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• Built numerical calculation model of interfacial heat transfer coefficient (IHTC) in freezing sand mold casting process by using finite difference method (FDM) combined with conjugate gradient method (CGM) and particle swarm optimization algorithm (PSO). • Combined with experimental and numerical calculation methods to determine the interface heat transfer coefficient (IHTC) at the freezing sand mold casting process. • The interfacial heat transfer coefficient (IHTC) shows "double fluctuation" in a low water content while "double turning" in higher. • The variation of interfacial heat transfer coefficient (IHTC) during freezing sand mold casting is divided into four stages. Compared to the resin sand mold casting process, frozen casting is more environmentally friendly, providing a better working environment and enhanced supercooling degree. The interfacial heat transfer coefficient (IHTC) between frozen sand mold and metal is an important parameter that significantly influences the final mechanical properties and microstructure of the castings. This paper solved the inverse heat conduction problem using the finite difference method (FDM). In addition, the conjugate gradient method (CGM) was adopted to calculate the temperature distribution and heat flux in the molten metal. At the same time, the particle swarm optimization algorithm (PSO) was used in temperature distribution determination in frozen sand mold. The interfacial heat transfer coefficient (IHTC) was estimated during the solidification of ZL101. The results showed a good agreement between calculated and experimental data, obtaining accurate casting interface temperature T m , frozen sand mold interface temperature T s , heat flux q , and IHTC. The analysis of the IHTC variation revealed a water content value within the range of 4 wt.% to 5 wt.% resulted in IHTC in two types of interpretation, called 'fluctuation type' and 'turning type'. [Display omitted] Combined with experimental and numerical calculation methods to determine the interface heat transfer coefficient (IHTC) at the freezing sand mold casting process. Analyzed the relationship between water content and interfacial heat transfer coefficient of freezing sand mold. [ABSTRACT FROM AUTHOR]

This paper presents a localization task for a robot swarm guided by the RAOI behavioral rules (repulsion, attraction, orientation, and influence). Slight changes in these parameters can significantly affect the task's performance. To address this challenge, we have developed a swarm simulator incorporating the robots' dynamic model. The localization task is evaluated using objective functions, represented as metrics, which are minimized using multi-objective optimization techniques. Our results showcase the Pareto fronts, illustrating how the objective functions react to variations in the RAOI parameters, aiming to expedite target localization while maintaining the swarm's integrity. • Repulsion, Attraction, Orientation, and Influence parameters (RAOI) are proposed. • We steer a swarm of robots through RAOI parameters to solve collective tasks. • Swarm efficiency depends on simulations and satisfies several objective functions. • Pareto fronts visualize the interaction of objective functions with RAOI parameters. • Results highlight the potential applicability of these methods in swarm tasks. [ABSTRACT FROM AUTHOR]

Multimodal multi-objective optimization problems (MMOPs) represent a highly challenging class of complex problems, characterized by the presence of several Pareto solution sets in the decision space which map to the identical Pareto-optimal front. The goal of solving MMOPs is to find multiple distinct Pareto sets to sustain a balance between good convergence and diversification of populations. In this paper, a multi-objective sand cat swarm optimization algorithm (MOSCSO) is developed to address MMOPs. In the MOSCSO algorithm, an adaptive clustering-based specific congestion distance technique is introduced to compute the level of crowdedness. This ensures an even distribution of individuals, avoiding excessive crowding in the local area. Subsequently, enhanced search-and-attack prey updating mechanisms are designed to effectively increase not only the exploration and exploitation capabilities of the algorithm but also to enhance the diversity of the swarm in both the decision space and the objective space. To verify the effectiveness of the proposed algorithm, the MOSCSO is applied to solve the CEC2019 complex multimodal benchmark function. The experimental outcomes illustrate that the proposed approach possesses excellent performance in searching for Pareto solutions compared with other algorithms. Meanwhile, the method is also employed to address the map-based distance minimization problem, which further validates the usefulness of the MOSCSO. [ABSTRACT FROM AUTHOR]

The main challenge in kernel-based approximation theory and its applications is the conflict between accuracy and stability. Hybrid kernels can be used as one of the simplest and most effective tools to manage this challenge. This article uses a meshless scheme based on hybrid radial kernels (HRKs) to solve second kind Fredholm integral equations (FIEs). The method estimates the solution via the discrete collocation procedure based on a hybrid kernel that combines appropriate kernels with suitable weight parameters. The optimal parameters in the hybrid kernels can be calculated using the particle swarm optimization (PSO) algorithm based on the root mean square (RMS) error. This technique converts the problem under investigation into a system of linear equations. Moreover, the convergence of the suggested hybrid approach is studied. Lastly, some numerical experiments are included to reveal the accuracy and stability of the hybrid kernels approach. The numerical results demonstrate that the presented hybrid procedure meaningfully reduces the ill-conditioning of the operational matrices, at the same time, it maintains the accuracy and stability for all values of shape parameters. [ABSTRACT FROM AUTHOR]

Purpose: Currently, massive multiple-input multiple-output (m-MIMO) antennas are typically designed using complex trial-and-error methods. The purpose of this study is to determine an effective optimization method to achieve more efficient antenna design processes. Design/methodology/approach: This paper presents the design stages of a m-MIMO antenna array compatible with 5G smartphones operating in long term evolution (LTE) bands 42, 43 and 46, based on a specific algorithm. Each antenna element in the designed 10-port m-MIMO antenna array is intended to perfectly cover the three specified LTE bands. The optimization methods used for this purpose include the Nelder–Mead simplex algorithm, covariance matrix adaptation evolution strategy, particle swarm optimization and trust region framework (TRF). Findings: Among the primary optimization algorithms, the TRF algorithm met the defined objectives most effectively. The achieved antenna efficiency values exceeded 60.81% in the low band and 68.39% in the high band, along with perfect coverage of the desired bands, demonstrating the success of the design with the TRF algorithm. In addition, the potential electromagnetic field exposure caused by the designed m-MIMO antenna array is elaborated upon in detail using computational human models through specific absorption rate analysis. Originality/value: The comparison of four different algorithms (two local and two global) for use in the design of a 10-element m-MIMO antenna array with a complex structural configuration and the success of the design implemented with the selected algorithm distinguish this study from others. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to gain a better understanding on how metasurfaces behave, in terms of currents in each unit cell. A better knowledge of their behavior could lead to an ad-hoc design for specific applications. Design/methodology/approach: The methodology used is both theoretical and numerical; it is based on circuit theory and on an optimization procedure. Findings: The results show that when the knowledge of the current in each unit cell of a metasurface is needed, the most common approximations currently used are often not accurate. Furthermore, a procedure for the termination of a metasurface, with application-driven goals, is given. Originality/value: This paper investigates the distribution of the currents in a 2D metamaterial realized with magnetically coupled resonant coils. Different models for the analysis of these structures are illustrated, and the effects of the approximations they introduce on the current values are shown and discussed. Furthermore, proper terminations of the resonators on the boundaries have been investigated by implementing a numerical optimization procedure with the purpose of achieving a uniform distribution of the resonator currents. The results show that the behavior of a metasurface (in terms of currents in each single resonator) depends on different properties; as a consequence, their design is not a trivial task and is dependent on the specific applications they are designed for. A design strategy, with lumped impedance termination, is here proposed. [ABSTRACT FROM AUTHOR]

The deployment of floating photovoltaics (FPVs) is growing substantially in maritime locations. Despite the advantages gained from FPVs, it can still suffer from radiation fluctuation, which severely harms the system's production. Avoiding such obstacles can help countries like Egypt to increase their clean electricity production from its huge natural Northern Lakes. The adoption of soft computing maximum power point tracking (MPPT) algorithms showed great potential for optimizing system performance, especially the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. Yet insufficient memory and rapid radiation fluctuations can compromise algorithms. Hence, the present work addresses a hybrid GA-PSO algorithm combining the advantageous features of both algorisms to extract the MPPT from a 5.3 kW FPV system with greater frequency and accuracy in varying weather conditions, including partial shading (PS); MATLAB simulations are conducted to evaluate the proposed system's potential under a range of weather conditions. The hybrid GA-PSO outperforms GA or PSO alone while being simple to implement, with an average electrical efficiency and performance ratio of (23.40%,0.87) and (18.90%,0.79) for FPV and partially shading PSFPV, respectively. Finally, by implementing the hybrid GA-PSO, the FPV array may prevent 17.52 tons of CO 2 / season from being emitted into the air. [ABSTRACT FROM AUTHOR]

This paper introduces the Gooseneck Barnacle Optimisation Algorithm (GBO) as a novel evolutionary method inspired by the natural mating behaviour of gooseneck barnacles, which involves sperm casting and self-fertilization. GBO is mathematically modelled, considering the hermaphroditic nature of these microorganisms that have thrived since the Jurassic period. In contrast to the previously published Barnacle Mating Optimizer (BMO) algorithm, GBO more accurately captures the unique static and dynamic mating behaviours specific to gooseneck barnacles. The algorithm incorporates essential factors, such as navigational sperm casting properties, food availability, food attractiveness, wind direction, and intertidal zone wave movement during mating, creating two vital optimization stages: exploration and exploitation. Real-world case studies and mathematical test functions serve as qualitative and quantitative benchmarks. The results demonstrate that GBO outperforms well-known algorithms, including the previous BMO, by effectively improving the initial random population for a given problem, converging to the global optimum, and producing significantly better optimization outcomes. • Proposed Gooseneck Barnacle Optimizer mimics gooseneck barnacles' mating behaviour. • Inspiration: sperm casting, wind direction, intertidal wave action during mating. • Address the issues with original Barnacle Mating Optimizer. • Standard benchmark functions evaluate GBO's performance. • Hybridize with Least Square Support Vector Machine for real time-series forecasting. [ABSTRACT FROM AUTHOR]

This paper aims to present a new class of recurrent neural networks based on projection operators for solving variational inequalities and related optimization problems subject to linear equalities and bound constraints. In the proposed approach, the neural network structure is based on the Karush–Kuhn–Tucker (KKT) optimality conditions. Instead of commonly used activation functions, the KKT multipliers are considered control inputs and implemented with finite time stabilizing terms based on unit control. The output variables of the neural network are proven to be stable in the sense of Lyapunov and convergent to optimal solutions in finite time. In addition, a methodology based on the particle swarm optimization (PSO) algorithm is presented for the optimal selection of network design parameters. The main advantage of the proposed neural network is its fixed number of parameters regardless of problem dimension, enabling the network to be easily scalable from a lower to a higher dimension. Finally, simulation results of numerical examples are provided to illustrate the effectiveness and performance of the proposed neural network. [Display omitted] • Convergence to optimal solutions in finite time. • Neural network with a fixed number of parameters regardless of the problem dimension. • Design of activation functions that enforce a sliding mode. • PSO-based methodology for optimal selection of the neural network design parameters. [ABSTRACT FROM AUTHOR]

This paper presents a Lyapunov control scheme to drive finite-dimensional closed and Markovian open quantum systems into any target pure state with as high fidelity and as little time as possible. The control law is established by a Lyapunov function with an unknown Hermitian operator, where the eigenvectors of the operator are constructed based on the condition that the LaSalle invariant set contains the desired target state and a set of optimal eigenvalues of the Hermitian operator is searched by the quantum-behaved particle swarm optimization (QPSO) algorithm. In particular, for open systems, when the denominator in the control law is quite small, we propose an improved QPSO algorithm with constraints to enhance the flexibility in implementation. Finally, numerical simulations are carried out on a five-level and a three-qubit closed quantum systems, and a five-level and a ten-level open quantum systems. Simulation results demonstrate that the proposed control scheme can achieve high-fidelity transfer to desired target states for high-dimensional closed and open quantum systems. [ABSTRACT FROM AUTHOR]

The development of an accurate rock discrete element model is essential for modeling core drilling in the lunar environment. However, most of the microscopic parameters of the discrete element model cannot be determined directly by tests. Consequently, a rapid calibration method for microscopic parameters was developed, combining an approximation model, multiple objectives with particle swarm optimization algorithm (MOPSO), and orthogonal experimental design (OED). Range analysis and variance analysis were conducted to investigate the sensitivity of these parameters. Through this approach, the optimal solution for microscopic parameters was obtained to align with the mechanical characteristics of lunar rock simulant, providing a foundation for rock core drilling. The results demonstrate a high level of agreement between the simulated stress-strain curves and the test results. The deviation between the mechanical strength parameters and actual test values was found to be less than 5%, confirming the effectiveness and rationality of the proposed parameter calibration method. This method holds significant potential for widespread application in calibrating the parameters of discrete element models for rock materials. [ABSTRACT FROM AUTHOR]

In the present work, four flat-plate solar collectors of similar dimensions have been simulated using the CFD approach. Model 1 has been considered as circular with a U-shaped zigzag channel, while model 2 is a square-shaped plate. In the 3rd model, the number of turns in the channel has been increased by keeping the geometry of the pipe constant, while in the 4th model, multiple straight channels have been incorporated instead of a single U-shaped zigzag channel. Three-dimensional simulations have been primarily done to understand the effect of channel geometry area and turnings on the collector's performance. A steady-state incompressible forced convective heat transfer analysis has been performed here. Water is used as a fluid in the channel to carry heat. The results of the numerical analysis led to the conclusion that model 3 is the best flat plate solar collector where turnings are maximum. With a maximum collector performance of 41.57, model 3 performs 36.7% better than model 1. Additionally, when water flows at a rate of 8 m/s inside the collector's channel, the rate of heat transfer in model 3 is 2.5% faster than in model 1. Moreover, a global optimization study has been performed using two swarm-based metaheuristic algorithms, Gravitational Search Algorithm (GSA) and Firefly Algorithm (FA), with the aim of determining good optimum values of the design variables that will maximize collector efficiency for model 3 at different flow velocities. For model 3, the percentage variation in collector efficiency between CFD and optimised findings using metaheuristic algorithms is found to be (a) 41.08% for v = 2 m/s; (b) 28.41% for v = 4 m/s; (c) 21.71% for v = 6 m/s; and (d) 13.09% for v = 8 m/s. Therefore, it is possible to anticipate that as flow velocity increases, there will be a steady decrease in the variation between computed and optimized results. [ABSTRACT FROM AUTHOR]

Purpose: The direct torque control (DTC) of induction motor (IM) drive is featured by high ripples in the electromagnetic torque and stator flux profiles because they are controlled by two hysteresis regulators. Furthermore, the machine flux is not directly measurable. Hence, it is better to reconstitute it from the instantaneous electrical equations of the machine. Once the stator flux is estimated, we can guarantee a reliable sensorless DTC control. Thus, the purpose of this research work is to ensure fast response and full reference tracking of the IM under sensorless DTC strategy with desired dynamic behavior and low ripple levels. Design/methodology/approach: In this work, an improved DTC strategy, which is DTC_SVM_3L, is suggested. The first step of the designed approach is to substitute the conventional inverter feeding the motor with a three-level inverter because it guarantees reduced switching losses, improved quality of voltage waveform and low-current total harmonic distortion rate. The second aim of this paper is to make the IM operate at a constant switching frequency by using the nearest three vectors-based space vector modulation (SVM) technique rather than hysteresis controllers. The third objective of this study is to conceive a sliding-mode stator flux observer, which can improve the control performances by using a sensorless algorithm to get an accurate estimation, and consequently, increase the reliability of the system and decrease the cost of using sensors. The stability of the proposed observer is demonstrated based on the Lyapunov theory. To overcome the load change disturbance in the proposed DTC control strategy, this paper exhibits a comparative assessment of four speed regulation methods: classical proportional and integral (PI) regulator, fuzzy logic PI controller, particle swarm optimization PI controller and backstepping regulator. The entire control algorithm was tested under different disturbances such as stator resistance and load torque variations. Findings: It was ascertained that the IM, controlled with three-level inverter, exhibits good performances under the proposed DTC-SVM strategy based on a sliding-mode observer. The robustness of the suggested approach against parameter variations is also proved. Originality/value: The theoretical development of the proposed control strategy is thoroughly described. Then, simulations using Matlab/Simulink software are launched to investigate the merits of the sensorless DTC-SVM command of three-level inverter-fed IM drive with different speed regulators. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to intend and implement the optimal power flow, where tuning the production cost is done with the inclusion of stochastic wind power and different kinds of flexible AC transmission systems (FACTS) devices. Here, the speed with fitness-based krill herd algorithm (SF-KHA) is adopted for deciding the FACTS devices' optimal sizing and placement integrated with wind power. Here, the modified SF-KHA optimizes the sizing and location of FACTS devices for attaining the minimum average production cost and real power depletions of the system. Especially, the objective includes reserve cost for overestimation, cost of thermal generation of the wind power, direct cost of scheduled wind power and penalty cost for underestimation. The efficiency of the offered method over several popular optimization algorithms has been done, and the comparison over different algorithms establishes proposed KHA algorithm attains the accurate optimal efficiency for all other algorithms. Design/methodology/approach: The proposed FACTS devices-based power system with the integration of wind generators is based on the accurate placement and sizing of FACTS devices for decreasing the actual power loss and total production cost of the power system. Findings: Through the cost function evaluation of the offered SF-KHA, it was noted that the proposed SF-KHA-based power system had secured 13.04% superior to success history-based adaptive differential evolution, 9.09% enhanced than differential evolution, 11.5% better than artificial bee colony algorithm, 15.2% superior to particle swarm optimization and 9.09% improved than flower pollination algorithm. Originality/value: The proposed power system with the accurate placement and sizing of FACTS devices and wind generator using the suggested SF-KHA was effective when compared with the conventional algorithm-based power systems. [ABSTRACT FROM AUTHOR]

Purpose: Parameter identification of photovoltaic (PV) modules plays a vital role in modeling PV systems. This study aims to propose a novel hybrid approach to identify the seven parameters of the two-diode model of PV modules with high accuracy. Design/methodology/approach: The proposed hybrid approach combines an improved particle swarm optimization (IPSO) algorithm with an analytical approach. Three parameters are optimized using IPSO, whereas the other four are analytically determined. To improve the performance of IPSO, three improvements are adopted, that is, evaluating the particles with two evaluation functions, adaptive evolutionary learning and adaptive mutation. Findings: The performance of proposed approach is first verified by comparing with several well-established algorithms for two case studies. Then, the proposed method is applied to extract the seven parameters of CSUN340-72M under different operating conditions. The comprehensively experimental results and comparison with other methods verify the effectiveness and precision of the proposed method. Furthermore, the performance of IPSO is evaluated against that of several popular intelligent algorithms. The results indicate that IPSO obtains the best performance in terms of the accuracy and robustness. Originality/value: An improved hybrid approach for parameter identification of the two-diode model of PV modules is proposed. The proposed approach considers the recombination saturation current of the p–n junction in the depletion region and makes no assumptions or ignores certain parameters, which results in higher precision. The proposed method can be applied to the modeling and simulation for research and development of PV systems. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to focus on the inclusion of the frequency behavior of grounding system effect on surge arrester (SA) model parameters' estimation. Design/methodology/approach: The grounding system impedance and its frequency behavior are the factors that have influence on the SA performance. Up to now, the grounding system impedance effect and the frequency behavior of the soil parameters have not been studied for the estimation of the parameters of the SA frequency-dependent model. In this paper, the grounding system's influence on the SA dynamic model has been simulated for rod- and counterpoise-shaped electrodes. Particle swarm optimization with a grey wolf optimization algorithm has been implemented as an optimization algorithm to adjust the parameters of the SA dynamic model. Findings: The results show that the frequency behavior of the grounding impedance and soil electrical parameters can impress the optimum parameters of the SA frequency-dependent model and should be considered for more reliable results. Also, the results evidence that the proposed optimization method provides more accurate results compared to other optimization methods. Originality/value: To the best of the authors' knowledge, this work is one of the first attempts to investigate the effect of frequency grounding system on SA frequency-dependent model parameters. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to present a novel multi-objective version of the Gorilla Troops optimizer (GTO), based on crowding distance, to achieve the optimal design of a brushless direct current motor. Design/methodology/approach: In the proposed algorithm, the crowding distance technique was integrated into the GTO to perform the leader selection and also for the external archive refinement from extra non-dominated solutions. Furthermore, with a view to improving the diversity of non-dominated solutions in the external archive, mutation operator was used. For constrained problems, an efficient strategy was adopted. The proposed algorithm is referred to as CD-MOGTO. Findings: To validate the effectiveness of the proposed approach, it was initially tested on three constrained multi-objective problems; thereafter, it was applied to optimize the design variables of brushless direct current motor to concurrently fulfill six inequality constraints, maximize efficiency and minimize total mass. Originality/value: The results revealed the high potential of the proposed algorithm over different recognized algorithms in solving constrained multi-objective issues and the brushless direct current motors. [ABSTRACT FROM AUTHOR]

Purpose: In this paper, an improved multiobjective particle swarm optimization (PSO) algorithm is proposed to optimize a three-phase, 12-slot, 19-pole, yokeless axial-field flux-switching permanent magnet (YASA-AFFSPM) motor. Design/methodology/approach: Based on the structural characteristics of the YASA-AFFSPM, a mathematical model is established to calculate the main size of the YASA-AFFSPM motor. The split ratio, stator axial length, sandwiching pole angle, rotor pole angle, PM arc and number of conductors per slot are selected as optimization variables. Also, the efficiency, power factor, cogging torque and average torque are considered as the optimization objectives. The objectives are optimized by combining the improved multiobjective PSO algorithm with electromagnetic calculation. Findings: Based on the proposed algorithm, the investigated motor is optimized. The on-load efficiency, power factor and average torque of the motor performance have increased by 0.87%, 3.13% and 10.39%, respectively. Moreover, the cogging torque and slot fill factor have undergone decreases of 8.57% and 3.34%, respectively. Finally, the effectiveness of the algorithm is verified using experiment results. Originality/value: So far, no comprehensive report has been observed on the optimization of the YASA-AFFSPM motor using evolutionary algorithms and the study of the effect of the motor parameters. Therefore, in this paper, the authors decided to investigate the effect of YASA-AFFSPM motor parameters and improve motor performance with the improved PSO method. [ABSTRACT FROM AUTHOR]

• Using the Hermite Radial Interpolator Base Function (HRBFs) in MQ-RPIM meshless method and enrichment of them. • Optimizing shape parameters used in base functions to define shape functions by the Multi-Objective Particle Swarm Optimization(M-PSO) algorithm. • Employment Voronoi computational geometry technique for the appropriate discretization and distribution of nodal points in the local analysis domain, increasing the accuracy of calculations and preventing the increase of nodal points and cost. • The effect of the number of sentences used in the definition of polynomial base functions based on the Khayyam-Pascal pyramid on the accuracy of calculations and loading. • Comparison of the findings with the results of different numerical methods and introduced OE-MQ-HRPIM as a simple and low-cost alternative method to solve solid mechanics problems with complex shapes or discontinuities geometric and cracks. The RPIM is one of the meshless methods that by enriching and extending Base Functions in the standard form of MQ-RPIM, can be developed and solving the Fracture Mechanics problems. In this research, with using the Hermite-RBFs and optimizing the Shape Parameters in the RBFs by the Multi-Objective PSO algorithm, the Enriched-MQ-RPIM method has been converted to the OE-MQ-HRPIM method. The appropriate distribution of nodal points in the analysis domain has also been done with the Voronoi computational geometry technique. A cracked structure will be used to discretize the analysis domain and calculation of the displacement and stress fields to estimate the 3D-SIFs in Mode-I under Uni-axial Cyclic Loading. The research findings, indicate that the OE-MQ-HRPIM 's answers are closer to the experimental results compared to the MQ-RPIM, OE-MLPG Penalty and E-PIM methods. Although in this method the size of the matrices, the convergence speed and the number of unknown coefficients from n+m+l to 2n+m+l are increased, but the answers are calculated with higher density nodal point in the Sub-Local domains and fewer nodal points in the domain analysis.Also Combining Second-Order sentences in the Khayyam-Pascal pyramid (Ns=7) to defining PBFs in OE-MQ-HRPIM, will lead to more accurate answers. [ABSTRACT FROM AUTHOR]

Purpose: Coronavirus disease (Covid-19) has created uncertainty in all countries around the world, resulting in enormous human suffering and global recession. Because the economic impact of this pandemic is still unknown, it would be intriguing to study the incorporation of the Covid-19 period into stock price prediction. The goal of this study is to use an improved extreme learning machine (ELM), whose parameters are optimized by four meta-heuristics: harmony search (HS), social spider algorithm (SSA), artificial bee colony algorithm (ABCA) and particle swarm optimization (PSO) for stock price prediction. Design/methodology/approach: In this study, the activation functions and hidden layer neurons of the ELM were optimized using four different meta-heuristics. The proposed method is tested in five sectors. Analysis of variance (ANOVA) and Duncan's multiple range test were used to compare the prediction methods. First, ANOVA was applied to the test data for verification and validation of the proposed methods. Duncan's multiple range test was used to identify a suitable method based on the ANOVA results. Findings: The main finding of this study is that the hybrid methodology can improve the prediction accuracy during the pre and post Covid-19 period for stock price prediction. The mean absolute percent error value of each method showed that the prediction errors of the proposed methods were all under 0.13106 in the worst case, which appears to be a remarkable outcome for such a difficult prediction task. Originality/value: The novelty of this study is the use of four hybrid ELM methods to evaluate the automotive, technology, food, construction and energy sectors during the pre and post Covid-19 period. Additionally, an appropriate method was determined for each sector. [ABSTRACT FROM AUTHOR]

This work develops a novel fuzzy adaptive forensic-based investigation algorithm (FAFBI) for optimizing frequency-constrained structural dome design. The Wilcoxon rank sum test of FAFBI and compared algorithms indicates that FAFBI is significantly better at finding optimal value than artificial bee colony, cultural algorithm, differential evolution, firefly algorithm, forensic-based investigation algorithm, genetic algorithm, harmony search, particle swarm optimization, simulated annealing, symbiotic organisms search, and teaching–learning-based optimization. Next, the proposed FAFBI algorithm is used to optimize the weight of steel dome structures under frequency constraints. Design cases involve 52-bar, 120-bar, 600-bar, 1180-bar, and 1410-bar​ domes. The results of optimizing reveal that the FAFBI can identify lighter domes while meeting the required frequency constraints than can the methods in the literature. Accordingly, the proposed algorithm is an effective tool for solving mathematical optimization problems and for use in the design phase of structural construction. The source code is currently available from https://www.mathworks.com/matlabcentral/fileexchange/126435-fuzzy-adaptive-forensic-based-investigation-algorithm. • A fuzzy adaptive forensic-based investigation (FAFBI) algorithm is developed for solving optimization problems. • The FAFBI algorithm is compared with well-known optimizers using benchmark functions. • Five frequency-constrained structural problems were efficiently solved using FAFBI. • The analytical results demonstrate the robustness of FAFBI in finding the optimal solution. [ABSTRACT FROM AUTHOR]

The gravitational search algorithm (GSA) is one of the most promising algorithm in the physics-based metaheuristics category. However, GSA suffers from premature convergence due to rapid reduction in diversity, whereas a chaotic gravitational search algorithm (CGSA) can degrade the convergence speed and exploitation power. To address these issues, the current study proposes an algorithm that enhances the exploration capability of GSA using a disruption strategy with chaotic dynamics. If no significant change is observed in diversity values during the initial stages of the search process, disruption is performed using a sigmoid function. Then, the search process executes gradual exploitation using a sigmoid function without chaotic dynamics. The proposed algorithm is tested with GSA, CGSA, and PSO (particle swarm optimization) on 28 benchmark functions. It is observed that the algorithm outperforms GSA and PSO in 19 cases and CGSA in 20 cases. Diversity analysis shows that the algorithm generates superior exploration versus exploitation percentage with improved mean diversity values. To determine its robustness, the algorithm is applied to four unconstrained engineering problems. The results suggest that the algorithm can solve practical engineering problems in a reasonable number of iterations. [ABSTRACT FROM AUTHOR]

We propose a hybrid variant of the level-based learning swarm optimizer (LLSO) for solving large-scale portfolio optimization problems. This solver fills the gap due to the inadequacy of the particle swarm optimization algorithm for high-dimensional instances. We aim to extend the classical mean-variance formulation by maximizing a modified version of the Sharpe ratio subject to cardinality, box, and budget constraints. The algorithm involves a projection operator to deal with these three constraints simultaneously. Further, we implicitly control transaction costs thanks to a rebalancing constraint handled by a suitable exact penalty function. In addition, we develop an ad hoc mutation operator to modify candidate exemplars in the highest level of the swarm. The experimental results, using three large-scale data sets, show that including this procedure improves the accuracy of the solutions. Then, a comparison with other variants of the LLSO algorithm and two state-of-the-art swarm optimizers points out the outstanding performance of the proposed solver in terms of exploration capabilities and solution quality. Finally, we assess the profitability of the portfolio allocation strategy in the last five years using an investable pool of 1119 constituents from the MSCI World Index. • A new LLSO algorithm is developed for large-scale portfolio optimization problems. • A novel hybrid constraint-handling technique and mutation operator are proposed. • A modified Sharpe ratio is taken as objective function. • A comparison with other state-of-the-art swarm optimizers is developed. • Investment profitability is assessed on a global equity market. [ABSTRACT FROM AUTHOR]

Purpose: Monitoring and diagnosis of fault cases for squirrel cage induction motors can be implemented using the multiple coupled circuit model. However, the identification of the associated model parameters for a specific machine is problematic. Up to now, the main options are measurement and test procedures or the use of finite element method analyses. However, these approaches are very costly and not suitable for use in an industrial application. The purpose of this paper is a practical parameter identification based on optimization methods and a comparison of different algorithms for this task. Design/methodology/approach: Population-based metaheuristics are used to determine the parameters for the multiple coupled circuit model. For this purpose, a search space for the required parameters is defined without an elaborate analytical approach. Subsequently, a genetic algorithm, the differential evolution algorithm and particle swarm optimization are tested and compared. The algorithms use the weighted mean squared error (MSE) between the real measured data of stator currents as well as speed and the simulation results of the model as a fitness function. Findings: The results of the parameter identification show that the applied methodology generally works and all three optimization algorithms fulfill the task. The differential evolution algorithm performs best, with a weighted MSE of 2.62, the lowest error after 1,000 simulations. In addition, this algorithm achieves the lowest overall error of all algorithms after only 740 simulations. The determined parameters do not completely match the parameters of the real machine, but still result in a very good reproduction of the dynamic behavior of the induction motor with squirrel cage. Originality/value: The value of the presented method lies in the application of condition-based maintenance of electric drives in the industry, which is performed based on the multiple coupled circuit model. With a parameterized model, various healthy as well as faulty states can be calculated and thus, in the future, monitoring and diagnosis of faults of the respective motor can be performed. Essential for this, however, are the parameters adapted to the respective machine. With the described method, an automated parameter identification can be realized without great effort as a basis for an intelligent and condition-oriented maintenance. [ABSTRACT FROM AUTHOR]

The paper "Improved Methods for Stator End Winding Leakage Inductance Calculation" presents improved methods for calculating the stator end winding leakage inductance of an IM. The authors used a DC hysteresis measurement and an optimization process to parameterize the hysteresis models, and then the calculated current waveforms of the no-load transformer have been compared with the measured no-load current waveforms to validate the model. The transformer topology models used in the study are: The inductance-reluctance models; and The capacitance-permeance models. The inductance-reluctance model represents the magnetic circuit of the transformer and its stray flux paths as inductances in an electric equivalent circuit, allowing the magnetic and surrounding electrical circuit to be modelled in the electrical domain. [Extracted from the article]

In this study, an application of green preservation technology to obtain optimal pricing and replenishment policies is developed. The decreasing value in waste is considered along with retailer dynamic promotional and green preservation technology investments under a price-promotion model with ramp-type demand. To maximize total profits, the study aims to clean the environment while simultaneously optimizing selling price, replenishment schedule, order quantity, green preservation technology investment, and dynamic investment rate. Pontryagin's maximum principle is adopted to obtain the optimal dynamic investment rate for waste reduction. Additionally, simulated annealing, particle swarm optimization, and BAT algorithms are individually applied to obtain an optimal decision for waste reduction via green preservation technology. Results showed that the investment rate in promotion is significantly affected by price changes and that the investment in green preservation technology is affected by the nature of the product. Extensive computational experiments are performed to validate the proposed model. Under Ramp-type demand, price differentiation is more rewarding than the uniform pricing of the retailer. Results demonstrate the significant role of dynamic investment strategy. It has invested more based on the low-price sensitivity in the second period, and the optimal investment follows a reverse trend. [ABSTRACT FROM AUTHOR]

This article proposes a finite population Markovian queueing model with discouragement and vacationing server to examine the performance of the fault-tolerant system in which admission of failed machines for repair jobs is controlled according to F-policy. When the number of broken-down machines in such systems reaches to its maximum capacity, it does not allow the failed machines to enter in the system until the counts of broken-down machines drop to a pre-specified threshold level 'F'. The feature of vacationing server along with set up, reboot and recovery are incorporated to deal with real-time scenarios of fault-tolerant systems. By considering the birth–death process, the governing equations are constructed using appropriate transition rates of the concerned system. The steady-state queue size distribution and various system metrics are evaluated using a recursive approach. To illustrate the usage of analytical findings, sensitivity analysis of the system metrics with regard to specific parameters is presented. The particle swarm optimization and harmony search optimization techniques are employed to determine the optimal decision parameters and optimum total cost. [ABSTRACT FROM AUTHOR]

Large-scale optimization problems (LSOPs) become increasingly ubiquitous but complicated in real-world scenarios. Confronted with such sophisticated optimization problems, most existing optimizers dramatically lose their effectiveness. To tackle this type of problems effectively, we propose a heterogeneous cognitive learning particle swarm optimizer (HCLPSO). Unlike most existing particle swarm optimizers (PSOs), HCLPSO partitions particles in the current swarm into two categories, namely superior particles (SP) and inferior particles (IP), based on their fitness, and then treats the two categories of particles differently. For inferior particles, this paper devises a random elite cognitive learning (RECL) strategy to update each one with a random superior particle chosen from SP. For superior particles, this paper designs a stochastic dominant cognitive learning (SDCL) strategy to evolve each one by randomly selecting one guiding exemplar from SP and then updating it only when the selected exemplar is better. With the collaboration between these two learning mechanisms, HCLPSO expectedly evolves particles effectively to explore the search space and exploit the found optimal zones appropriately to find optimal solutions to LSOPs. Furthermore, to help HCLPSO traverse the vast search space with promising compromise between intensification and diversification, this paper devises a dynamic swarm partition scheme to dynamically separate particles into the two categories. With this dynamic strategy, HCLPSO gradually switches from exploring the search space to exploiting the found optimal zones intensively. Experiments are executed on the publicly acknowledged CEC2010 and CEC2013 LSOP benchmark suites to compare HCLPSO with several state-of-the-art approaches. Experimental results reveal that HCLPSO is effective to tackle LSOPs, and attains considerably competitive or even far better optimization performance than the compared state-of-the-art large-scale methods. Furthermore, the effectiveness of each component in HCLPSO and the good scalability of HCLPSO are also experimentally verified. [ABSTRACT FROM AUTHOR]

In this work, a sequential switching quadratic particle swarm optimization (SSQPSO) scheme is investigated, where the velocity update mechanism is improved to enhance the convergence performance. Considering the sequential characteristics (related to evolution factors) of the evolution process, a Markov chain with special probability transition matrix is employed to characterize the switching of evolution state. With the help of the mean distance, the concept of population density is first put forward in the dynamic search region enclosed by all particles. Then, taking into account the change of the population density in different generations, two quadratic acceleration terms are introduced into the velocity update model based on the Hadamard product, where four evolution-state-dependent acceleration coefficients are also adopted. The positivity or negativity of the quadratic acceleration terms is retained by resorting to the matrix sign functions. Several widely utilized benchmark functions (including two unimodal and multimodal functions) are employed to evaluate the search capability of the studied SSQPSO scheme. The experimental consequences illustrate that the performance of the developed SSQPSO scheme is superior to that of some popular particle swarm optimization (PSO) schemes. To further demonstrate the effectiveness in practical engineering, the addressed SSQPSO scheme is successfully applied to achieve the fast parameter tuning of the proportional-integral-derivative controller in a spring-mass-damper system. [ABSTRACT FROM AUTHOR]

Optimization of the fiber angle in the laminated composite material to achieve the highest strength or stability has great importance in structural design. In the present study, we aim to utilize genetic algorithm (GA) and particle swarm optimization (PSO) together to find the best angle of composite layers in a multilayer cylindrical shell structure. To this aim, a detailed formulation of the composite cylindrical structure is presented using nonlocal strain gradient theory (NSGT), zigzag theory, and Hamilton's principle for a shear deformable shell displacement field. The modal equations of motion are further solved using the general differential quadrature element method (GDQEM). Having the route of obtaining the resonance frequency of the cylindrical composite structure, GA-PSO is engaged to obtain the optimal angle of the laminate composite to achieve the highest frequency. It is revealed that angle θ = 37.5° provides the highest frequency for a single-layer cylindrical shell. Using this angle, stacking sequences for 3, 5, and 7 layers' composite are optimized to have the highest frequency. In the end, a detailed parametric study is presented using optimum condition shell composite. The results show that after 30 composite layers, changing the number of layers seems to be not effective on the resonance frequency of the cylinder for both simply supported and clamped boundary conditions. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a well-known optimization method used for solving various optimization problems. However, PSO suffers from premature convergence and is ineffective in balancing exploration and exploitation when solving complex optimization problems. To overcome these drawbacks of PSO, a multi-sample learning particle swarm optimization with adaptive crossover operation (MLPSO) is proposed. In MLPSO, two novel strategies, multi-sample selecting strategy (MSS) and adaptive sample crossover strategy (ASC), are used to select proper learning samples for the whole population. Firstly, in MSS, two sample pools, namely elite pool and improver pool, are used to save elites and improvers. Elites are the particles with preferable fitness, while improvers denote the particles whose fitness have been improved largely in recent consecutive generations. In each generation, two particles are randomly selected from the two sample pools respectively to breed a learning sample through crossover operation for the whole population. Therefore, the generated learning sample by MSS strategy contains more diversity information. Secondly, in ASC, various crossover operations are conducted for breeding a learning sample according to the evolutionary states. Therefore, the ASC strategy proposed in this paper can realize a better trade-off between exploration and exploitation. Finally, the performance of MLPSO is evaluated using CEC2013, CEC2017 test suites and three engineering optimization problems. Experimental results show that MLPSO outperforms compared seven competitive PSO variants and 19 meta-heuristics algorithms in most functions. • A novel multi-sample learning strategy is developed. • The newly learning sample contains both information of fitness and improvement. • Different crossover operations are used according to the evolutionary states. • The proposed method is suitable for solving complex optimization problems. [ABSTRACT FROM AUTHOR]

Since the exploration of multiple solution sets will lead to the deterioration of convergence in multi-objective particle swarm optimization, the motion of the particles is severely disturbed by the under-convergence solutions in multi-modal multi-objective optimization problems (MMOPs). To solve this problem, a multi-modal multi-objective particle swarm optimization with self-adjusting strategy (MMOPSOSS) is proposed to promote the complete convergence of multiple solution sets through the self-adjusting of parameters and population size. First, a multi-swarm optimization framework is designed to obtain diverse convergence directions. Second, a self-adjusting local search mechanism is introduced to improve the search performance of sub-swarms in the potential regions according to the feedback information detected by diversity entropy under this framework. Third, a sub-swarm-balancing strategy is developed to balance the degree of convergence among different regions by adjusting the size of the sub-swarms. Finally, MMOPSOSS is compared with several multi-modal multi-objective optimization algorithms in benchmark experiments and engineering simulation experiments. The results demonstrate that MMOPSOSS has a positive effect on the convergence of multiple solution sets for MMOPs. [ABSTRACT FROM AUTHOR]

• The finish time and total costs are extracted as the optimization objectives based on problem characteristics. • The dynamic task disassembling mechanism (DTDM) and four technician scheduling modes are novelly proposed. • A flexible time is introduced into DTDM to provide decision-makers with a larger pool of choices to meet specific needs. • A new solution method suitable for both AMTS model and ATMS-DTDM model is designed based on PSO and MOPSO algorithms. This paper focuses on the aviation maintenance technician scheduling (AMTS) problem and formulates AMTS and AMTS-DTDM model with a practical dynamic task disassembly mechanism (DTDM) and arrange maintenance technicians across shifts in short-term maintenance situations (less than 24–48 h). In DTDM, four technician scheduling modes are devised to flexibly disassemble the overtime work or reassigned it to other maintenance technicians according to work efficiency and progress, which could shorten the maintenance time and save the total cost. Moreover, a flexible time interval is designed to adjust the boundary of task disassembly. To further study the effectiveness of DTDM in reducing maintenance time and total costs, this paper divides both the AMTS and AMTS-DTDM models into three sub-models (i.e., two single-objective models and one multi-objective model), respectively. After that, we design solution methods as well as the encoding schemes compatible with both the AMTS model and the AMTS-DTDM model for different problem scales. Finally, to verify the effectiveness of DTDM, four groups of experiments are set up and particle swarm optimization (PSO) and multi-objective particle swarm optimization (MOPSO) are applied to compare AMTS and AMTS-DTDM sub-models. The experimental results show that the AMTS-DTDM model can effectively shorten the maintenance time and reduce the total costs for different scaled problems. Furthermore, the flexible time interval can facilitate more options for airlines to adjust the two above objectives to a small extent. The increase in flexible time may weaken the advantages of the AMTS-DTDM model while improving the rationality of the ultimate technician scheduling scheme. [ABSTRACT FROM AUTHOR]

• PSO-ELPM makes some modifications over PSO to enhance its search capabilities. • This PSO variant uses elite pbests and the gbest as the exemplars of all particles. • The self-cognition coefficients are defined proportional to the quality of elites. • An exponential mutation operator is proposed to escape from local optimums. • This operator determines the mutation probability and the number of mutated genes. Particle Swarm Optimization (PSO) has been widely used to solve optimization problems. Although a large number of PSO variants have been proposed so far, they suffer from the shortcomings of the original PSO. This paper proposes PSO with Elite Learning, enhanced Parameter updating, and exponential Mutation operator (PSO-ELPM) to balance the exploration and exploitation capabilities of PSO. In this algorithm, the best-performing particles in the population, known as the elites, are used as exemplars to guide the optimization process. The elitism scheme helps to discover valuable knowledge about the solution space. The adopted elites are also used to compute self-cognition coefficients of particles. Additionally, the inverse of the cube root function is applied to ensure a smooth distribution of weight among the elites. The final improvement is to apply an exponential mutation operator, which determines the mutation probability per particle based on the current iteration and its history. The comparisons among PSO-ELPM and 10 state-of-the-art PSO variants on the CEC 2017 benchmark functions reveal that the proposed algorithm yields higher accuracy with acceptable time complexity. According to the Wilcoxon rank sum test, PSO-ELPM at-least outperforms the competitive algorithms on six and seven functions in 30 and 50-dimensional problems, respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • A framework is proposed for population-based optimization algorithms' convergence. • DIRECT's partition and population evolutions are repeated in this framework. • The global convergence of the framework is proved. • Framework is applied successfully on three population-based optimization algorithms. Population-based optimization algorithms, such as genetic algorithm and particle swarm optimization, have become a class of important algorithms for solving global optimization problems. However, there is an issue that the global convergence is often absent for most of them. This paper proposes a partition-based convergence framework for population-based optimization algorithms to solve this troubling problem. In this framework, regular partitions and evolutions of populations are implemented alternatively. Specifically, the initial population is generated from a regular partition on the search space; after several generations of evolution of the population, the evolution result is returned to join in the regular partition again, and a new population is generated. Repeat such progress until some stop condition is satisfied. Global convergence is guaranteed for the framework. Then this convergence framework is applied to particle swarm optimization, differential evolution, and genetic algorithm. The modified algorithms are globally convergent and perform better than the original version. [ABSTRACT FROM AUTHOR]

• This work proposes a hybrid water quality prediction method called SVABEG. • It uses an SG filter and VMD to reduce noise, and handle nonlinear features. • It combines bidirectional LSTM, encoder-decoder, and an attention mechanism. • It adopts a hybrid algorithm called GSPSO to optimize its hyperparameters. • Real-life datasets are used to demonstrate its superiority over typical algorithms. Accurate and real-time prediction of water quality not only helps to assess the environmental quality of water, but also effectively prevents and controls water quality emergencies. In recent years, neural networks represented by Bidirectional Long Short-Term Memory (BiLSTM) and Encoder-Decoder (ED) frameworks have been shown to be suitable for prediction of time series data. However, traditional statistical methods cannot capture nonlinear characteristics of the water quality, and deep learning models often suffer from gradient disappearance and gradient explosion problems. This work proposes a hybrid water quality prediction method called SVABEG, which combines a S avitzky-Golay (SG) filter, V ariational Mode Decomposition (VMD), an A ttention mechanism, B iLSTM, an E D structure, and a hybrid algorithm called G enetic Simulated annealing-based Particle Swarm Optimization (GSPSO). SVABEG first adopts the SG filter and VMD to remove noise and deal with nonlinear features in the original time series, respectively. Then, SVABEG combines BiLSTM, the ED structure and the attention mechanism to capture bi-directional long-term correlations, realize dimensionality reduction and extract key information, respectively. Furthermore, SVABEG adopts GSPSO to optimize its hyperparameters. Experimental results with real-life datasets demonstrate that the proposed SVABEG outperforms current state-of-the-art algorithms in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

In a multi-objective particle swarm optimization (MOPSO), the selection strategies of the personal best solution (pBest) for a single particle and the global best solution (gBest) for the whole swarm are two key challenges to balance the convergence and diversity of an algorithm during its iterative process. Many selection strategies in the existing literatures were emphasized on the individual characteristics of the separate particles rather than the collective features of the whole swarm. In this paper, a novel gBest selection strategy based on a new defined virtual generational distance indicator, which is calculated from a virtual Pareto front fabricated according to the geometry of a given elite archive, is proposed for selecting the most appropriate Pareto optimal solution as the gBest with respect to the comprehensive convergence and diversity contribution to improve the search effectiveness and efficiency of a MOPSO. Besides, an adaptive pBest selection strategy based on the evolutionary state in different iterations is designed for identifying the more suitable pBest from its personal archive for each particle to strengthen the exploitation or exploration ability adaptively. The experimental results show that a MOPSO with the new gBest and pBest selection strategies outperforms ten state-of-the-art competitive algorithms on DTLZ, F, WFG and ZDT series of benchmark problems. In addition, a case study on the site selection for mobile earthquake monitoring stations is also illustrated the effectiveness of the proposed algorithm in the real-world application. [ABSTRACT FROM AUTHOR]

In the digital age, rumor spreading is becoming more widespread and faster than ever before, and results in the more social panic and instability. Because of this, it is crucial to implement effective control strategies to prevent the continued spread of rumors, and avoid all kinds of unnecessary harm caused by rumors. In this paper, a stochastic rumor spreading model incorporating time delay within the framework of the event-triggered impulsive control (ETIC) strategies are presented. To begin with, the stability problem of this model is discussed and proved. Besides, the optimal ETIC strategies are explored by the particle swarm optimization (PSO) algorithm. Furthermore, some numerical simulations are performed to illustrate the optimal ETIC strategies of the given model. In addition, a real case is used to prove the validity of given model. Finally, the following conclusions are drawn that the stochastic model is feasible and consistent with actual rumor propagation trends, and ETIC strategies can help control rumors effectively. Meanwhile, different ETIC strategies should be used according to the different situations of rumor spreading. For instance, control strategies need to be more frequent and robust when transmission rates are higher or time delay are shorter. [ABSTRACT FROM AUTHOR]

An Impact Angle, Speed and Acceleration Control Guidance (IASAG) law against the stationary target is proposed, which is critical for the effectiveness of the air-to-surface guided weapons. It is hard to address multiple terminal constraints problem for unpowered missile, especially including terminal speed constraint, which is uncontrollable state. Based on Line-of-Sight (LOS) angle, a fourth-order polynomial function is designed to make the number of coefficients of the function equal to number of boundary conditions. Through analytic calculation and transformation, the relation between the specified boundary conditions and the coefficients are established. The coefficient equations are reduced to a univariate nonlinear equation whose solution is determined by terminal speed constraint. Based on the characteristic of the nonlinear equation, we propose a Particle Swarm Optimization(PSO) method to find the coefficient that satisfies terminal speed constraint. According to Lyapunov stability theory, an asymptotically stable trajectory tracking controller is designed to track the reference leading angle with respect to range-to-go to guarantee the impact angle, speed and acceleration constraints. The effectiveness of the proposed guidance law is verified through numerical simulations. [ABSTRACT FROM AUTHOR]

This paper appends three proposed heuristic algorithms, viz. Initialization Algorithm, Fraction Repair Algorithm and Negative Repair Algorithm, to Particle Swarm Optimization (PSO) and extends its application to Pure Integer Linear Solid Transportation Problem (STP). The chief contribution of the paper is that the proposed Hybrid Particle Swarm Optimization (HPSO) algorithm discretizes the continuous search space of PSO so that it can operate in the discrete solution space of STP with any of the valid parameter settings. The Initialization Algorithm is developed to generate the sufficient number of random solutions for initiating HPSO with a diverse population of given size. Whereas Fraction Repair and Negative Repair Algorithms are developed to ensure that each successive population obtained with HPSO remains in the solution space of STP. The validity of HPSO is examined and illustrated by solving a numerical problem with certain parameter settings. Further, the HPSO demonstrates its ability to obtain an optimal or near optimal solution along with the alternate optimal solution, if it exists. It searches for the solutions even without adhering to the rigid conditions of traditional methods that every solution of STP must have a fixed number of non-zero decision variables. Moreover, the performance of HPSO is tested with three different parameter settings and compared amongst each other to analyze their significance in solving the problem. The performance of HPSO and the variants of parameters are statistically validated through an extensive experimental design. [ABSTRACT FROM AUTHOR]

Multiple-swarm approach is a quite successful evolutionary computation framework for multi-objective particle swarm optimization algorithm (MOPSO) to solve multi-objective optimization problems (MOPs). However, the main challenge of using this framework lies in the lack of leader selection, resulting in the optimal solutions being distributed loosely, as well as far away from the true Pareto-optimal front. To overcome this problem, a multi-swarm MOPSO with an adaptive multiple selection strategy (MOPSO-AMS) is investigated in this paper. This proposed MOPSO-AMS is able to guide each swarm with a suitable leader to improve the evolutionary performance. The novelties and advantages of MOPSO-AMS include the following three aspects. First, a hierarchical evolutionary state detection mechanism, based on the distribution and dominance information of non-dominated solutions, is designed to obtain the evolutionary state of current iteration. Then, the requirements of evolutionary process can be detected. Second, an adaptive multiple selection strategy, using the evolutionary state information and spatial features of candidate solutions, is developed to select leaders of sub-swarms in multiple evolutionary states. Then, suitable leaders can be selected to keep the balance between convergence and diversity. Third, an adaptive parameter adjustment mechanism, based on the dominance relationship of each particle, is introduced to further improve the evolutionary performance of MOPSO-AMS. Finally, numerical simulations and a practical application are used to validate the analytical results and demonstrate the significant improvement of MOPSO-AMS. [ABSTRACT FROM AUTHOR]

• New group theory based PSO learning for optimal parameters of fuzzy inference system. • Concept and an overview of the new algorithm for determining the filter coefficients. • Three-process analytical design for filter coefficient extraction to design person independent wavelet. • The energy compaction ratio is lower than the db4 coefficients. This paper presents a new method for designing matched digital filters with discrete valued coefficients. The fuzzy particle swarm optimization vector quantization (FPSOVQ) has been applied to obtain the optimum codebook in design of matched wavelet function. Abelian group has been used to extract the similarity present in the input voiced signal. Fuzzy particle swarm optimization (FPSO) process is used to find approximate ideal vector quantization (VQ) codebook to be carried out for compression of data. FPSOVQ scheme utilises features of fuzzy inference method (FIM) and expert particle swarm optimization (PSO). The generated codebook consists of set of highly ideal features, which are considered as the filter coefficients. These coefficients are used in the designing of the filter. All of the phonemes in the American English language were included in the 30 sentences that were chosen from the IEEE database. The sentences were originally down-sampled from 25 kHz to 8 kHz. The magnitude responses of each filter have been drawn, which indicates the characteristics of the filter. A comparison has been provided using energy compaction ratio as a parameter to judge the performance of matched designed filter with db4 filter. Experimental results show the advantage of the developed algorithm as the average value of energy compaction ratio for sampled voice signals is 2.8046 times lower for matched designed filter. [ABSTRACT FROM AUTHOR]

• The IGD indicator is employed as selection criteria to face the challenge of the large ratio of non-dominated solutions that exist in MaPs. • A multi-agent system is used to model the intelligent behavior of multi-swarms. • A new strategy of knowledge sharing based on automated negotiation is proposed. • Empirical studies demonstrate the effectiveness of the proposed algorithm in solving MaOPs. Most evolutionary algorithms, including particle swarm optimization (PSO), use Pareto dominance as a major selection criterion and face significant challenges when dealing with many-objective problems. To tackle this issue, this paper proposes a novel algorithm, termed: Many- Objective PSO with Cooperative Agents (MaOPSO-CA). This exploits an Inverted Generational Distance (IGD) indicator in two innovative ways: firstly, as a leader selection method to select the preferable solution in terms of convergence and diversity, and, secondly, as an archiving method to decide which non-dominated solutions are kept in a bounded archive. The proposed strategy significantly promotes selection pressure toward the Pareto front. The results indicate that the IGD-based selection circumvents the issue of a large ratio of non-dominated solutions that exist in MaOPs. Moreover, a multi-swarm is investigated and modeled as a Multi-Agent System (MAS), so that knowledge sharing among different sub-swarms is easily improved through automated negotiation. The effectiveness of our proposed algorithm is validated with numerous experimental studies in solving 110 benchmark testing instances with up to twenty objectives. Experimental results demonstrate the effectiveness of the new algorithm compared to recent state-of-the-art methods. Finally, the application of MaOPSO-CA to a challenging, real-world water resource-management problem is shown to produce very encouraging results, demonstrating its potential as a benchmark resource for the research community. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a popular stochastic algorithm for solving nonlinear optimization problems. It has been used in various industrial applications because of its few parameters and ease of implementation. Various optimal problems must be frequently formed as nonlinear constrained optimization (NCO) problems because of global resource limitations. These problem-specific nonlinear constraints can result in many infeasible regions in the search space. Current PSO-based approaches are inherently designed for unconstrained optimization problems and cannot directly handle the constraint issue; therefore, they add the constraint handling technique (CHT) to account for NCO problems. This study proposes a double-track schema inside PSO called double-track PSO (DTPSO) to address the NCO problem. When an infeasible region blocks a particle, the DTPSO creates a copy of the particle. The original and copied particles utilize different search strategies to move to feasible and infeasible regions. The results indicate that the proposed DTPSO outperforms the seven referenced PSO-based methods in all 33 NCO problems and five recent strong methods in 19 mechanical design problems belonging to the IEEE CEC 2020 competition on real-world single-objective constrained optimization problems. Furthermore, the experiment results demonstrate the quality of the solution and the effective computation of the proposed DTPSO. [ABSTRACT FROM AUTHOR]

In this research, we used an easy and efficient computation procedure for deriving performance measures of (S-1, S) inventory problems with M/M/c replenishment under a retrial demand scenario. We prove some properties of the studied problem and also under back-ordered demands for comparison. We apply our method to a capacity design problem and a location-inventory design problem. For both problems, we used our method combined with direct search and genetic algorithm (GA) for optimization and compare with matrix geometric method (MGM) based evaluation and other optimization methods, including enumeration and FICO Xpress Mosel. Numerical examples show our method is about ten times more efficient than MGM-based evaluation and outperforms the other optimization methods. To find if the solution can be improved, we compare GA with other meta-heuristics, including simulated annealing, particle swarm optimization, and ant colony optimization. Finally, a sensitivity study for the location-inventory design problem is investigated. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced hybrid chameleon swarm algorithm (CSA) is proposed and applied to the degree reduction problem of disk Wang–Ball (DWB) curve. CSA is a novel population-based algorithm inspired by the hunting behavior of chameleons, its simplicity and easy implementation make it applied to different fields. However, it suffers from premature convergence and easy to fall into local optimum, especially in the face of complex optimization problems. Therefore, this paper proposes an enhanced hybrid CSA (CCECSA, for short). Compared with the classic CSA, the proposed CCECSA mainly introduces three improvements: (1) The crisscross optimization algorithm is mixed to avoid premature convergence, in which the horizontal and vertical crossover can generate moderation solutions to increase the diversity of the population. (2) Elite guidance mechanism is introduced to speed up the convergence. (3) Competitive substitution mechanism is added to replace the worst individual, and an interference strategy is set to prevent the algorithm from falling into a local optimum. The efficiency and robustness of the proposed CCECSA are demonstrated by the comparison results with some advanced meta-heuristic algorithms on CEC2014, CEC2017, and 4 engineering design examples. In addition, for the degree reduction problem of DWB curves, the multi-degree reduction optimization models of its center curve and radius function are established respectively. At the same time, the optimal center curve and radius function of the approximating DWB curves of lower degree are obtained by the proposed CCECSA. The experimental results show that the proposed CCECSA achieves the optimal solution with better convergence and robustness. The source code of CCECSA is publicly available in the supplementary material related to this article. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate optimal control the non-linear fractional-order chaotic system of cancer cells by use of particle swarm optimization and genetic algorithms. The chaotic behavior of cancer cell growth and the tumor growth model were expressed as a system of differential equations. Then, optimal control of cancer cells using drugs was presented. Particle swarm optimization method and genetic algorithms were used to solve the problem of optimal control of cancer cell growth. The results of the control applied to the model can control the cancer cell growth. The application of control results in decreasing the number of cancer cells to zero. When the controller is applied from the beginning, the Results of genetic algorithm method are excellent. All the results obtained for the particle swarm optimization method show that this method has also been very successful and have results very close to the genetic algorithm method. [ABSTRACT FROM AUTHOR]

Purpose: Due to the non-linear nature of the hysteresis behavior, the accurate identification of the parameters of the Bouc–Wen hysteresis model is still a challenging problem. The purpose of this paper is to explore the potential of a heuristic improved whale optimization algorithm (IWOA) to accurately identify the model parameters, which has never been applied to the field of piezoelectric hysteresis identification. Design/methodology/approach: Based on the analysis of the Bouc–Wen model structure and WOA optimization process, an approach that can fully exploit the potential of WOA is proposed. In this work, the position updating formula is improved by introducing non-linear weights, and the convergence factor formula is modified. And thus, the iteration speed, accuracy and stability of the classical WOA can be improved. Findings: The experimental results show that the model output is in good agreement with the response of the real piezoelectric platform. Compared with the standard WOA and particle swarm optimization algorithms, the search performance of the proposed IWOA is better than those two competitors in terms of convergence speed and identification accuracy. Originality/value: An IWOA is proposed according to the properties of the Bouc–Wen model and piezoelectric hysteresis. It has been approved that the algorithm has a good prospect in the identification of piezoelectric hysteresis systems. Furthermore, this method is easy to implement and is a good candidate algorithm to identify Bouc–Wen model parameters. [ABSTRACT FROM AUTHOR]

Grey Wolf Optimizer (GWO) is a swarm intelligent optimization algorithm that simulates the leadership and social behavior of grey wolves to prey. GWO is extensive used in various fields since it has the advantage of being simple and easy to implement. However, when solving complex optimization problems, GWO has insufficient population diversity and unbalanced exploration and exploitation. To overcome these shortcomings of GWO, a multi-swarm improved grey wolf optimizer (MIGWO) is proposed. In MIGWO, firstly, chaotic grouping mechanism is utilized to improve population diversity and dynamic regrouping mechanism to further improve population diversity and balance exploration and exploitation. Secondly, double adaptive weights and dimension learning are utilized to ameliorate the hunting behavior of grey wolves, which can improve search performance. The MIGWO is verified on 53 test problems. The test results show that it is better to other metaheuristic algorithms and GWO variants in solving the optimal solution and convergence accuracy. In addition, MIGWO is utilized to solve 4 engineering design problems. [ABSTRACT FROM AUTHOR]

A time minimization transportation problem deals with the resource efficiency to minimize time taken by the transport systems to deliver the commodity from sources to destinations. In this paper, a two-level time minimization transportation problem has been considered that categorizes the source–destination links into Level-I and Level-II with respect to the higher and lower level priority. The optimal delivery schedule of Level-I is followed up with the same for the Level-II cells. The paper proposes a solution procedure consisting of new algorithms that have been hybridized within the Particle Swarm Optimization to solve the problem making efficient use of resources. The solution procedure provides a methodical approach to the transport enterprises.This procedure does away with the rigid constraints, such as the location and number of non-zero allocations, required to be met by the traditional techniques of solving the transportation problem. The procedure generates pairs of Level-I and Level-II times at each iteration and the best pair(s) amongst these is/are marked out as the optimal solution of the problem. The solution procedure is explained through a numerical illustration. [ABSTRACT FROM AUTHOR]