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Identifying parameters in photovoltaic (PV) cell and module models is one of the primary challenges of the simulation and design of photovoltaic systems. Metaheuristic algorithms can find near-optimal solutions within a reasonable time for such challenging real-world optimization problems. Control parameters must be adjusted with many existing algorithms, making them difficult to use. In real-world problems, many of these algorithms must be combined or hybridized, which results in more complex and time-consuming algorithms. This paper presents a new artificial parameter-less optimization algorithm (APLO) for parameter estimation of PV models. New mutation operators are designed in the proposed algorithm. APLO's exploitation phase is enhanced by each individual searching for the best solution in this updating operator. Moreover, the current best, the old best, and the individual's current position are utilized in the differential term of the mutation operator to assist the exploration phase and control the convergence speed. The algorithm uses a random step length based on a normal distribution to ensure population diversity. We present the results of a comparative study using APLO and well-known existing parameter-less meta-heuristic algorithms such as grey wolf optimization, the salp swarm algorithm, JAYA, teaching-learning based optimization, colliding body optimization, as well as three major parameter-based algorithms such as differential evolution, genetic algorithm, and particle swarm optimization to estimate the parameters of PV the modules. The results revealed that the proposed algorithm could provide excellent exploration–exploitation balance and consistency during the iterations. Furthermore, the APLO algorithm shows high reliability and accuracy in identifying the parameters of PV cell models. [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]

In recent years, detecting credit card fraud transactions has been a difficult task due to the high dimensions and imbalanced datasets. Selecting a subset of important features from a high-dimensional dataset has proven to be the most prominent approach for solving high-dimensional dataset issues, and the selection of features is critical for improving classification performance, such as the fraud transaction identification process. To contribute to the field, this paper proposes a novel feature selection (FS) approach based on a metaheuristic algorithm called Rock Hyrax Swarm Optimization Feature Selection (RHSOFS), inspired by the actions of rock hyrax swarms in nature, and implements supervised machine learning techniques to improve credit card fraud transaction identification approaches. This approach is used to select a subset of optimal relevant features from a high-dimensional dataset. In a comparative efficiency analysis, RHSOFS is compared with Differential Evolutionary Feature Selection (DEFS), Genetic Algorithm Feature Selection (GAFS), Particle Swarm Optimization Feature Selection (PSOFS), and Ant Colony Optimization Feature Selection (ACOFS) in a comparative efficiency analysis. The proposed RHSOFS outperforms existing approaches, such as DEFS, GAFS, PSOFS, and ACOFS, according to the experimental results. Various statistical tests have been used to validate the statistical significance of the proposed model. [ABSTRACT FROM AUTHOR]

With the aim of improving passive safety protection in subway vehicles, this paper investigates a cutting energy-absorbing structure by means of experiment and simulation. The proposed structure comprises a cutting knife, an energy-absorbing tube, and a support tool. The Johnson-Cook material model is used to study the coupled thermal–structural cutting energy-absorbing process, and a finite-element model of the cutting energy-absorbing structure is validated using data from a cutting collision experiment. The sensitivity of each factor is discussed using extreme difference analysis to identify the effects of the cutting depth, cutting width, and cutting knife rake angle on the energy absorbed (EA) and the peak cutting force (PCF). Based on sample points obtained from an optimal Latin-hypercube experimental design, a relationship between the crashworthiness index and the design variables is formulated using polynomial-response-surface and radial-basis-function surrogate models. Through comprehensive error analysis, the most accurate surrogate model is used to optimize the crashworthiness of the structure. The multi-objective particle swarm optimization algorithm is applied to multi-objective optimization to obtain the maximum EA and minimum PCF. The weighting factor plays an important role in selecting the ideal Pareto solution, and the optimal solution is determined using the minimum distance selection method. The results allow us to determine the structure with the best crashworthiness, for which EA = 991 kJ and PCF = 1441 kN. • Present a cutting energy absorption structure for used in subway vehicles. • The coupled thermal–structural method is used to analysis the crashperformance of the cutting absorption energy structure. • The results of simulations are in good agreement with the experimental results. • The multi-objective optimization of cutting energy absorption structure is carried out by two surrogate models. • The MOPSO algorithm is used to obtain the optimal parameters of the cutting energy absorption structure. [ABSTRACT FROM AUTHOR]

Summary: Automatic generation control (AGC) plays a crucial role in modern power system to control the frequency with change in power demand. A novel fractional order (FO) hybrid PID‐fuzzy‐PID (FO‐hPID‐FPID) controller is proposed in this paper as the secondary controller for AGC of a three unequal area interconnected nonlinear reheat thermal power system considering generation rate constraint. This paper also deals with the implementation of FO fuzzy PID (FOFPID) and FO PID (FOPID) controllers in addition to the proposed controller along with their counterpart integer order controllers to establish the dominance of FO controllers over integer order controllers. Various parameters of the controllers are optimized by hybridizing two global optimization techniques: particle swarm optimization and modified sine cosine algorithm (MSCA) and the optimization techniques so obtained is called as hybrid PSO‐MSCA (hPSO‐MSCA) technique. A step load perturbation of 0.01 p.u. is injected in area‐1, and the system response is investigated independently with different controllers. It is revealed that the proposed FO‐hPID‐FPID delivers superior performance than the other controllers. Sensitivity analysis for parametric variations is carried out, and random load test is performed to validate the robustness of the proposed controller. Further, it is observed that the controllers deliver result in an ascending order of control performance as PID, FOPID, FPID, FOFPID, hPID‐FPID and FO‐hPID‐FPID controllers with FO‐hPID‐FPID controller delivering the best result and the FO controllers deliver better control performance than their integer order counterparts. [ABSTRACT FROM AUTHOR]

With the significant increase of social informatization, the emerging information technology represented by machine vision has been applied to more and more scenes. Among them, the detection and extraction of human skeleton in a dance video based on this technology has a huge market demand in education and training. However, the existing detection and extraction technology has the problems of slow recognition speed and low extraction accuracy. Therefore, this paper proposes a neural network based on particle swarm optimization to detect and extract human skeletons in a dance video. Through the research and test on different data sets, it is found that the neural network based on particle swarm optimization algorithm has good detection and extraction ability and has high accuracy for the detection and recognition of human skeleton points. Among them, on all MPII data sets, the average accuracy of PSO-LSTM proposed in this paper is 3.9% higher than that of other optimal algorithms; on the PoseTrack data set, the average accuracy of detection and extraction is improved by 2.3%. The above results show that the neural network based on particle swarm optimization has fast detection speed and good extraction accuracy and can be used for the detection and extraction of human skeleton in a dance video. [ABSTRACT FROM AUTHOR]

• A variable order fractional accumulation generating operator is proposed. • A variable order derivative is introduced into a grey model. • The effect of the new model is better than that of similar competitive models. • The new model can be used to predict problems with variable order characteristics. The use of constant order differential equations to describe the evolution of complex systems is often unable to describe some of the changing characteristics of the systems accurately. Variable order fractional derivatives provide us with new tools to solve such problems. In this paper, the accumulation and derivative orders of the classic grey model are expanded from constants to functions, and a variable order fractional grey model is established to describe the evolution process of complex systems. Firstly, this paper defines the variable order fractional accumulation generation sequence. On the basis of this sequence, a variable order fractional derivative grey model is established, the parameters of the model are estimated using the least square method, and the quantum particle swarm optimization algorithm is used to solve the order of fractional derivative and accumulation. Sadik transform and Laplace transform are adopted to obtain the analytical solution of the new model. Lastly, the effectiveness of the new model is verified through four cases. Compared with other models, the variable order fractional model can describe the development process of complex systems more accurately. [ABSTRACT FROM AUTHOR]

The automobile instrument is the indispensable item that is essential to keep the driver conversant of the process of the engineer and the other system. There are different parameters involved in the automobile instrument, which requires a different process involved in creating the automobile parameter. This research is designed to show the simulation development of injection molding and optimization of automobile instrument parameters using different optimization techniques. In this paper, Taguchi Orthogonal parameter design and particle swarm optimization (PSO) are used to measure the shrinkage volume rate and warpage amount rate. Most of the studies are designed to calculate the reduction and warpage for the gate location and glass material. The Taguchi orthogonal design contains five different parameters which are inversely proportional to each other. Hence, by optimizing those parameters, the injection molding procedure is passed out. In this research, the paper is designed to process the injection molding for the automobile instrument parameter using optimization techniques. [ABSTRACT FROM AUTHOR]

Wireless powered communication network (WPCN) is a promising technique to resolve the power constraint issue faced by wireless nodes at the same time it also provides green and a safer solution. This paper studies, mutual sharing between WPCN and simultaneous wireless information and power transfer model where optimal resource allocation occurs between licensed/bandwidth group and an unlicensed/power group to enhance the system performance. The main aim of this paper is to maximize system weighted sum rate and minimize power consumption during transmission by optimal time allocation and energy beamforming vector allocation. These multiobjective combinatorial problems are solved using joint metaheuristic particle swarm optimization-cuckoo search algorithm to provide an optimal solution. The results are compared using a conventional mathematical optimization algorithm to set as a baseline for performance parameters. The obtained results demonstrate that the efficient joint metaheuristic approach provides a significant gain in the system performance and also offers a computationally less complex approach for solving a complicated non-convex multiobjective problem of wireless powered network. [ABSTRACT FROM AUTHOR]

A key area of wireless communications is visible light communication which uses solid-state light-emitting-diode. This paper shows, a comparative study to optimize the received signal strength indication (RSSI) in the optical wireless link between two optimization algorithms. The optimization algorithm we used in the paper is a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm. To, compute the maximum RSSI over the non-line-of-sight (NLOS) link, within an indoor environment, and to find the optimum NLOS route through distinct angles to achieve better communication quality, we have used those algorithms. Simulations were performed at matlab to check the feasibility of the scheme proposed. Also, we have compared the efficiency of the both system. In addition, GA and PSO detect the maximum RSSI of the receiver (Rx) in a specific direction instead of calculating RSSI at any random angle. The simulation result shows that the proposed system precisely detects the direction that provides an optimal RSSI for the Rx. This paper concludes that the GA is computationally effective while compared with PSO. After several iterations, the result estimates that the average elapsed time of the GA is 0.21 ms and the PSO is 30 ms, respectively. In the case of user positions 1 and 2, the percentage enhancement for GA as compared to PSO is almost 4.2% and 12.5% respectively. [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]

Summary: Even with significant progresses in the maximum power point (MPP) research area, the necessity to improve the existing methods becomes mandatory to increase the energy conversion efficiency. Since the power–voltage (P‐V) characteristic curve of photovoltaic (PV) arrays has multiple peaks under partially shaded (PS) conditions, the conventional MPP tracking (MPPT) control methods have the difficult challenge of locate the global MPP (GMPP) among many local MPPs (LMPPs). In recent years, numerous research papers have been focused on techniques to efficiently track the GMPP and alleviate the partial shading effects. One of the most popular evolutionary search technique is particle swarm optimization (PSO) that provides high tracking speed and the ability operate under different environmental conditions. For solving some conventional PSO technique common weaknesses, several modifications and improvements have emerged in the past years. This paper provides a comparative and comprehensive review of some relevant PSO‐based methods taking into account the effects of important key issues such as particles initialization criteria, search space, convergence speed, initial parameters, performance with and without partial shading, and efficiency. The simulation results are validated under numerous test conditions using MATLAB code and Simulink package. [ABSTRACT FROM AUTHOR]

In order to deal with the problem that the noise of leakage signals from natural gas pipelines has a great influence on the feature extraction of pipeline leakage, this paper proposes a signal denoising method of variational mode decomposition (VMD) and Euclidean distance (ED) based on optimizing parameters of classification particle swarm optimization (CPSO) algorithm. First, CPSO algorithm is used to optimize parameters K and α of VMD, adaptively. The sum of the ratio of the mean and variance of the cross-correlation coefficient and the ratio of the mean and variance of kurtosis is used as the fitness function of CPSO. Then, the optimized VMD is used to decompose the signal to obtain several intrinsic mode functions (IMFs). Finally, ED is used to select the effective modes, and the signal is reconstructed to achieve signal noise reduction. The corresponding evaluation indicators show that the accuracy and robustness of the improved method are better than other noise reduction methods. The denoising effect is significant, which proves that the algorithm proposed in this paper is effective in signal filtering. [ABSTRACT FROM AUTHOR]

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

The feed forward neural network (FNN) has a significant breakthrough in recent times in solving different real-life complex problems. The success of FNN is primarily attributed to its architecture, the optimization technique used, and the tuning of hyper-parameters to identify different patterns in data. This study tries to find an optimization technique that will play a vital role in an FNN to get better results. Whale optimization algorithm (WOA) is one of the popular nature-inspired optimization techniques used to solve real-time sciences and engineering problems. A new optimization approach namely chaotic oppositional-based whale optimization algorithm (COWOA) is developed in this paper, for the first time, to discuss the FNN problems of four different dataset by integrating chaotic function and oppositional-based learning with WOA. Simulation results are presented to validate the success of the suggested COWOA approach and demonstrate its dominance over basic WOA, chaotic WOA (CWOA), particle swarm optimization (PSO), Adam optimization algorithm (AOA), and stochastic gradient descent (SGD) for both unimodal and multimodal dataset problems. The comparative analysis with other optimization techniques validates its superiority and robustness on four standard dataset of FNN problem. Furthermore, to validate the performance of the proposed algorithm, a statistical analysis is performed. The outcome of the statistical results validates the superiority and acceptability of COWOA over other optimization methods. [ABSTRACT FROM AUTHOR]

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

Generation expansion planning (GEP) is a widely studied problem in the literature. However, with increasing participation of renewable energy sources (RES), the problem has to be looked upon considering intermittency associated with these sources. Integration of storage devices is seen as a viable option for counteracting the intermittency, particularly in isolated micro-grid (IMG). This paper puts forth a generalized mathematical structure for GEP centred on techno-economic criteria which duly addresses uncertainties associated with RES and correspondingly proposes a plan for addition of RES and storage units. As GEP problem is a highly constrained, large-scale problem involving assessment of enormous combinations, a decentralized multi-agent system (MAS) is used along with butterfly particle swarm optimization (BFPSO) technique for parallel processing. Hybridization of features of MAS with BFPSO called as multi-agent-based butterfly PSO algorithm (MABFPSO) presents a time effective and more accurate alternative to existing optimization techniques for addressing GEP problem. The formulation has been applied on IMG comprising of RES (solar and wind) and battery storage system. Results obtained from MABFPSO are compared with PSO with the purpose of establishing superiority of MABFPSO. It has been found that MABFPSO provides a cost benefit of 3.5% in comparison with classic PSO. For the same reliability standards, the capacity addition required for solar and wind generators is similar for both the techniques. However, the storage capacity requirement with PSO is 100% higher in comparison with that of MABFPSO. The comparison of convergence characteristics of two techniques clearly demonstrates that on the iteration scale, MABFPSO presents 29.6% faster convergence in comparison with that of PSO. [ABSTRACT FROM AUTHOR]

Reporting the empirical results of swarm and evolutionary computation algorithms is a challenging task with many possible difficulties. These difficulties stem from the stochastic nature of such algorithms, as well as their inability to guarantee an optimal solution in polynomial time. This research deals with measuring the performance of stochastic optimization algorithms, as well as the confidence intervals of the empirically obtained statistics. Traditionally, the arithmetic mean is used for measuring average performance, but we propose quantiles for measuring average, peak and bad-case performance, and give their interpretations in a relevant context for measuring the performance of the metaheuristics. In order to investigate the differences between arithmetic mean and quantiles, and to confirm possible benefits, we conducted experiments with 7 stochastic algorithms and 20 unconstrained continuous variable optimization problems. The experiments showed that median was a better measure of average performance than arithmetic mean, based on the observed solution quality. Out of 20 problem instances, a discrepancy between the arithmetic mean and median happened in 6 instances, out of which 5 were resolved in favor of median and 1 instance remained unresolved as a near tie. The arithmetic mean was completely inadequate for measuring average performance based on the observed number of function evaluations, while the 0.5 quantile (median) was suitable for that task. The quantiles also showed to be adequate for assessing peak performance and bad-case performance. In this paper, we also proposed a bootstrap method to calculate the confidence intervals of the probability of the empirically obtained quantiles. Considering the many advantages of using quantiles, including the ability to calculate probabilities of success in the case of multiple executions of the algorithm and the practically useful method of calculating confidence intervals, we recommend quantiles as the standard measure of peak, average and bad-case performance of stochastic optimization algorithms. [ABSTRACT FROM AUTHOR]

An extended membrane system with a dynamic nested membrane structure, which is integrated with the evolution-communication mechanism of a cell-like P system with evolutional symport/antiport rules and active membranes (ECP), and the evolutionary mechanisms of particle swarm optimization (PSO) and improved PSO inspired by starling flock behavior (SPSO), named DSPSO-ECP, is designed and developed to try to break application restrictions of P systems in this paper. The purpose of DSPSO-ECP is to enhance the performance of extended membrane system in solving optimization problems. In the proposed DSPSO-ECP, the updated model of velocity and position of standard PSO, as basic evolution rules, are adopted to evolve objects in elementary membranes. The modified updated model of the velocity of improved SPSO is used as local evolution rules to evolve objects in sub-membranes. A group of sub-membranes for elementary membranes are specially designed to avoid prematurity through membrane creation and dissolution rules with promoter/inhibitor. The exchange and sharing of information between different membranes are achieved by communication rules for objects based on evolutional symport rules of ECP. At last, computational results, which are made on numerical benchmark functions and classic test images, are discussed and analyzed to validate the efficiency of the proposed DSPSO-ECP. [ABSTRACT FROM AUTHOR]

The Riemannian manifold optimization algorithms have been widely used in machine learning, computer vision, data mining, and other technical fields. Most of these algorithms are based on the geodesic or the retracement operator and use the classical methods (i.e., the steepest descent method, the conjugate gradient method, the Newton method, etc.) to solve engineering optimization problems. However, they lack the ability to solve non-differentiable mathematical models and ensure global convergence for non-convex manifolds. Considering this issue, this paper proposes a quantum-behaved particle swarm optimization (QPSO) algorithm on Riemannian manifolds named RQPSO. In this algorithm, the quantum-behaved particles are randomly distributed on the manifold surface and iteratively updated during the whole search process. Then, the vector transfer operator is used to translate the guiding vectors, which are not in the same Euclidean space, to the tangent space of the particles. Through the searching of these guiding vectors, we can achieve the retracement and update of points and finally obtain the optimized result. The proposed RQPSO algorithm does not depend on the expression form of a problem and could deal with various engineering technical problems, including both differentiable and non-differentiable ones. To verify the performance of RQPSO experimentally, we compare it with some traditional algorithms on three common matrix manifold optimization problems. The experimental results show that RQPSO has better performance than its competitors in terms of calculation speed and optimization efficiency. [ABSTRACT FROM AUTHOR]

The traditional Grey Wolf Optimization algorithm (GWO) has received widespread attention due to features of strong convergence performance, few parameters, and easy implementation. However, in actual optimization projects, there are problems of slow convergence speed and easy to fall into local optimal solution. The paper proposed a Grey Wolf Optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy (CG-GWO) in response to the above problems. The Cauchy-Gaussian mutation operator is introduced to increase the population diversity of the leader wolves and improve the global search ability of the algorithm. This work retains outstanding grey wolf individuals through the greedy selection mechanism to ensure the convergence speed of the algorithm. An improved search strategy was proposed to expand the optimization space of the algorithm and improve the convergence accuracy. Experiments are performed with 16 benchmark functions covering unimodal functions, multimodal functions, and fixed-dimension multimodal functions to verify the effectiveness of the algorithm. Experimental results show that compared with four classic optimization algorithms, particle swarm optimization algorithm (PSO), whale optimization algorithm (WOA), sparrow optimization algorithm (SSA), and farmland fertility algorithm (FFA), the CG-GWO algorithm shows better convergence accuracy, convergence speed, and global search ability. The proposed algorithm shows the same better performance compared with a series of improved algorithms such as the improved grey wolf algorithm (IGWO), modified Grey Wolf Optimization algorithm (mGWO), and the Grey Wolf Optimization algorithm inspired by enhanced leadership (GLF-GWO). [ABSTRACT FROM AUTHOR]

In industrial applications and automation, the robotic manipulators exhibit a significant role. Several complex robotic systems performed a number of industrial works named spray painting, welding, assembly, pick and place action, etc. The end-effector's position and the joint angles play a vital role since any task is activated inside the pre-defined robotic manipulator's work space. Also, the problem of trajectory planning is a very challenging task in the robotic fields. To solve these problems, this paper proposes a kinematics analysis and trajectory planning of an Industrial robotic manipulators (IRMs) based on the hybrid optimization algorithms. Here, three IRMs such as PUMA 560 (6 DOF), KUKA LBR iiwa 14 R820 (7DOF) and ABB IRB 140 (6DOF) are considered. For each robot, the forward and the inverse kinematics (IK) are analysed and also the trajectory planning of each robot is discussed using the hybrid optimization algorithms. In this work, 18 optimization algorithms such as PSO (particle swarm optimization), SSO (social spider optimization), DFO (dragonfly optimization), BOA (butterfly optimization), CSA (crow search algorithm), BSA (bird swarm algorithm), SHO (selfish herd optimization), KHO (krill herd optimization), ALO (antlion optimization), ACO (ant colony optimization), GWO (Grey wolf optimization), GOA (grasshopper optimization), SBO (satin bowerbird optimizer), WCO (world cup optimization), COA (cuckoo optimization algorithm), CFA (cuttlefish algorithm), SOA (seagull optimization algorithm), and TSA (tunicate swarm algorithm) are utilized for both forward and inverse kinematic analysis and the trajectory planning problem. For the PUMA 560, KUKA LBR iiwa 14 R820, and ABB IRB 140 IRMs, forward kinematics (FK) are solved by the hybrid combination of PSO-SSO, SHO-KHO, and SBO-WCO individually. Also, the IK are solved by the DFO-BOA, ALO-ACO and COA-CFA for each IRMs. The trajectory planning problem is solved by the CSA-BSA, GWO-GOA and SOA-TSA for each robot individually. These optimization techniques give number of solution for kinematics and trajectory problem but it converses the best solution for the minimum multi-objective function value. Each robot obtained the minimum travelling time for without and with an obstacle which is 0.0118 and 0.0313 s for PUMA and 0.0117 and 0.0310 s for KUKA, and 0.0114 and 0.0120 s for ABB IRB 140 robot. The advantages of these hybrid algorithm are shorter computation time, and fewer iterations. The kinematics and trajectory analysis of each IRM is simulated using robotic tool box in MATLAB with GUI interface. For each IRM, optimized position values of the end effector, joint angles and the best optimal path are computed with minimum objective function. Finally, the performance of each robot is compared to the existing robotic works. [ABSTRACT FROM AUTHOR]

In this paper, the combination of particle swarm optimization (PSO) and gravitational search algorithm (GSA) is enhanced by the first-order gradient method and a new optimization algorithm is introduced as GPSG. In metaheuristic methods, some search directions are randomly selected and the resulting information gradually progresses toward the optimal solution. Since along the gradient direction usually yields the largest decrease in the desired function, it is added to the GSA and PSO process to allow for faster and more accurate convergence. By integrating the metaheuristic methods with the gradient directions, a powerful method for optimizing functions has been made possible. A novel approach is introduced by merging two metaheuristic methods with the randomly generated steepest decent directions. Numerous examples of unconstrained problems of mathematical functions of CEC2005 and CEC2017 together with some constrained examples of stress and displacement structural design problems have been chosen to demonstrate the reliability and capability of the presented method. Comparison of the numerical results with some methods indicates that the average rank of the proposed technique is better. [ABSTRACT FROM AUTHOR]

Aiming at the inherent defects of BP neural network in the field of rolling bearing fault diagnosis, based on the optimization of particle swarm optimization algorithm, this paper uses a variety of optimization strategies to optimize the particle swarm optimization algorithm, and then uses the optimized particle swarm optimization algorithm to optimize the BP neural network. Therefore, a new fault diagnosis method (Dual Strategy Particle Swarm Optimization BP neural network, DSPSOBP) is proposed. DSPSOBP fault diagnosis method is mainly divided into two steps. The first step is EMD decomposition of vibration signal, and the second step is to classify rolling bearing faults by using BP neural network optimized by Double Strategy Particle Swarm Optimization algorithm. Experiments show that DSPSOBP has stronger advantages than BP neural network basic fault diagnosis model. [ABSTRACT FROM AUTHOR]

In this paper, a fast reverse design method of multi-layer frequency selective surface (FSS) based on the equivalent circuit (EC)-assisted multi-objective particle swarm optimization (MOPSO) algorithm is proposed. Converting the desired frequency response requirements into an EC and then determining structural parameters via building blocks' EC and MOPSO simplifies the inverse design process of the FSS. The layer-by-layer building blocks of EC are used instead when dealing with the problem of complicated EC computation associated with multi-layer FSS. By converting factors that are difficult to calculate, such as interlayer coupling, into an MOPSO seeking process, the computational complexity is reduced while the design accuracy can be improved. To begin with, it is necessary to determine the distribution of zeros and poles according to the design goals in order to calculate the appropriate EC. Then, the preliminary design of the FSS has been completed in accordance with the EC and the associated building block structure. Finally, the objective function of the optimization algorithm is determined according to the desired frequency response, and the FSS structure parameters are optimized. Taking dual band-stop FSS and triple band-pass FSS structures as examples, the transmission coefficient results obtained by the proposed reverse design method are consistent with the transmission coefficient results based on the ECs, which verifies the effectiveness of the proposed method. The optimized triple band-pass FSS demonstrates strong stability even at oblique incident angles of up to 45° in both TE and TM polarizations. [ABSTRACT FROM AUTHOR]

• A novel nonlinear impulsive time-delay system in fed-batch process is proposed. • The robust parameter identification problem is presented to determine the optimal parameters. • A parallel particle swarm optimization algorithm is developed to solve the identification problem. In this paper, taking the feeding process as an impulsive form and considering the time-delay in fermentation process, we propose a nonlinear impulsive time-delay system to formulate the fed-batch culture in microbial 1,3-propanediol production. For this system, we discuss some important properties of the solution. To determine the optimal time-delay and kinetic parameters in the system, we then present a robust parameter identification model subject to continuous state inequality constraints. By the constraint transcription technique and a penalty method, we transform the robust parameter identification problem to a sequence of optimization problems with only box constraints. On this basis, we propose a parallel Particle Swarm Optimization algorithm to solve the resulting problem. Finally, the numerical results show that the impulsive time-delay system can well describe the fed-batch process and the developed algorithm is highly effective. [ABSTRACT FROM AUTHOR]

Partial shading is an unavoidable severe problem experienced by a photovoltaic system. The P‐V characteristics are highly non‐linear and exhibit multiple peaks under partial shading conditions (PSC). The conventional MPPT techniques are unable to track global maximum power point (GMPP) under PSC. Various bio‐inspired optimization methods employed for MPPT were presented in the literature. The operation of each algorithm differs from one another when tracking the GMPP. Therefore, this paper proposes a hybrid of adaptive particle swarm optimization (APSO) and Nelder Mead (NM) in searching for the global maximum power point. In this hybrid method, the unnecessary movement of APSO particles is minimized, leading to rapid convergence without the local trapping of the NM process. Thus, the proposed algorithm can balance both explorations by APSO and exploitation by NM. Extensive simulations are performed using Matlab/Simulink, which shows that this method successfully tracks global power under various shading patterns. The proposed method is compared with existing advanced optimization algorithms. Further validation is done via experimental analysis, and it confirms that the proposed method performs better in tracking maximum global power, less steady‐state oscillations, and fast‐tracking speed. The results demonstrate the effectiveness of proposed method in tracking GMPP with an average efficiency of 99.32% and an average tracking time of 0.513 s. [ABSTRACT FROM AUTHOR]

The integration of distributed generations (DGs) to the distribution networks is increasing day by day to reduce the pressure of rising load demand. The excessive penetration of the DGs at improper locations leads to performance deterioration in terms of higher losses, over-voltages, poor voltage stability and changes in fault levels. Thus, in this paper, the DGs' optimal location, size and power factor, capable of satisfying maximum load demand, are obtained in distribution networks using performance indices related to power loss, voltage stability, short-circuit current deviation and substation power intake. The optimisation is performed using particle swarm optimization and chaotic particle swarm optimisation algorithms considering the voltage and current constraints. A new voltage stability index that remains unaffected by radial or mesh flow of current in the network is proposed to quantify the voltage stability of the system. The short-circuit current deviation index is formulated to decrease the deviations in fault level, so the existing protective devices, with minimum or no changes in their settings, protect the system having DGs. The minimisation of the substation power index causes an increase in penetration of the DGs. The optimal power factor of the DGs gives better loss reduction. The proposed methodology is also applied to obtain optimal penetration of the DGs at their optimal locations for each hour of 96-h load profile representing the load curve of four seasons of a year. The methodology is tested on 33-node and 69-node distribution systems in both radial and mesh configurations. [ABSTRACT FROM AUTHOR]

PV systems are widely installed and the building block to the solar system is the solar cell itself. In this paper, a new idea was presented, where Artificial Neural Network (ANN) was used to extract the five parameters of a solar module using only the basic voltage and current parameters. In addition, a large number of PV modules datasheet information was used to train an ANN to obtain the five parameters of any new module. In order to train the neural network (NN), particle swarm optimization technique was employed to obtain the input and output data set. Instead of five parameters of a solar module, only four parameters were used for the optimization technique and remaining was found from the direct equation.A comparison of Multilayer Perceptron and Radial Basis Function Neural Networks (NN) performance was provided. The proposed technique for solar cell parameters extraction was found robust, and accurate. Unlike parameter extraction for a specific panel, the proposed approach is a generalized approach that can extract the five parameters for any panel regardless its manufacturer. Using the mean square error and the squared error as measurement tools of the robustness of the proposed models, it has been found that the developed models are very accurate and robust. [ABSTRACT FROM AUTHOR]

Global optimization algorithms are widely used to effectively suppress the torque ripple in switched reluctance motors (SRMs). In this paper, an improved velocity-controllable particle swarm optimization algorithm (VCPSO) is proposed to optimize the turn-off angle of an SRM under the current chopping control (CCC) method. In addition, the performances of three global optimization algorithms are compared and analyzed. The specific steps are outlined as follows. First, the static non-linear inductance–current–position and torque–current–position curves of the SRM are obtained through finite-element calculations, and a non-linear model of the SRM is established on this basis. Second, the turn-off angle optimization method based on the VCPSO is proposed. Finally, the performances of the simulated annealing algorithm (SA), the genetic algorithm (GA), and the proposed algorithm are compared and analyzed in terms of torque ripple suppression. The obtained results show that the proposed VCPSO has the advantages of a low number of iterations, low torque ripple, and small peak current in the torque ripple reduction issue of the SRM. [ABSTRACT FROM AUTHOR]

The main temperature compensation method for MEMS piezoresistive pressure sensors is software compensation, which processes the sensor data using various algorithms to improve the output accuracy. However, there are few algorithms designed for sensors with specific ranges, most of which ignore the operating characteristics of the sensors themselves. In this paper, we propose three temperature compensation methods based on swarm optimization algorithms fused with machine learning for three different ranges of sensors and explore the partitioning ratio of the calibration dataset on Sensor A. The results show that different algorithms are suitable for pressure sensors of different ranges. An optimal compensation effect was achieved on Sensor A when the splitting ratio was 33.3%, where the zero-drift coefficient was 2.88 × 10−7/°C and the sensitivity temperature coefficient was 4.52 × 10−6/°C. The algorithms were compared with other algorithms in the literature to verify their superiority. The optimal segmentation ratio obtained from the experimental investigation is consistent with the sensor operating temperature interval and exhibits a strong innovation. [ABSTRACT FROM AUTHOR]

Lung CAD system can provide auxiliary third-party opinions for doctors, improve the accuracy of lung nodule recognition. The selection and fusion of nodule features and the advancement of recognition algorithms are crucial improving lung CAD systems. Based on the HDL model, this paper mainly focuses on the three key algorithms of feature extraction, feature fusion and nodule recognition of lung CAD system. First, CBAM is embedded into VGG16 and VGG19, and feature extraction models AE-VGG16 and AE-VGG19 are constructed, so that the network can pay more attention to the key feature information in nodule description. Then, feature dimensionality reduction based on PCA and feature fusion based on CCA are sequentially performed on the extracted depth features to obtain low-dimensional fusion features. Finally, the fusion features are input into the proposed MKL-SVM-IPSO model based on the improved Particle Swarm Optimization algorithm to speed up the training speed, get the global optimal parameter group. The public dataset LUNA16 was selected for the experiment. The results show that the accuracy of lung nodule recognition of the proposed lung CAD system can reach 99.56%, and the sensitivity and F1-score can reach 99.3% and 0.9965, respectively, which can reduce the possibility of false detection and missed detection of nodules. [ABSTRACT FROM AUTHOR]

Recently, there has been considerable research on combining multi-agent simulation and particle swarm optimization in practice. However, most existing studies are limited to specific engineering fields or problems without summarizing a general and universal combination framework. Moreover, particle swarm optimization can be less effective in complex problems due to its weakness in balancing exploration and exploitation. Yet, it is not common to combine multi-agent simulation with improved versions of the algorithm. Therefore, this paper proposes an improved particle swarm optimization algorithm, introducing a multi-level structure and a competition mechanism to enhance exploration while balancing exploitation. The performance of the algorithm is tested by a set of comparison experiments. The results have verified its capability of converging to high-quality solutions at a fast rate while holding the swarm diversity. Further, a problem-independent simulation-optimization approach is proposed, which integrates the improved algorithm into multi-agent systems, aiming to simulate realistic scenarios dynamically and solve related optimization problems simultaneously. The approach is implemented in a response planning system to find optimal arrangements for response operations after the Sanchi oil spill accident. Results of the case study suggest that compared with the commonly-used shortest distance selection method, the proposed approach significantly shortens the overall response time, improves response efficiency, and mitigates environmental pollution. [ABSTRACT FROM AUTHOR]

Aiming at the problems of single planning technology and relatively few resource types in the process of regional comprehensive energy system planning at the current stage, this paper proposes a method based on the construction of regional comprehensive energy system planning and the operation model. Through the integration of the particle swarm optimization algorithm, this method can better realize the optimization and economic operation of the regional comprehensive energy system and build a system optimization mode based on two stages of planning and operation to pursue the optimal configuration of system equipment. Through the simulation algorithm, it is found that the solution time of the traditional basic particle swarm optimization algorithm is 10.49s, while the average solution time of the particle swarm optimization system proposed in this study is 7.93s; the efficiency is increased by 24.4%, and the system operation efficiency is significantly improved, providing theoretical and technical guidance for the economic operation of the regional comprehensive energy system. [ABSTRACT FROM AUTHOR]

A fusion protein is a protein with at least two domains that are each encoded by a different gene and are combined into a single polypeptide by transcription and translation. For example, chromosomal rearrangement could result in the in vivo production of fusion proteins. One such fusion protein is the one responsible for chronic myelogenous leukaemia, the BCR-ABL protein. Recombinant DNA techniques could be used to create fusion proteins in vitro. By combining genes or portion of genes from similar or dissimilar organisms, fusion genes and proteins may be produced. But, real-time lab experiments for automated fusion protein functionality prediction are expensive and timeconsuming. This paper proposes a novel Fusion Protein Functionality Prediction based on a Hybrid Genetic Particle Swarm Optimization (HybGPSO) algorithm to deal with this problem. The cellular component, biological process, and molecular function of an unannotated fusion protein by the GO consortium are the three functionalities predicted by this algorithm. The results of the experiments demonstrate that the proposed HybGPSO algorithm accurately predicts the function of fusion proteins. [ABSTRACT FROM AUTHOR]

PEMFC system is a complex new clean power system. Based on MATLAB/Simulink, this paper develops a system-level dynamic model of PEMFC, including the gas supply system, hydrogen supply system, hydrothermal management system, and electric stack. The neural network fits the electric stack model to the simulation data. The effects of different operating conditions on the PEMFC stack power and system efficiency are analyzed. Combining the power of the reactor and the system efficiency to define the integrated performance index, the particle swarm optimization (PSO) algorithm is introduced to optimize the power density and system efficiency of the PEMFC with multiple objectives. The final optimal operating point increases the power density and system efficiency by 1.33% and 12.8%, respectively, which maximizes the output performance and reduces the parasitic power. [Display omitted] • Build PEMFC system level model in MATLAB/Simulink. • Analyze sensitivity analysis of operating conditions. • Combine stack power and system efficiency to define integrated performance index. • Introduce neural networks and PSO algorithms to optimize system performance. [ABSTRACT FROM AUTHOR]

As one of the main areas of value investing, the stock market attracts the attention of many investors. Among investors, market index movements are a focus of attention. In this paper, combining the efficient market hypothesis and the fractal market hypothesis, a stock prediction model based on mixed fractional Brownian motion (MFBM) and an improved fractional-order particle swarm optimization algorithm is proposed. First, the MFBM model is constructed by adjusting the parameters to mix geometric Brownian motion (GBM) and geometric fractional Brownian motion (GFBM). After that, an improved fractional-order particle swarm optimization algorithm is proposed. The position and velocity formulas of the fractional-order particle swarm optimization algorithm are improved using new fractional-order update formulas. The inertia weight in the update formula is set to be linearly decreasing. The improved fractional-order particle swarm optimization algorithm is used to optimize the coefficients of the MFBM model. Through experiments, the accuracy and validity of the prediction model are proven by combining the error analysis. The model with the improved fractional-order particle swarm optimization algorithm and MFBM is superior to GBM, GFBM, and MFBM models in stock price prediction. [ABSTRACT FROM AUTHOR]

This paper proposes adaptive fuzzy lead-lag controller structures for power system stabilizer and flexible AC transmission system based damping controllers to increase the stability of power system. The parameters of the proposed controller are tuned by a modified grasshopper optimization algorithm (MGOA). The new algorithm named MGOA accomplishes a proper balance between exploration and exploitation phases of original grasshopper optimization algorithm. This capability of MGOA is certified by using the benchmark functions by comparing with that of a grasshopper optimization algorithm, genetic algorithm, evolutionary strategies, particle swarm optimization, bat algorithm, population based incremental learning, flower pollination algorithm, monarch butterfly optimization and improved monarch butterfly optimization. The proposed controller is optimized and verified under various loading circumstances using MGOA method. The results of MGOA are compared with grasshopper optimization algorithm, genetic algorithm, and particle swarm optimization. Additionally, the results of the proposed MGOA are compared with conventional lead-lag controller to demonstrate its superiority. [ABSTRACT FROM AUTHOR]

In this paper, a whale optimization algorithm based on Levy flight and memory (WOALFM) is proposed to solve the curvature discontinuity problem in the global path planning of unmanned vehicles. Levy flight and chaotic mapping are introduced to disturb the solutions of each generation and enhance the diversity of solutions. A memory strategy based on fractional-order expansion is presented to remember the influence of the positions of individuals in previous generations on the positions of current generation. This strategy based on Levey flight and memory can enhance searching ability and avoid falling into local optimum. Furthermore, the ratio of global searching to local searching can be adjusted to achieve desired results in WOALFM algorithm. The proposed WOALFM algorithm is tested and compared with five algorithms including whale optimization algorithm (WOA), Moth-Flame Optimization (MFO), particle swarm optimization (PSO), Fractional-Order Velocity based Particle Swarm Optimization (FOPSO) and Grey Wolf Optimizer (GWO) on 23 standard benchmark functions. The experimental results show the effectiveness of WOALFM. The proposed algorithm is applied to smooth path planning problem of unmanned vehicles. Three factors, including the length, curvature and curvature derivative of a path are considered in order to obtain the shortest smooth path without collisions. The experimental results show that more collision-free paths can be obtained in lower computational cost by the presented method. [ABSTRACT FROM AUTHOR]

The structural health monitoring (SHM) approach plays a key role not only in structural engineering but also in other various engineering disciplines by evaluating the safety and performance monitoring of the structures. The structural damage detection methods could be regarded as the core of SHM strategies. That is because the early detection of the damages and measures to be taken to repair and replace the damaged members with healthy ones could lead to economic advantages and would prevent human disasters. The optimization-based methods are one of the most popular techniques for damage detection. Using these methods, an objective function is minimized by an optimization algorithm during an iterative procedure. The performance of optimization algorithms has a significant impact on the accuracy of damage identification methodology. Hence, a wide variety of algorithms are employed to address optimization-based damage detection problems. Among different algorithms, the particle swarm optimization (PSO) approach has been of the most popular ones. PSO was initially proposed by Kennedy and Eberhart in 1995, and different variants were developed to improve its performance. This work investigates the objectives, methodologies, and results obtained by over 50 studies (2005-2020) in the context of the structural damage detection using PSO and its variants. Then, several important open research questions are highlighted. The paper also provides insights on the frequently used methodologies based on PSO, the computational time, and the accuracy of the existing methodologies. [ABSTRACT FROM AUTHOR]

The time-interleaved analog-to-digital converters (TIADCs) technique is an efficient solution to improve the sampling rate of the acquisition system with low-speed ADCs. However, channel mismatches such as gain mismatch, time skew mismatch, and offset mismatch may seriously degrade the performance of TIADC. Furthermore, for high-speed signal acquisition, the gain and time skew mismatches would vary with the signal frequency, and the traditional fixed model does not work any longer. In this paper, a series of sinusoidal signals are adopted to estimate the variable mismatches. First, an autocorrelation-based approach is presented to estimate the gain mismatch. The information about the gain mismatch is extracted from the autocorrelation function of sub-ADC output samples. Then, the time skew mismatch is estimated by utilizing the particle swarm optimization algorithm. The reported simulation results show that the mismatches can be accurately estimated. Finally, a commercial 12.5 GSPS four-channel TIADC system is utilized to verify the performance of the proposed method. The spurious free dynamic range of the system can be improved by about 20 dB, and the effectiveness of the proposed estimation method is demonstrated. [ABSTRACT FROM AUTHOR]

In this paper, the main objective is the simulation of the electric supply for homes in remote areas located in Morocco (Oujda and Ouarzazate), Spain (Granada), and Algeria (Bechar). This simulation study is divided into two ideas, the first one is to optimize the hybrid system under a varied number of houses and the second part is to fix it in chosen values. This work relied on the particle swarm optimization (PSO) method to optimize and analyze the proposed stand-alone photovoltaic/wind/diesel/battery hybrid system. MATLAB software is used to run the simulations and to carry out the optimal solutions of the optimized system using a developed PSO algorithm. The PSO approach contains five principal operator axes, which are problem definition, PSO initial parameters, initialization, PSO main loop, and then run of the algorithm. Optimizing the system component sizes and reaching the minimum cost of energy (COE) were the function objectives of this optimization study. Moreover, calculation of the hydrogen production from the generated energy by PV and wind systems is performed. [ABSTRACT FROM AUTHOR]

This paper is concerned with the parameter estimation of non-linear discrete-time systems from noisy state measurements in the state-space form. A novel sparse Bayesian convex optimisation algorithm is proposed for the parameter estimation and prediction. The method fully considers the approximation method, parameter prior and posterior, and adds Bayesian sparse learning and optimization for explicit modeling. Different from the previous identification methods, the main identification challenge resides in two aspects: first, a new objective function is obtained by our improved Stein approximation method in the convex optimization problem, so as to capture more information of particle approximation and convergence; second, another objective function is developed with L 1 -regularization, which is sparse method based on recursive least squares estimation. Compared with the previous study, the new objective function contains more information and can easily mine more important information from the raw data. Three simulation examples are given to demonstrate the proposed algorithm's effectiveness. Furthermore, the performances of these approaches are analyzed, including parameter estimation of root mean squared error (RMSE), parameter sparsity and prediction of state and output result. [ABSTRACT FROM AUTHOR]

This paper focuses on the dynamic workforce scheduling and routing problem for the maintenance work of harvesters in a sugarcane harvesting operation. Technician teams categorized as mechanical, hydraulic, and electrical teams are assumed to have different skills at different levels to perform services. The jobs are skill-constrained and have time windows. During a working day, a repair request from a sugarcane harvester may arrive, and as time passes, the harvester's position may shift to other sugarcane fields. We formulated this problem as a multi-visit and multi-period dynamic workforce scheduling and routing problem (MMDWSRP) and our study is the first to address the workforce scheduling and routing problem (WSRP). A mixed-integer programming formulation and a hybrid particle swarm and whale optimization algorithm (HPSWOA) were firstly developed to solve the problem, with the objective of minimizing the total cost, including technician labor cost, penalty for late service, overtime, travel, and subcontracting costs. The HPSWOA was developed for route planning and maintenance work for each mechanical harvester to be provided by technician teams. The proposed algorithm (HPSWOA) was validated against Lingo computational software using numerical experiments in respect of static problems. It was also tested against the current practice, the traditional whale optimization algorithm (WOA), and traditional particle swarm optimization (PSO) in respect of dynamic problems. The computational results show that the HPSWOA yielded a solution with significantly better quality. The HPSWO was also tested against the traditional genetic algorithm (GA), bat algorithm (BA), WOA, and PSO to solve the well-known CEC 2017 benchmark functions. The computational results show that the HPSWOA achieved more superior performance in most cases compared to the GA, BA, WOA, and PSO algorithms. [ABSTRACT FROM AUTHOR]