaussian mutation to enhance the exploration capability and avoid getting trapped in local optimal. The methodology is implemented in two cases as deterministic and stochastic without and with uncertainties, respectively. Monte Carol Simulation (MCS) based on probability distribution function (PDF) is used for uncertainties modeling. The deterministic results proved the superiority of the IHLOA compared with conventional HLOA, particle swarm optimization (PSO), to obtain better values of the different objectives and faster convergence speed and accuracy. The results are clear that enhancing the conventional HLOA has increased the algorithm's ability to explore and achieve the optimal global solution with higher convergence accuracy. Moreover, the stochastic results were clear that considering the uncertainties leads to correct and robust decision-making against existing uncertainties and accurate knowledge of the network operator against the exact values of various objectives compared to the deterministic case. [ABSTRACT FROM AUTHOR]

Much attention has been paid to the methodology for measuring the performance of EDM (Electric Discharge Machining). In this paper, an optimization methodology, based on a combination of multivariate linear regression and the Particle swarm optimization method, is constructed to obtain optimum process parameters for EDM machining. The machining tests presented in this study are performed on an ONA NX4 die-sinking machine under different machining parameters such as discharge current (I), voltage (V) and pulse time (Ton ). The optimization strategy developed aims to maximize the material removal rate MRR, extend the lifetime of the TWR electrode and minimize the surface roughness Ra of AISI 1095 steel. Mathematical optimization models are developed by linear regression using multiple explanatory variables to predict the outcome of MRR, TWR and Ra responses. The models developed have a useful and significant precession compared to the experimental values: 97% for MRR, 94% for TWR and 82% for Ra. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) as a successful optimization algorithm is widely used in many practical applications due to its advantages in fast convergence speed and convenient implementation. As a population optimization algorithm, the quality of initial population plays an important role in the performance of PSO. However, random initialization is used in population initialization for PSO. Using the solution of the solved problem as prior knowledge will help to improve the quality of the initial population solution. In this paper, we use homotopy analysis method (HAM) to build a bridge between the solved problems and the problems to be solved. Therefore, an improved PSO framework based on HAM, called HAM-PSO, is proposed. The framework of HAM-PSO includes four main processes. It contains obtaining the prior knowledge, constructing homotopy function, generating initial solution and solving the to be solved by PSO. In fact, the framework does not change the PSO, but replaces the random population initialization. The basic PSO algorithm and three others typical PSO algorithms are used to verify the feasibility and effectiveness of this framework. The experimental results show that the four PSO using this framework are better than those without this framework. [ABSTRACT FROM AUTHOR]

This paper is a contribution to the research which aims to provide an efficient optimization algorithm for job-shop scheduling problems with multi-purpose machines or MPMJSP. To meet its objective, this paper proposes a new variant of particle swarm optimization algorithm, called GLN-PSOc, which is an extension of the standard particle swarm optimization algorithm that uses multiple social learning topologies in its evolutionary process. GLN-PSOc is a metaheuristic that can be applied to many types of optimization problems, where MPMJSP is one of these types. To apply GLN-PSOc in MPMJSP, a procedure to map the position of particle into the solution of MPMJSP is proposed. Throughout this paper, GLN-PSOc combined with this procedure is named MPMJSP-PSO. The performance of MPMJSP-PSO is evaluated on well-known benchmark instances, and the numerical results show that MPMJSP-PSO performs well in terms of solution quality and that new best known solutions were found in some instances of the test problems. [ABSTRACT FROM AUTHOR]

Chimp Optimization Algorithm (ChOA), a novel meta-heuristic algorithm, has been proposed in recent years. It divides the population into four different levels for the purpose of hunting. However, there are still some defects that lead to the algorithm falling into the local optimum. To overcome these defects, an Enhanced Chimp Optimization Algorithm (EChOA) is developed in this paper. Highly Disruptive Polynomial Mutation (HDPM) is introduced to further explore the population space and increase the population diversity. Then, the Spearman's rank correlation coefficient between the chimps with the highest fitness and the lowest fitness is calculated. In order to avoid the local optimization, the chimps with low fitness values are introduced with Beetle Antenna Search Algorithm (BAS) to obtain visual ability. Through the introduction of the above three strategies, the ability of population exploration and exploitation is enhanced. On this basis, this paper proposes an EChOA-SVM model, which can optimize parameters while selecting the features. Thus, the maximum classification accuracy can be achieved with as few features as possible. To verify the effectiveness of the proposed method, the proposed method is compared with seven common methods, including the original algorithm. Seventeen benchmark datasets from the UCI machine learning library are used to evaluate the accuracy, number of features, and fitness of these methods. Experimental results show that the classification accuracy of the proposed method is better than the other methods on most data sets, and the number of features required by the proposed method is also less than the other algorithms. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of standard particle swarm optimization (PSO) algorithms that easily fall into local optimum, this paper proposes an optimization algorithm (LTQPSO) that improves quantum behavioral particle swarms. Aiming at the problem of premature convergence of the particle swarm algorithm, the evolution speed of individual particles and the population dispersion are used to dynamically adjust the inertia weights to make them adaptive and controllable, thereby avoiding premature convergence. At the same time, the natural selection method is introduced into the traditional position update formula to maintain the diversity of the population, strengthen the global search ability of the LTQPSO algorithm, and accelerate the convergence speed of the algorithm. The improved LTQPSO algorithm is applied to landscape trail path planning, and the research results prove the effectiveness and feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

To overcome the shortcomings of the basic glowworm swarm optimization (GSO) algorithm, such as low accuracy, slow convergence speed and easy to fall into local minima, chaos algorithm and cloud model algorithm are introduced to optimize the evolution mechanism of GSO, and a chaos GSO algorithm based on cloud model (CMCGSO) is proposed in the paper. The simulation results of benchmark function of global optimization show that the CMCGSO algorithm performs better than the cuckoo search (CS), invasive weed optimization (IWO), hybrid particle swarm optimization (HPSO), and chaos glowworm swarm optimization (CGSO) algorithm, and CMCGSO has the advantages of high accuracy, fast convergence speed and strong robustness to find the global optimum. Finally, the CMCGSO algorithm is used to solve the problem of face recognition, and the results are better than the methods from literatures. [ABSTRACT FROM AUTHOR]

Multiple sequence alignment (MSA) is a core problem in many applications. Various optimization algorithms such as genetic algorithm and particle swarm optimization (PSO) have been used to solve this problem, where all of them are adapted to work in the bioinformatics domain. This paper defines the MSA problem, suggests a novel MSA algorithm called 'NestMSA' and evaluates it in two domains: Web data extraction and removing different URLs with similar text (DUST). The suggested algorithm is inspired by the PSO optimization algorithm. It is not a generalization of a two-sequence alignment algorithm as it processes all the sequences at the same time. Therefore, it looks globally at the same time on all sequences. Different from other PSO-based alignment algorithms, swarm particles in the proposed NestMSA algorithm are nested inside the sequences and communicated together to align them. Therefore, global maximum is guaranteed in our algorithm. Furthermore, this work suggests a new objective function which both maximizes the number of matched characters and minimizes the number of gaps inserted in the sequences. The running time complexity and the efficiency of NestMSA are addressed in this paper. The experiments show an encouraging result as it outperforms the two approaches DCA and TEX in the Web data extraction domain (95% and 96% of recall and precision, respectively). Furthermore, it gives a high-performance result in the DUST domain (95%, 93% and 92% of recall, precision and SPS score, respectively). [ABSTRACT FROM AUTHOR]

Forming a team of experts that can match the requirements of a collaborative task is an important aspect, especially in project development. In this paper, we propose an improved Jaya optimization algorithm for minimizing the communication cost among team experts to solve team formation problem. The proposed algorithm is called an improved Jaya algorithm with a modified swap operator (IJMSO). We invoke a single-point crossover in the Jaya algorithm to accelerate the search, and we apply a new swap operator within Jaya algorithm to verify the consistency of the capabilities and the required skills to carry out the task. We investigate the IJMSO algorithm by implementing it on two real-life datasets (i.e., digital bibliographic library project and StackExchange) to evaluate the accuracy and efficiency of proposed algorithm against other meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, African buffalo optimization algorithm and standard Jaya algorithm. Experimental results suggest that the proposed algorithm achieves significant improvement in finding effective teams with minimum communication costs among team members for achieving the goal. [ABSTRACT FROM AUTHOR]

A large amount of calculation exists in a complex engineering optimization problem. The swarm intelligence algorithm can improve calculation efficiency and accuracy of complex engineering optimization. In the existing research, the surrogate model and the swarm intelligence algorithm are only two independent tools to solve the optimization problem. In this paper, we propose the surrogate-assisted crow swarm intelligent search optimization algorithm (SACSA) by combining the characteristics of swarm intelligence algorithm and surrogate model. The proposed algorithm utilizes the initial samples to construct the surrogate model, and then the improved crow search algorithm (CSA) is applied to obtain optimal solution. Finally, the proposed algorithm is compared with EGO, MSSR, ARSM-ISES, AMGO and SEUMRE, MPS, HAM algorithms. The comparison results show that the proposed algorithm can find a global optimal solution with fewer samples and is beneficial to improving the efficiency and accuracy of calculation. [ABSTRACT FROM AUTHOR]

In smart city wireless network infrastructure, network node deployment directly affects network service quality. This problem can be attributed to deploying a suitable ordinary AP node as a wireless terminal access node on a given geometric plane, and deploying a special node as a gateway to aggregate. Traffic from ordinary nodes is to the wired network. In this paper, Pareto multi-objective optimization strategy is introduced into the wireless sensor network node security deployment, and an improved multi-objective particle swarm coverage algorithm based on secure connection is designed. Firstly, based on the mathematical model of Pareto multi-objective optimization, the multi-target node security deployment model is established, and the security connectivity and node network coverage are taken as the objective functions, and the problems of wireless sensor network security and network coverage quality are considered. The multi-objective particle swarm optimization algorithm is improved by adaptively adjusting the inertia weight and particle velocity update. At the same time, the elite archive strategy is used to dynamically maintain the optimal solution set. The high-frequency simulation software Matlab and simulation platform data interaction are used to realize the automatic modeling, simulation analysis, parameter prediction and iterative optimization of wireless network node deployment in smart city based on adaptive particle swarm optimization. Under the premise of meeting the performance requirements of wireless network nodes in smart cities, the experimental results show that although the proposed algorithm could not achieve the accuracy of using only particle swarm optimization algorithm to optimize the parameters of wireless network nodes in smart cities, the algorithm is completed. The antenna parameter optimization process takes less time and the optimization efficiency is higher. [ABSTRACT FROM AUTHOR]

The operation of the photovoltaic (PV) system under partial shading conditions (PSC) is complicated since the output characteristic of the PV system is profoundly affected by the heterogeneous irradiance of PSC. This paper proposes a dynamic reconfiguration framework to tackle PSC in the PV array. Continuous operation of the dynamic PV array reconfiguration under cloud-induced partial shading is considered by developing an emulator of the moving cloud. In addition, the Particle Swarm Optimization and Rao algorithms are improved to obtain the optimal PV array configuration under PSC. The operation of switching is enhanced by simultaneously considering the total switching times and the operation of highly active switches. The simulation results on the 9 × 9 PV array demonstrate the effectiveness of the proposed framework in terms of reducing the number of local maximum power points on the power-voltage characteristic, enhancing power output, and relieving stress on the switching operation of the PV array under different PSC. [ABSTRACT FROM AUTHOR]

The mayfly algorithm (MA), as a newly proposed intelligent optimization algorithm, is found that easy to fall into the local optimum and slow convergence speed. To address this, an improved mayfly algorithm based on dynamic elite strategy (DESMA) is proposed in this paper. Specifically, it first determines the specific space near the best mayfly in the current population, and dynamically sets the search radius. Then generating a certain number of elite mayflies within this range. Finally, the best one among the newly generated elite mayflies is selected to replace the best mayfly in the current population when the fitness value of elite mayfly is better than that of the best mayfly. Experimental results on 28 standard benchmark test functions from CEC2013 show that our proposed algorithm outperforms its peers in terms of accuracy speed and stability. [ABSTRACT FROM AUTHOR]

Vast amounts of data are transferred through communication networks resulting in node congestion, which varies according to peak usage times. The Glowworm Swarm Optimization (GSO) algorithm is inspired by the rummaging and courtship behavior of glowworms. The glow intensity of glowworms is a measure of fitness that attracts other glowworms in its neighborhood. This work applies the GSO algorithm to the computer network congestion problem in order to lessen the network burden by shifting loads to the fittest neighborhood nodes, thereby enhancing network performance during peak traffic times, when the response of systems on the network would go down. The proposed solution aims to alleviate the burdened nodes, thereby improving the flow of traffic throughout the network, improving the users' experience and productivity, and efficiency. In this paper, three swarm algorithms, namely Particle Swarm Optimization (PSO), Cuckoo Search (CK), and GSO have been employed to solve the network load balancing problem. The results produced by GSO show improvement of 71.17%, 74.14%, and 84.15% in networks consisting of 50, 100, and 200 nodes in peak hour load, while PSO shows 13.87%, 11.75%, and 23.72%, and CK 10.61%, 3.19%, and 6%. The results prove the superior performance of GSO. [ABSTRACT FROM AUTHOR]

Parameter extraction of photovoltaic (PV) models based on measured current–voltage data plays an important role in the control, simulation, and optimization of PV systems. Despite the fact that various parameter extraction strategies have been dedicated to solving this problem, they may have certain drawbacks. In this paper, an effective hybrid optimization method based on adaptive rat swarm optimization (ARSO) and pattern search (PS) is presented for effectively and consistently extracting PV parameters. The proposed method employs the global search ability of ARSO and the local search ability of PS. The performance of the new algorithm is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. The extraction of parameters from several PV models, such as single‐diode, double‐diode, and PV modules, confirms the performance of the suggested method. Simulation results show that the proposed method surpasses other state‐of‐the‐art procedures in terms of accuracy, reliability, and convergence speed. [ABSTRACT FROM AUTHOR]

Four-bar linkages are one of the most widely used mechanisms in industries. This paper presents a comparative study on the accuracy and efficiency of the optimum synthesis of path-generating four-bar linkages using five metaheuristic optimization algorithms. The utilized metaheuristic methods included two swarm intelligence-based algorithms, i.e., particle swarm optimization and hybrid particle swarm optimization, and three evolutionary-based algorithms, i.e., differential evolution, ensemble of parameters and mutation strategies in differential evolution, and linearly ensemble of parameters and mutation strategies in differential evolution. The objective function to be minimized is the sum of squares of the distance between the generated points and the precision points of a coupler point. The optimal design of four-bar linkages must meet the Grashof's criteria and exhibit sequential constraints that can prevent the occurrence of order defect. This study investigated five representative cases of the dimensional synthesis of four-bar path generators with and without prescribed timing and compared the optimal solutions of the utilized five metaheuristic methods to those of previously reported algorithms in literature. The improved metaheuristic methods exhibited superior optimal solution and enhanced reliability compared to the original methods. Moreover, three improved metaheuristic methods were not only easy implemented, but also more efficient for solving the optimal synthesis problems, particularly for high dimensional problems. [ABSTRACT FROM AUTHOR]

Non-Line-of-Sight (NLOS) conditions are created by blocking the direct path between the transmitter and receiver, resulting in an increased signal propagation path. To mitigate the Time of Arrival (TOA) measured errors caused by the NLOS phenomenon in cellular radio positioning, we use the Maximum Likelihood (ML) estimation method in this work. The cost function of the ML estimator is usually a high-dimensional, nonlinear, and multimodal function, where standard deterministic optimization techniques cannot solve such problems in real-time and without significant computing resources. In this paper, effective metaheuristic algorithms based on the enhanced variants of Particle Swarm Optimization (PSO) are applied for the optimal solution of the ML problem and efficiently determine the mobile station location. Time-Varying Acceleration Coefficients (TVAC) are introduced into the standard PSO algorithm to enhance the global search and convergence properties. The resulting algorithm is known as PSO-TVAC. To further improve the performance of the metaheuristic optimization, we suggest adding Chaos Search (CS), Opposition-Based Learning (OBL), and TVAC strategy to the PSO process. The simulation results show that the proposed metaheuristic algorithm named the Chaotic Opposition-based PSO-TVAC (COPSO-TVAC) can reach the Generalized Cramer–Rao Lower Bound (GCRLB) and surpass the original PSO, PSO-TVAC, and the presented conventional optimization algorithms. [ABSTRACT FROM AUTHOR]

• A Mixed Particle Swarm Optimization model is developed for optimal network reconfiguration and DG integration. • Optimal network reconfiguration allow a better penetration of renewable energy in the system. • Optimal integration of DG units dramatically increases the voltage level and the optimal exploitation of the DS. • DG integration along with optimal NR greatly contribute to power losses minimization and voltage level enhancement. This paper introduces a Mixed Particle Swarm Optimization (MPSO) approach for active power loss minimization and voltage profile improvement in the distribution network. The developed technique joins the Binary Particle Swarm Optimization (BPSO) and the conventional PSO algorithms. The first one is devoted to identify the optimal distribution network configuration, while the second one is used to solve Distributed Generation (DG) placement and sizing problems. To evaluate the performance of the developed approach, three different load scenarios were assessed during network reconfiguration (NR) and DG integration. Simulations are conducted on two distribution test systems, namely, the IEEE-33-bus and the IEEE-69-bus. The obtained results clearly demonstrate the performance and the effectiveness of the proposed method to find optimal status of switches, as well as DG locations and sizes. A benchmark comparison is presented to prove the efficiency of the proposed MPSO with regard to other optimization techniques. The results show that MPSO outperforms these techniques in terms of quality of solution, power loss reduction and voltage profile enhancement. This study is an extension of the earlier published conference paper. [ABSTRACT FROM AUTHOR]

This paper presents characteristics of model-based optimization methods utilized within the Generalized Self-Adapting Particle Swarm Optimization (GA– PSO) – a hybrid global optimization framework proposed by the authors. GAPSO has been designed as a generalization of a Particle Swarm Optimization (PSO) algorithm on the foundations of a large degree of independence of individual particles. GAPSO serves as a platform for studying optimization algorithms in the context of the following research hypothesis: (1) it is possible to improve the performance of an optimization algorithm through utilization of more function samples than standard PSO sample-based memory, (2) combining specialized sampling methods (i.e. PSO, Differential Evolution, model-based optimization) will result in a better algorithm performance than using each of them separately. The inclusion of model-based enhancements resulted in the necessity of extending the GAPSO framework by means of an external samples memory - this enhanced model is referred to as M-GAPSO in the paper. We investigate the features of two model-based optimizers: one utilizing a quadratic function and the other one utilizing a polynomial function. We analyze the conditions under which those model-based approaches provide an effective sampling strategy. Proposed model-based optimizers are evaluated on the functions from the COCO BBOB benchmark set. [ABSTRACT FROM AUTHOR]

The objective of this research paper is to categorize the magnetic resonance imaging (MRI) images as demented (DEM) or nondemented (ND) using improved chicken swarm optimization technique (ICSO). In literature, CSO technique is widely used to solve numerical optimization and feature selection problem. Using this optimization technique for medical image classification problem will be a pioneering idea. If this technique is directly used to classify the medical images, it provides poor results. Hence, appropriate enhancements are made on the original algorithm using a novel controlled randomness optimization algorithm and control parameter tuning. Cross‐over and Rooster selection methods are also implemented in cascaded manner for further performance improvization. All the experiments are made for two cases: with and without statistical features. The brain MRI images of 65 ND and 52 DEM subjects obtained from the Open Access Series of Imaging Studies website are used in this analysis. The ICSO without statistical features provides the highest accuracy of 86.32%, whereas the original chicken swarm optimization technique provides the accuracy of 52.13% and 52.99% with and without statistical features, respectively. [ABSTRACT FROM AUTHOR]

Clustering algorithms have been applied to different problems in many different real-word applications. Nevertheless, each algorithm has its own advantages and drawbacks, which can result in different solutions for the same problem. Therefore, the combination of different clustering algorithms (cluster ensembles) has emerged as an attempt to overcome the limitations of each clustering technique. The use of cluster ensembles aims to combine multiple partitions generated by different clustering algorithms into a single clustering solution (consensus partition). Recently, several approaches have been proposed in the literature in order to optimize or to improve continuously the solutions found by the cluster ensembles. As a contribution to this important subject, this paper presents an investigation of five bio-inspired techniques in the optimization of cluster ensembles (Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Coral Reefs Optimization and Bee Colony Optimization). In this investigation, unlike most of the existing work, an evaluation methodology for assessing three important aspects of cluster ensembles will be presented, assessing robustness, novelty and stability of the consensus partition delivered by different optimization algorithms. In order to evaluate the feasibility of the analyzed techniques, an empirical analysis will be conducted using 20 different problems and applying two different indexes in order to examine its efficiency and feasibility. Our findings indicated that the best population-based optimization method was PSO, followed by CRO, AG, BCO and ACO, for providing robust and stable consensus partitions. [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]

Summary: The accuracy of the finite element model is essential for fatigue analysis and vibration of a flexible body. This paper presents a finite element model updating (FEMU) algorithm based on crows search algorithm incorporated with Levy flight (LFCSA). The proposed algorithm is tested on the updating of two different cases: a simple structure (beam) and a complex structure (gearbox housing). To verify the performance of LFCSA, two optimization algorithm, which is the standard CSA and the particle swarm optimization algorithm with Levy flight (LFPSO), are applied to FEMU of the simple beam. The result of the comparison shows that the LFCSA has sufficient convergence speed and global search ability in the implementation of FEMU. In the case of gearbox housing, the error of frequencies and mode shapes are analyzed to indicate that the LFCSA gives a satisfactory result in a complex structure. [ABSTRACT FROM AUTHOR]

The application mapping problem is an NP-hard combinatorial optimization problem in network-on-chip (NoC) design. Applications of size ($n$ $ >$ 30) cannot be solved optimally by an exact algorithm in reasonable time, and the evolutionary algorithms have drawn the attention of NoC researchers. In this paper, we propose a new effective optimization method based on the discrete particle swarm optimization framework, which includes the novel principles for representation, velocity computing, and position-updating of the particles. In our proposed method, particles are allowed to swing between elite and regular pools, and a simple local search procedure is applied on elite particles to exploit the promising solutions. Extensive computational studies using standard benchmark instances and task graphs for free (TGFF) random instances reveal that the proposed optimization algorithm is able to attain the best results, and thus competes very favorably with the previously proposed heuristic approaches. A stability analysis and the two-sided Wilcoxon rank sum tests are also presented to shed light on the robust behavior of the algorithm. [ABSTRACT FROM AUTHOR]

In electronic beam scanning, the number of phase shifters is an obvious challenge. So, there are several methods to reduce the number of phase shifters. The aim of this paper is to investigate the use of the meta-heuristic algorithm to lower the grating lobe level in the subarray antenna. Improve the result obtained by group subarray optimization techniques to determine topology and space between elements, and complex optimization of weight, simultaneously. Uniform subarray and random subarray are analyzed in Matlab to determine the coefficient of excitation by the evolutionary algorithm, as well as swarm and hybrid. The results of the simulation are shown; this method leads to radiation pattern without grating lobe in wide scanning angle. It indicates that there is a possibility of obtaining wide electronic scanning with minimum number of phase shifters and improving result. [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]

In this paper, six metaheuristic algorithms, in the form of artificial bee colony optimization, ant colony optimization, particle swarm optimization, differential evolution, firefly algorithm and teaching-learning-based optimization techniques are applied for parametric optimization of a multi-pass face milling process. Using those algorithms, the optimal values of cutting speed, feed rate and depth of cut for both roughing and finishing operations are determined for having minimum total production time and total production cost. It is observed that the teaching-learning-based optimization algorithm outperforms the others with respect to accuracy and consistency of the derived solutions as well as computational speed. Two statistical tests, i.e. paired t-test and Wilcoxson signed rank test also confirm its superiority over the remaining algorithms. Finally, these metaheuristics are employed for multi-objective optimization of the considered multi-pass milling process while concurrently minimizing both the objectives. [ABSTRACT FROM AUTHOR]

The arithmetic optimization algorithm is a recently proposed metaheuristic algorithm. In this paper, an improved arithmetic optimization algorithm (IAOA) based on the population control strategy is introduced to solve numerical optimization problems. By classifying the population and adaptively controlling the number of individuals in the subpopulation, the information of each individual can be used effectively, which speeds up the algorithm to find the optimal value, avoids falling into local optimum, and improves the accuracy of the solution. The performance of the proposed IAOA algorithm is evaluated on six systems of nonlinear equations, ten integrations, and engineering problems. The results show that the proposed algorithm outperforms other algorithms in terms of convergence speed, convergence accuracy, stability, and robustness. [ABSTRACT FROM AUTHOR]

The PID control algorithm for balancing robot attitude control suffers from the problem of difficult parameter tuning. Previous studies have proposed using metaheuristic algorithms to tune the PID parameters. However, traditional metaheuristic algorithms are subject to the criticism of premature convergence and the possibility of falling into local optimum solutions. Therefore, the present paper proposes a CFHBA-PID algorithm for balancing robot Dual-loop PID attitude control based on Honey Badger Algorithm (HBA) and CF-ITAE. On the one hand, HBA maintains a sufficiently large population diversity throughout the search process and employs a dynamic search strategy for balanced exploration and exploitation, effectively avoiding the problems of classical intelligent optimization algorithms and serving as a global search. On the other hand, a novel complementary factor (CF) is proposed to complement integrated time absolute error (ITAE) with the overshoot amount, resulting in a new rectification indicator CF-ITAE, which balances the overshoot amount and the response time during parameter tuning. Using balancing robot as the experimental object, HBA-PID is compared with AOA-PID, WOA-PID, and PSO-PID, and the results demonstrate that HBA-PID outperforms the other three algorithms in terms of overshoot amount, stabilization time, ITAE, and convergence speed, proving that the algorithm combining HBA with PID is better than the existing mainstream algorithms. The comparative experiments using CF prove that CFHBA-PID is able to effectively control the overshoot amount in attitude control. In conclusion, the CFHBA-PID algorithm has great control and significant results when applied to the balancing robot. [ABSTRACT FROM AUTHOR]

The Bacterial Foraging Algorithm (BFOA) is a well-known swarm collective intelligence algorithm used to solve a variety of constraint optimization problems with wide success. Despite its universality, implementing the BFOA may be complex due to the calibration of multiple parameters. Moreover, the Two-Swim Modified Bacterial Foraging Optimization Algorithm (TS-MBFOA) is a state-of-the-art modification of the BFOA which may lead to solutions close to the optimal but with more parameters than the original BFOA. That is why in this paper we present the design using the Unified Modeling Language (UML) and the implementation in the MATLAB platform of a front-end for the TS-MBFOA algorithm to calibrate the algorithm parameters faster and with no need for editing lines of code. To test our proposal, we solve a numerical optimization problem with constraints known as tension/compression spring, where 30 independent executions were conducted using the TS-MBFOA and then compared with an earlier version called MBFOA. The runtime configuration and the parameter tuning were fluent using our front-end, and the TS-MBFOA obtained the better results. To date, there is no other user-friendly implementation of this specific algorithm in an open-source code, and the front-end is flexible enough to include other numerical optimization problems with minimal effort. [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]

Aiming at the problem that Harris hawks optimization algorithm converges slowly and is prone to local optimization, this paper proposes an improved Harris hawks optimization algorithm (IHHO) which combines mutually beneficial symbiosis and lens imaging learning. Firstly, the algorithm uses Tent chaotic map to initialize the population to increase the diversity of the population and improve the optimization performance of the algorithm. Secondly, in the exploration stage, the algorithm integrates the idea of mutually beneficial symbiosis and nonlinear inertia factor to enhance the exchange of population information and accelerate the convergence speed. Then, the algorithm uses lens imaging reverse learning strategy to perturb and mutate the Harris hawks position with a certain probability to improve the ability of the algorithm to jump out of the local optimum. Finally, the simulation results of 16 benchmark test functions show that IHHO has faster convergence speed, higher precision and stronger robustness compared with the other five algorithms. At the same time, IHHO is applied to the problem of image segmentation, and the simulation results verify the feasibility of the algorithm in practical engineering applications. [ABSTRACT FROM AUTHOR]

The traditional least squares (LS) and gradient descent (GD) algorithms can estimate the parameters of the regression models. They can be inefficient when the models have complex structures: (1) the unknown parameters in the information vector make the algorithm be impossible to update the parameters; (2) the zigzagging nature of the gradient descent algorithm and the complex structures lead to slow convergence rates; and (3) the step-size and derivative function calculations may be unsolvable for complex nonlinear models. This paper proposes two kinds of algorithms for exponential nonlinear models. The first is the two-stage algorithm, which decomposes the complex model into a linear part and a nonlinear part, where the linear part is estimated using the LS algorithm and the nonlinear part is identified based on the GD algorithm. The second is the particle swarm optimization algorithm which can simultaneously obtain all the parameters. To increase the convergence rates, the Aitken method is also introduced. The simulation results demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

An optimal and smooth trajectory for industrial robots has a positive impact on reducing the execution time in an operation and the vibration in their joints. In this paper, a methodology for the time-optimal and jerk-continuous trajectory planning of industrial robots is proposed. The entire trajectory is interpolated in the joint space utilizing fifth-order B-splines and then optimized by a hybrid whale optimization algorithm and genetic algorithm (WOA-GA). Two objective functions, including the integral of the squared jerk along the entire trajectory and the total execution time, are minimized to obtain the optimal entire trajectory. A fifth-order B-spline interpolation technique enables the achievement of a jerk-continuous trajectory, while respecting the kinematic limits of jerk, acceleration and velocity. WOA-GA is utilized to solve the time-jerk optimal trajectory planning problem with nonlinear constraints. The proposed hybrid optimization algorithm yielded good results and achieved the time-jerk optimal trajectory better under kinematic constraints compared to the genetic algorithm, whale optimization algorithm, improved whale optimization algorithm with particle swarm optimization and adaptive cuckoo search algorithm. The numerical results show the competent performances of the proposed methodology to generate trajectories with high smooth curves and short total execution time. [ABSTRACT FROM AUTHOR]

The soil water retention curve (SWRC) has a significant role in determining the unsaturated properties of soil. A stochastic optimisation algorithm named Rao-1 algorithm is employed to estimate the parameters of the SWRC model in this paper. The Rao-1 algorithm is a simple heuristic search algorithm containing only addition and multiplication operations. This paper introduces the method and its application in determining soil water retention this model parameters in detail. In this study, the van Genuchten model is used to depict the SWRC for its good fitting capacity, and the van Genuchten model parameters are determined using Rao-1 algorithm. The feasibility and efficiency of the proposed method are validated via the experimental results of 24 soil samples of 12 soil textural classes. Besides, the performance of Rao-1 algorithm is compared with that of salp swarm algorithm, the particle swarm optimization algorithm, differential evolution algorithm, and RETC program. Through comparative analysis, Rao-1 algorithm outperforms other methods in determining SWRC parameters. [ABSTRACT FROM AUTHOR]

In recent years, emotional neural networks (ENNs) have been extensively used in the field of time series prediction. As a variant of ENN, the radial basis emotional neural network (RBENN) is chosen as the prediction model of time series in this paper, because it has a special type of structure that can preprocess the interference in the data. However, it is difficult for many existing methods to determine network structure automatically while adjusting network parameters. To solve this problem, an RBENN based on adaptive inertia weight comprehensive learning particle swarm optimization algorithm (ADw-CLPSO-RBENN) is designed. Firstly, an adaptive inertia weight adjustment strategy based on the CLPSO algorithm (ADw-CLPSO) is exploited to balance the global and local search ability of particles. Secondly, a particle-variable dimensional learning mechanism (PVDLM) is developed based on the ADw-CLPSO algorithm, which enables particles to find the appropriate network structure while searching for the optimal parameter solution. Finally, the proposed method is evaluated in two time series and a real wastewater treatment system. The simulation results demonstrate that the proposed ADw-CLPSO-RBENN can automatically adjust to a suitable network structure, and the prediction accuracy is also better than other methods. Therefore, the proposed method has higher superiority in time series prediction. [ABSTRACT FROM AUTHOR]

This paper presents a generic strategy for short-term load forecasting (STLF) based on the support vector regression machines (SVR). Two important improvements to the SVR based load forecasting method are introduced, i.e., procedure for generation of model inputs and subsequent model input selection using feature selection algorithms. One of the objectives of the proposed strategy is to reduce the operator interaction in the model-building procedure. The proposed use of feature selection algorithms for automatic model input selection and the use of the particle swarm global optimization based technique for the optimization of SVR hyper-parameters reduces the operator interaction. To confirm the effectiveness of the proposed modeling strategy, the model has been trained and tested on two publicly available and well-known load forecasting data sets and compared to the state-of-the-art STLF algorithms yielding improved accuracy. [ABSTRACT FROM PUBLISHER]

The paper presents a study on the efficiency of Ant Colony Communities (ACC) used to solve the Travelling Salesman Problem. The ACC is an approach to parallelize the Ant Colony Optimization algorithm (ACO). An ACC is made up of a Community Server that coordinates the work of a set ant colony clients. Each client implements a classical ACO algorithm. The individual colonies process cargos of data obtained from the server and send them back the as partial results. The paper presents a general description of the ACC concept and describes in details two ways of implementing it. The first one uses an inhomogeneous environment of traditional computers working in an asynchronous mode. The second one uses the homogenous Hadoop environment and the processing is done in a synchronized mode. The performance of the Communities is estimated by low level measures: their power and scalability. The high level measure deals with the length of obtained routes. The paper presents also the taxonomy of parallel implementations of the Ant Colony Optimization. [ABSTRACT FROM AUTHOR]

Most of the traditional tourism route planning algorithms only consider single-factor planning, that is, the influence of scenic spots on route planning. Particle swarm optimization algorithm is favored by many people because of its simple concept, easy implementation, and good robustness. Aiming at this problem, this paper takes the actual geographic data as the research object of the tourism route problem and describes the model of the discrete particle swarm algorithm based on geographic coordinates to solve the tourism route problem, which is used to solve practical problems. In order to further improve the global search ability of the algorithm, a self-balancing mechanism is proposed, which makes the algorithm process simple and the algorithm performance improved. At the same time, multithread parallelism is adopted to improve the solution speed of the algorithm, which makes up for the deficiency of the parallelization research of the algorithm. [ABSTRACT FROM AUTHOR]

Rationale: Ion mobility spectrometry (IMS) is a powerful analytical tool extensively applied in numerous domains. However, there still exists the phenomenon of peaks overlapping in the analysis of isomers with similar structures due to the limited resolution of IMS. In this paper, a dynamic inertia weight particle swarm optimization (DIWPSO) algorithm combined with second-order differentiation is proposed to separate the IMS overlapping peaks efficiently and precisely. Methods: It can identify the component number of overlapping peaks and limit those parameters (ion mobility, intensity, and full-width at half maximum of each single peak) of the peak model in a small range using second-order differentiation. Based on this, DIWPSO that has been set the best operating parameters is capable of accurately separating IMS overlapping peaks to identify the compound within a short time. Results: A comparison between the performance of DIWPSO and the improved particle swarm optimization (IPSO) found that DIWPSO with separation errors less than 2.34% overall outperforms IPSO whose maximum error is up to 5.58%. Moreover, the running time of DIWPSO is 30-80 times less than that of IPSO, and DIWPSO exhibits stronger robustness. Conclusions: This method can automatically identify the component number of IMS overlapping peaks and resolve them with muticomponents and different overlapped degrees rapidly and accurately, which further improves the structural resolution of IMS. [ABSTRACT FROM AUTHOR]