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In this paper, a comprehensive study is presented whereby genetic algorithm optimization (GAO) is used to find the best arrangement of three‐phase high‐voltage transmission line (TL) conductors, that minimizes emitted electric and magnetic fields (EMFs), maximizes power capacity, and avoids corona. Moreover, a financial cost study is conducted in order to show the feasibility of implementing such rearrangement, where construction costs are compared with the impact on a populated area in close proximity to the line. To facilitate applying the procedure of this paper, a detailed description of each step is provided. Both single circuit as well as double circuit transmission lines are considered, and bundled conductors are analyzed. To demonstrate the validity and usefulness of the mathematical formulation and MATLAB computer codes, computed results are compared with measured data where good agreement is observed. To show that using GAO for solving high voltage line problems is an alternative to other robust optimization techniques such as particle swarm optimization (PSO), GAO solutions are compared with PSO solutions. The GAO results are also compared with the unoptimized results, where significant improvements can be seen. Since according to this algorithm, line conductors move freely throughout the solution space in an attempt to find the best solution, it is important to restrict this movement in order to adhere to the separation limits of the transmission line international standards IEC‐71. [ABSTRACT FROM AUTHOR]

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

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

Nowadays, all the world does its best to develop the power generation systems that depend on nature in order to reduce the dependence on fuel. Photovoltaic (PV) systems are considered one of the most important renewable energy resources. Scientific research has gained a high interest, especially in PV cell modeling and parameter estimation. The estimation of optimum parameters for the PV model has been considered the main target of the paper optimization problem. Equilibrium optimization (EO) algorithm is considered one of optimization algorithms inspired from nature physical phenomena. EO algorithm has been inspired from the nature physical process of controlling mass balance through specific volume until reaching equilibrium state. In this paper, an EO algorithm has been proposed and applied to prepare a mathematical model for photovoltaic solar cell. The challenge in this optimization problem is the nonlinearity in PV solar cell characteristic. The EO algorithm has been evaluated through the following items. EO has been applied to estimate the parameters of different PV models such as single, double, and triple PV models, which have different complexity, applying the previous item for real PV application. The obtained results have been compared though different functions such as root mean square value and absolute mean error. In all cases, EO obtained results have been compared with the more recent optimization algorithms such as particle swarm optimization (PSO), teaching learn based optimization (TLBO), and Harries Hawk optimization (HHO). From the all obtained results, EO algorithm gives more accurate PV models in comparison with other optimization algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a 30 dB broadband microstrip directional coupler is designed by using a discrete coding type coupling structure with a co‐simulation technique. Combined with the proposed hybrid binary particle swarm genetic optimization algorithm (HBPSGOA) and the carefully designed fitness function, the discrete coding type structure can provide satisfactory flexibility and excellent performance. For a fixed design space, the discrete space can firstly be encoded and mapped to the corresponding coding matrix, and then the optimal coding matrix searched by HBPSGOA will be used to design a broadband coupler with high directivity. The effectiveness of the proposed HBPSGOA, binary particle swarm algorithm (BPSO), and genetic algorithm (GA) to design a discrete coding type coupler is tested. Experimental results prove that the HBPSGOA proposed in this paper has higher optimization efficiency. For the 30 dB broadband microstrip directional coupler designed in this paper, the center frequency is at 2 GHz and the fractional bandwidth is 39.4%. The minimum directivity of the designed coupler is 20 dB in the operating band and the maximum directivity is 39.3 dB at 1.9 GHz. In addition, the reason why the coupler can work in a wideband is investigated. [ABSTRACT FROM AUTHOR]

The target damage assessment when a projectile is attacking a ground target under uncertain information is a difficult problem to solve, because the intersection-relative position of the projectile and target is a random distribution and the target damage is also uncertain in the intersection confrontation between projectile and target. This paper regards the projectile and target as two participants of the zero-sum non-cooperative confrontation game and studies an offensive/defensive game damage strategy modeling method when the projectile meets the target under uncertain information. According to the information of both the projectile and the target, from the perspective of the gain of the projectile attacking the ground target and the gain of the ground target defense, this paper establishes the indicator function of offensive/defensive game of projectile and target intersection under uncertain information and constructs the payoff matrix of the confrontation game between the projectile and the target. The Nash equilibrium of the game is obtained by combining the particle swarm optimization algorithm and the interval number multiple attribute ranking method in the case of uncertain information, and then, a new optimal damage assessment strategy method of Offensive/Defensive Game of projectile and target is gained, the method to solve the Nash equilibrium value of the payment matrix is given. Simulations are performed to validate the feasibility and effectiveness of the proposed game damage strategy model and the solution method. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid genetic-particle swarm optimization (GPSO) algorithm for solving multi-constrained optimization problems. This algorithm is different from the traditional GPSO algorithm, which adopts genetic algorithm (GA) and particle swarm optimization (PSO) in series, and it combines PSO and GA through parallel architecture, so as to make full use of the high efficiency of PSO and the global optimization ability of GA. The algorithm takes PSO as the main body and runs PSO at the initial stage of optimization, while GA does not participate in operation. When the global best value (gbest) does not change for successive generations, it is assumed that it falls into local optimum. At this time, GA is used to replace PSO for particle selection, crossover and mutation operations to update particles and help particles jump out of local optimum. In addition, the GPSO adopts adaptive inertia weight, adaptive mutation parameters and multi-point crossover operation between particles and personal best value (pbest) to improve the optimization ability of the algorithm. Finally, this paper uses a nonlinear constraint problem (Himmelblau's nonlinear optimization problem) and three structural optimization problems (pressure vessel design problem, the welded beam design problem and the gear train design problem) as test functions and compares the proposed GPSO with the traditional GPSO, dingo optimization algorithm, whale optimization algorithm and grey wolf optimizer. The performance evaluation of the proposed algorithm is carried out by using the evaluation indexes such as best value, mean value, median value, worst value, standard deviation, operation time and convergence speed. The comparison results show that the proposed GPSO has obvious advantages in finding the optimal value, convergence speed and time overhead. [ABSTRACT FROM AUTHOR]

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

Summary: Battery storage performance optimization is crucial in ensuring the reliable operation of renewable energy integrated power systems and emergency backup applications. Considering the long life cycle and several other merits such as deep discharge capacity, scalability of power, and energy capacity, Vanadium Redox Flow Battery (VRFB) has become one of the most potential storage solutions for stationary storage applications compared to the other conventional batteries. In this paper, a kW scale VRFB storage system power loss optimization based on the particle swarm optimization (PSO) technique has been demonstrated for the first time. It is a multi‐variable optimization considering both the VRFB pump power loss and stack power loss simultaneously. The electrolyte flow rate has been considered as the control variable in optimizing the overall VRFB system power loss. Charge‐discharge operations of VRFB with four sets of electrolyte flow rates at three different levels of stack terminal current have been taken for validation of the proposed work. It has been observed that for a 1 kW 6kWh VRFB storage system, the overall system power loss becomes the minimum at an optimal flow rate of 6 L/min for a range of (3‐18) L/min of electrolyte flow rate at 40, 45, and 50 A charge‐discharge operations. The proposed work formulation demonstrated in this paper is a generalized one and can be very useful for maximizing the overall system efficiency of VRFB storage and ensuring the reliability of VRFB integrated power system applications. [ABSTRACT FROM AUTHOR]

The attraction towards the Swarm Intelligence (SI) is increasing day by day in the various research fields. There are many swarm-based optimizations are being introduced since early 60's, Evolutionary Algorithms (EA) are the most updated one, Grey Wolf Optimization Algorithm. All Evolutionary Algorithms have proving their capability to resolve most of the optimization problems. These algorithms are using for training the neural networks in this paper. The main difficulty for any optimization problems is selecting the correct values of parameters to get feasible results. The main idea to get best convergence rate and best performance is to vary the parameters of the algorithms. Quadratic Task Issue (QAP) is a NPhard combinatorial advancement issue, in this manner, settling the QAP requires applying at least one of the metaheuristic calculations. This paper presents a similar report between Meta-heuristic calculations: Comparing the optimization algorithms, Particle Swarm Optimization (PSO), Multi Verse Optimization (MVO) and Grey Wolf Optimization (GWO) before and after tuning the parameters with three different datasets. [ABSTRACT FROM AUTHOR]

Panax notoginseng is a valuable herb with geographical indication, and the quality and price of P. notoginseng from different origins are very different. Therefore, this paper proposes a rapid and accurate method for identifying the origins of P. notoginseng by collecting the roots of P. notoginseng. This paper improves the whale optimization algorithm in terms of global convergence and convergence speed, introduces the Levy flight strategy and reconstructed whale synergy factor A, and applies it to the parameter optimization of support vector machines, to obtain a high-performance classification model. The improved whale optimization algorithm model identifies the origin of P. notoginseng by discriminating their terahertz spectra. Compared with the commonly used genetic algorithm and the original whale optimization algorithm, improvement in the whale optimization algorithm was able to avoid falling into local optimum solutions more effectively while having a high convergence rate. Accordingly, the improved whale optimization algorithm optimized support vector machine model obtained an overall accuracy of 98.44%, which was significantly higher than the 95.31% overall accuracy of the genetic algorithm optimized support vector machine model and the 96.88% overall accuracy of the whale optimization algorithm optimized support vector machine model. It was concluded that terahertz spectroscopy together with machine learning would be a promising technique for identifying the origins of P. notoginseng. [ABSTRACT FROM AUTHOR]

In this paper, an approach for the synthesis of desired pattern in 4D antenna linear and planar arrays is proposed, taking into account practical element models. The element of the array is a plasma ME-dipole operating in the band 1.3–3.65 GHz with circular polarization characteristics of 13.3%, from 2.2 GHz to 2.52 GHz with 95% radiation efficiency. A linear arrangement of 16-element spaced by 0.65λ connected with 16 RF-switches with periodic switching frequency of 100 MHz is investigated using different switching time sequences. A planar array consists of 8 × 8 plasma ME-dipoles are designed for different beam shapes. The approach is based on the combination of the particle optimization technique (PSO) and the full-wave simulation using the finite integral technique (FIT). The time sequences are optimized by the PSO algorithm and the patterns at the fundamental frequency and sideband frequencies are synthesized and exported automatically to the array analyzes using the FIT technique. The time-domain excitations can be decomposed into a Fourier series in frequency-domain, thus obtaining equivalent complex excitation distributions at different frequencies. This paper introduces the applicability of planar array in the beam-shaping. Chebyshev, Taylor and binomial planar array are presented. This paper focuses on the pattern synthesis of 4D planar arrays taking into account the mutual coupling between the elements. [ABSTRACT FROM AUTHOR]

Distribution process is the core of logistics enterprise system. Efficient distribution process is the key for enterprises to improve logistics service level, gain competitive advantage, and win customers. In order to reduce the design error of the system and ensure the effective operation of the system, this paper establishes the system model and analyzes the properties of the model through the evaluation and analysis of various resources of the distribution system. The future development of intelligent logistics system is not only the key for logistics enterprises to win competition, but also a new measure to promote China's economic development. This is not only a typical NP hard problem, but also a major challenge for the rational and scientific development of the intelligent logistics industry. Based on the previous theoretical research results, this paper intends to explore the intelligent logistics distribution route selection scheme by using mathematical modeling methods such as particle swarm optimization, so as to provide new technologies and methods for logistics distribution operation and management decision-making. The results show that compared with the traditional genetic algorithm, the accuracy of the improved particle swarm optimization algorithm is improved by 10.36%. This method effectively improves the operation cycle and link efficiency and achieves the purpose of optimization. The improved particle swarm optimization algorithm proposed in this paper is not only more suitable and effective for enterprise decision makers to deal with subjective judgments in an imprecise environment. It is also based on the evaluation sequence of each indicator of the alternative obtained from the evaluation and the comprehensive evaluation of all indicators. By weighting and considering the results, the best solution for enterprise logistics and distribution is selected. [ABSTRACT FROM AUTHOR]

This paper employs machine learning algorithms to detect tax evasion and analyzes tax data. With the development of commercial businesses, traditional algorithms are not appropriate for solving the tax evasion detection problem. Hence, other algorithms with acceptable speed, precision, analysis, and data decisions must be used. In the case of assets and tax assessment, the integration of machine learning models with meta-heuristic algorithms increases accuracy due to optimal parameters. In this paper, intelligent machine learning algorithms are used to solve tax evasion detection. This research uses an improved particle swarm optimization (IPSO) algorithm to improve the multilayer perceptron neural network by finding the optimal weight and improving support vector machine (SVM) classifiers with optimal parameters. The IPSO-MLP and IPSO-SVM models using the IPSO algorithm are used as new models for tax evasion detection. Our proposed system applies the dataset collected from the general administration of tax affairs of West Azerbaijan province of Iran with 1500 samples for the tax evasion detection problem. The evaluations show that the IPSO-MLP model has a higher accuracy rate than the IPSO-SVM model and logistic regression. Moreover, the IPSO-MLP model has higher accuracy than SVM, Naive Bayes, k-nearest neighbor, C5.0 decision tree, and AdaBoost. The accuracy of IPSO-MLP and IPSO-SVM models is 93.68% and 92.24%, respectively. [ABSTRACT FROM AUTHOR]

This paper projects Rhinotia hemistictus based Jackal (RJ) optimization algorithm for solving real power loss reduction problem. Core objective of the paper is power loss reduction. Jackal Optimization (JO) algorithm is modelled based on the hunting actions of Jackal. Naturally Jackals hunt the prey in a group. A leader jackal in the group will lead and other Jackals will follows during hunting. Inadequate multiplicity of Jackals in certain circumstances, make the method to settle in confined optimal state. To avoid the local optima and premature convergence Rhinotia hemistictus actions has been integrated to improve the performance of the Jackal algorithm. Natural actions of exploring food by Rhinotia hemistictus are imitated and it has been assimilated in the algorithm. In the proposed approach the forerunner Jackal utilize the sense of hearing to progress in the global search capability and diminish excessive explorations. Random and adaptive parameter applied in the procedure to control the Exploration and exploitation. Then a modernizing strategy which grounded on the cosine function is used to control the process of the RJ algorithm for evading the local optima. Legitimacy of Proposed Rhinotia hemistictus based Jackal (RJ) optimization algorithm is corroborated in IEEE 30bus test system and outcomes show the projected RJ algorithm condensed the factual power loss splendidly. [ABSTRACT FROM AUTHOR]

To achieve the optimal operation of power system, an improved multi-goal particle swarm optimization (IMPSO) algorithm is proposed in this paper. Based on the multi-goal optimal power flow (MOOPF) calculation, IMPSO algorithm can determine high-quality scheduling schemes which effectively reduce fuel cost, power loss and exhaust emission. Compared with the basic multi-goal PSO (BMPSO) algorithm, IMPSO algorithm realizes better solution diversity and searching ability by integrating an innovative dominant strategy and the mutation-crossover operation of inferior solutions. Four experiments prove that the proposed IMPSO algorithm achieves more superior Pareto optimal set (POS) and best compromise scheme (BCS) than BMPSO algorithm. Furthermore, the multi-output BP power flow prediction model is put forward in this paper to seek the winning elite schemes (WES) around the BCS of IMPSO algorithm. The presented BP prediction model can find multiple WES schemes of bi-objective and tri-objective MOOPF problems, which realize zero constraint violation and smaller goals. In general, the superior WES schemes determined by proposed IMPSO and BP power flow prediction model are of great help to realize the optimal operation of power system with improved economy and safety. [ABSTRACT FROM AUTHOR]

Due to the high ability and flexibility of meta-heuristic algorithms (MAs), they can widely be used in many applications to solve different problems. Recently, real-world engineering applications of these optimization algorithms have attracted researchers' attention. This paper applies particle swarm optimization (PSO) as an effective population-based MAs to design the baghouse (BH). BH filters are among the most commonly used devices in air pollution control systems in mining and food manufacturers and power plants. Designing the BH depends on several parameters such as its capacity or airflow (Nm3/h), air-to-cloth ratio (a ∕ c), cam velocity, and installation limitations. Generally, industrial designers select the number and length of bags and their arrangement based on the experimental observations to meet the parameters mentioned above. The minimum cost or total weight of equipment is utilized for proposing a competitive price for suppliers. In this paper, a PSO algorithm is used to minimize the total cost by finding the best possible design (the number, length, and arrangement of bags). In addition, a real example of installed BH in a pelletizing plant is given and compared with PSO results to investigate the efficiency of the proposed algorithm. The results suggest that PSO can find a better design with minimum total cost than an installed BH filter, and therefore, PSO is applicable to industrial designers. [ABSTRACT FROM AUTHOR]

Biophysical models contain a large number of parameters, while the spiking characteristics of neurons are related to a few key parameters. For thalamic neurons, relay reliability is an important characteristic that affects Parkinson's state. This paper proposes a method to fit key parameters of the model based on the spiking characteristics of neurons, and improves the traditional particle swarm optimization algorithm. That is, a nonlinear concave function and a Logistic chaotic mapping are combined to adjust the inertia weight of particles to avoid the particle falling into a local optimum in the search process or appearing premature convergence. In this paper, three parameters that play an important role in Parkinson's state of the thalamic cell model are selected and fitted by the improved particle swarm optimization algorithm. Using the fitted parameters to reconstruct the neuron model can predict the spiking trajectories well, which verifies the effectiveness of the fitting method. By comparing the fitting results with other particle swarm optimization algorithms, it is shown that the proposed particle swarm optimization algorithm can better avoid local optima and converge to the optimal values quickly. [ABSTRACT FROM AUTHOR]

Multipath data transmission is a key problem that needs to be solved urgently in wireless sensor networks. In this paper, sensor node failure, link failure, energy exhaustion, and external interference affect the stability and reliability of network data transmission. A multipath transmission strategy for wireless sensor networks based on improved shuffled frog leaping algorithm is proposed. A mathematical model of multipath transmission in wireless sensor networks is established. In the shuffled frog leaping algorithm, combined with the transition probability in the particle swarm optimization algorithm, random individuals in the subgroup are introduced to assist the search when updating the frog individual position, which improves the algorithm's ability to jump out of the local optimum and improves the quality of the optimization algorithm solution. The model is applied to multipath transmission in wireless sensor networks. Then, the shuffled frog leaping algorithm is used to update, divide, and reorganize the sensor nodes to select the optimal node to establish the optimal transmission path and improve the stability and reliability of the network. Simulation experiments show that the algorithm in this paper can ensure the reliability of data transmission, reduce the network packet loss rate and network energy consumption, and reduce the average delay of data transmission. [ABSTRACT FROM AUTHOR]

This paper elaborates on optimized design of steel structures directed towards the sustainability of materials. The case in point is steel trusses that are extensively used structural components. Though copious research is available on use of conventional optimization methods, nature-inspired optimization algorithms have received scarce attention particularly in optimal design of planar trusses. In this paper, the development of Ant Lion algorithm for the optimal design models for steel trusses is elaborated. A comprehensive comparison with the optimized sectional weights obtained by other nature inspired optimization algorithms implemented in earlier research by the author. They include elitism based genetic algorithm (EBGA), ant colony optimization (ACO), artificial honeybee optimization (AHBO), and Particle swarm optimization (PSO) algorithm. Four steel trusses with different articulations have been considered for this purpose. It is found that the optimal weights obtained by Ant Lion algorithm are almost on par with those obtained by PSO. The other three algorithms vary marginally. However, the convergence to overall weight of trusses is different for different algorithms. ALO took 100-200 iterations for the convergence. In fact, the convergence to optimized weights are faster in case of ALO and PSO in relation to other algorithms. [ABSTRACT FROM AUTHOR]

In data cleaning, the process of detecting and correcting corrupt, inaccurate or irrelevant records from the record set is a tedious task. Particularly, the process of "outlier detection" occupies a significant role in data cleaning that removes or eliminates the outlier's that exist in data. Traditionally, more efforts have been taken to remove the outliers, and one of the promising ways is customizing clustering models. In this manner, this paper intends to propose a new outlier detection model via enhanced k-means with outlier removal (E-KMOR), which assigns all outliers into a group naturally during the clustering process. For assigning the point to be outliers, a new intra-cluster based distance evaluation is employed. The main contribution of this paper is to select cluster centroid optimally through a newly proposed hybrid optimization algorithm termed particle updated lion algorithm (PU-LA), which hybrids the concepts of LA and particle swarm optimization (PSO), respectively. Thereby, the proposed work is named as E-KMOR-PU-LA. Finally, the efficacy of the proposed E-KMOR-PU-LA model is proved through a comparative analysis over conventional models by concerning runtime and accuracy. [ABSTRACT FROM AUTHOR]

This paper considers the problem of finding the position of a passive target using noisy time difference of arrival (TDOA) measurements, obtained from multiple transmitters and a single receiver. The maximum likelihood (ML) estimator's objective function is extremely nonlinear and non-convex, making it impossible to use traditional optimization techniques. In this regard, this paper proposes the chaos-enhanced adaptive hybrid butterfly particle swarm optimization algorithm, named CAHBPSO, as the hybridization of butterfly optimization (BOA) and particle swarm optimization (PSO) algorithms, to estimate passive target position. In the proposed algorithm, an adaptive strategy is employed to update the sensory fragrance of BOA algorithm, and chaos theory is incorporated into the inertia weight of PSO algorithm. Furthermore, an adaptive switch probability is employed to combine global and local search phases of BOA with the PSO algorithm. Additionally, the semidefinite programming is employed to convert the considered problem into a convex one. The statistical comparison on CEC2014 benchmark problems shows that the proposed algorithm provides a better performance compared to well-known algorithms. The CAHBPSO method surpasses the BOA, PSO and semidefinite programming (SDP) algorithms for a broad spectrum of noise, according to simulation findings, and achieves the Cramer–Rao lower bound (CRLB). [ABSTRACT FROM AUTHOR]

The application of three different categories such as swarm-based, physics-based, and evolutionary-based techniques to various optimization problems related to different fields gained importance due to accuracy, speed and least chance to fall in local minima. Although there are number of physics based optimization techniques available to solve the optimization problems, however, they suffer from their complexity and may require multiple runs to achieve the optimum value. This paper focuses on a simple physics based problem which outperforms few of the existing algorithms. This work investigates the physics-based Lens formula integration using a population-based approach. The contender for a population-level solution to picture obstructions created by a convex, concave, or combination of the two lenses is the item with multiple dimensional locations. The object's location is updated using the image position. The fundamental goals of population-based optimization algorithms are highlighted by the concave, convex, or combination of both lenses, which enhances the speed of exploration, exploitation, and local optimum avoidance of the search space. The focal length equation, controls how the object's picture is produced, may be altered by specifying a number of random and adaptive factors to highlight the exploitation and exploration of the search space. The capacity of the proposed algorithm for exploitation, exploration, utilisation of the search space, avoidance of local minima, and attainment of global maxima is validated using unimodal, multimodal, and composite benchmark functions. In addition, a variety of real-world engineering issues taken into account to verify the performance of the proposed algorithm. The observations compared with few fundamental optimization techniques of the same category and found the proposed solution better performing in some context than the existing ones. [ABSTRACT FROM AUTHOR]

Hydropower plants play an integral role in energy supply and are one of the main renewable energy sources. The efficiency of hydropower reservoir systems is controlled by operating rules that need to be optimized to maximize energy production. Here, a novel optimization algorithm, influential flower pollination algorithm (IFPA), is developed in this paper. IFPA has three enhancements: an efficient exploration mechanism based on an adaptive coefficient, a new exploitation mechanism around the best solution, and a rank-based strategy to smoothly transition from exploration to exploitation. IFPA is tested in two benchmark multi-reservoir systems and a real-world five-reservoir hydropower system in Iran. The results show IFPA outperforms other advanced methods in terms of convergence speed and preciseness to achieve the global solution. The tested algorithms include adaptive guided differential evolution (AGDE), composite DE (CODE), hybridizing sum-local search optimizer (HSLSO), improved teaching learning-based optimization (ITLBO), self-adapting control parameters in DE (jDE), self-adaptive TLBO (SATLBO), and a multi-strategy hybrid of DE and particle swarm optimization (MS-DEPSO). IFPA also achieved better total power production in the real-world case study compared to other algorithms. Overall, IFPA can provide better solutions with less power deficit and shows as a promising method for optimizing hydropower reservoir problems. [ABSTRACT FROM AUTHOR]

Precision object handling and manipulation require the accurate positioning of industrial robots. A common practice for performing end effector positioning is to read joint angles and use industrial robot forward kinematics (FKs). However, industrial robot FKs rely on the robot Denavit–Hartenberg (DH) parameter values, which include uncertainties. Sources of uncertainty associated with industrial robot FKs include mechanical wear, manufacturing and assembly tolerances, and robot calibration errors. It is therefore necessary to increase the accuracy of DH parameter values to reduce the impact of uncertainties on industrial robot FKs. In this paper, we use differential evolution, particle swarm optimization, an artificial bee colony, and a gravitational search algorithm to calibrate industrial robot DH parameters. A laser tracker system, Leica AT960-MR, is utilized to register accurate positional measurements. The nominal accuracy of this non-contact metrology equipment is less than 3 μm/m. Metaheuristic optimization approaches such as differential evolution, particle swarm optimization, an artificial bee colony and a gravitational search algorithm are used as optimization methods to perform the calibration using laser tracker position data. It is observed that, using the proposed approach with an artificial bee colony optimization algorithm, the accuracy of industrial robot FKs in terms of mean absolute errors of static and near-static motion over all three dimensions for the test data decreases from its measured value of 75.4 μm to 60.1 μm (a 20.3% improvement). [ABSTRACT FROM AUTHOR]

This paper presents ocean wave energy control using the Aquila optimization (AO) technique. An oscillating water column (OWC)-type wave energy converter has been considered that is fitted with a Wells turbine and doubly fed induction generator (DFIG). To achieve maximum power point tracking (MPPT), the rotor speed of the DFIG must be controlled as per the MPPT law. The MPPT law is designed in such a way that the Wells turbine flow coefficient remains within the threshold limit. It avoids the turbine from stalling which generates the maximum power. The MPPT law provides the reference rotor speed which is followed by the actual rotor speed. For this, a backstepping controller (BSC)-based rotational speed control strategy has been designed using the Lyapunov stability theory. The BSC has unknown control parameters which should be selected such that tracking errors are minimum. Hence, the objective of this work is to find the unknown control parameters using an optimization approach. The optimization approach of selecting BSC control parameters for an OWC plant has not been explored yet. To achieve this, an integral square error (ISE)-type fitness function has been defined and minimized using the AO technique. The results achieved using the AO technique have been compared with particle swarm optimization (PSO) and a genetic algorithm (GA), validating its superior performance. The rotor speed error maximum peak overshoot is least for AO-BSC as compared to PSO-BSC and GA-BSC. The fitness function value for AO comes out to be least among all the optimization methods applied. However, all tested methods provide satisfactory results in terms of turbine flow coefficient, rotor speed and output power. The approach paves the way for future research on ocean wave energy control. [ABSTRACT FROM AUTHOR]

This paper discusses the importance of microgrids in power systems and introduces a new method for enhancing their performance by improving the transient voltage response in the face of disturbances. The method involves using a hybrid optimization approach that combines driving training-based and particle swarm optimization techniques (HDTPS). This hybrid approach is used to fine-tune the system's cascaded control scheme parameters, based on proportional–integral–accelerator (PIA) and proportional–integral controllers. The optimization problem is formulated using a central composite response surface methodology (CCRSM) to create an objective function. To validate the suggested control methodology, PSCAD/EMTDC software is used to carry out the simulations. The simulations explore various scenarios wherein the microgrid is transformed into an islanded system and is subjected to various types of faults and load changes. A comparison was made between the two proposed optimized controllers. The simulation results demonstrate the effectiveness of using a PIA-optimized controller; it improved the microgrid performance and greatly enhanced the voltage profile. In addition, the two controllers' gains were optimized using only PSO to ensure that the outcomes of the HDTPS model demonstrated the same results. Finally, a comparison was made between the two optimization techniques (HDTPS and PSO); the results show a better impact when using the HDTPS model for controller optimization. [ABSTRACT FROM AUTHOR]

An electronic transition-based bare bones particle swarm optimization (ETBBPSO) algorithm is proposed in this paper. The ETBBPSO is designed to present high precision results for high dimensional single-objective optimization problems. Particles in the ETBBPSO are divided into different orbits. A transition operator is proposed to enhance the global search ability of ETBBPSO. The transition behavior of particles gives the swarm more chance to escape from local minimums. In addition, an orbit merge operator is proposed in this paper. An orbit with low search ability will be merged by an orbit with high search ability. Extensive experiments with CEC2014 and CEC2020 are evaluated with ETBBPSO. Four famous population-based algorithms are also selected in the control group. Experimental results prove that ETBBPSO can present high precision results for high dimensional single-objective optimization problems. [ABSTRACT FROM AUTHOR]

The present work aims to find the optimal solution of Nash Equilibrium (NE) in the traditional Game Theory (GT) applied to water resources allocation. Innovatively, this paper introduces Particle Swarm Optimization (PSO) into GT to propose a cooperative game model to solve the NE problem. Firstly, the basic theory of the PSO algorithm and cooperative game model is described. Secondly, the PSO-based cooperative game model is explained. Finally, the PSO-based cooperative game model is compared with the Genetic Algorithm (GA) to test the performance. Besides taking the countries in Lancang Mekong River Basin as the research object, this paper discusses each country's water consumption and economic benefits under different cooperation patterns. Then, a series of improvement measures and suggestions are put forward accordingly. The results show that the average server occupancy time of the PSO-based cooperative game model is 78.46% lower than that of GA, and the average waiting time is 79.24% lower than that of the GA. Thus, the model reported here has higher computational efficiency and excellent performance than the GA and is more suitable for the current study. In addition, the multi-country cooperation mode can obtain more economic benefits than the independent water resource development mode. This model can quickly find the optimal combination of 16 cooperation modes and has guiding significance for maximizing the benefits of cross-border water Resource Utilization. This research can provide necessary technical support to solve the possible contradictions and conflicts between cross-border river basin countries and build harmonious international relations. [ABSTRACT FROM AUTHOR]

With the increasing openness and development of network technology, the network based on the wireless sensor network system has increasingly become an important tool for human social life and production, but it also brings some network security problems. Among them, focus is on network privacy disclosure and foreign intrusion and the research of intrusion detection and privacy protection has increasingly become an important topic of network security. This paper deeply studies the wireless sensor network system based on neural network on the basis of traditional privacy protection and intrusion detection system. Firstly, it applies particle swarm optimization algorithm and constructs a wireless sensor network intrusion detection system based on particle swarm optimization algorithm. The system includes important modules such as data extraction, data analysis, data feedback, and auxiliary decision-making. Compared with other algorithms, particle swarm optimization algorithm does not rely on problem information. It mainly uses real numbers to solve, so the algorithm has strong universality. At the same time, its corresponding principle is simple and easy to implement and less parameters need to be adjusted. Compared with other algorithms, particle swarm optimization algorithm has fast convergence speed and little memory requirement for the computer. At the same time, this paper uses the leap of particle swarm optimization algorithm to make it easier to find the global optimal solution. At the corresponding level of wireless sensor network privacy protection, based on the original data aggregation privacy protection scheme, this paper proposes a privacy protection scheme based on polynomial regression and a user privacy protection scheme based on the same state encryption, which further improves the security of privacy protection and facilitates the management of information. To realize the integrity of user privacy information protection, this paper realizes the decryption of data based on the correlation between binary metadata and compares the corresponding decrypted data with aggregated data, so as to complete the integrity of privacy data protection. The experimental results show that the binary metadata correlation decryption method proposed in this paper and the introduction of the corresponding particle swarm optimization algorithm improve the stability of the system by about 10%, the corresponding system security by a positive proportion, and the integrity of private data by about 10%; therefore, the algorithm proposed in this paper has obvious advantages. [ABSTRACT FROM AUTHOR]

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

The migration and predation of grasshoppers inspire the grasshopper optimization algorithm (GOA). It can be applied to practical problems. The binary grasshopper optimization algorithm (BGOA) is used for binary problems. To improve the algorithm's exploration capability and the solution's quality, this paper modifies the step size in BGOA. The step size is expanded and three new transfer functions are proposed based on the improvement. To demonstrate the availability of the algorithm, a comparative experiment with BGOA, particle swarm optimization (PSO), and binary gray wolf optimizer (BGWO) is conducted. The improved algorithm is tested on 23 benchmark test functions. Wilcoxon rank-sum and Friedman tests are used to verify the algorithm's validity. The results indicate that the optimized algorithm is significantly more excellent than others in most functions. In the aspect of the application, this paper selects 23 datasets of UCI for feature selection implementation. The improved algorithm yields higher accuracy and fewer features. [ABSTRACT FROM AUTHOR]

The paper proposes the optimal placement of distributed generators (DGs) in radial distribution system along with reconfiguration. The DGs which are considered in this paper are designed with photo voltaic (PV) cells and wind power generation. The paper studies on the PV and wind generation penetration in radial distribution system as per the irradiation and wind velocity throughout the day. The irradiation levels are considered throughout the day. Unlike in former hybrid optimization techniques, the placement of DGs and reconfiguration is shared between two optimization techniques. The paper also details the reliability improvement of radial distribution system with reconfiguration and DGs placement. Some part of reconfiguration and DG locations are selected with one optimization technique and rest of reconfiguration and DG size are selected with another optimization technique. The combination of two optimization techniques properties leads towards the novel hybrid optimization. The methodology is formulated with the black widow algorithm and crow search algorithm. The performance of the method is tested on two test systems which are 16 bus RDS and 33 bus RDS. [ABSTRACT FROM AUTHOR]

This paper aims to map the task of reliable transmission of wireless sensor networks. At the same time, this paper transforms the mapping problem of wireless sensor networks into a problem of reducing the energy consumption of mapping under many constraints such as reliability and scheduling length and uses discrete particle swarm optimization algorithm to map. For optimization, the algorithm performs iterative calculations to obtain the best mapping node for each operation so that the inertia coefficient of the existing particle swarm optimization algorithm is improved and linearly minimized with the number of iterations. When resource-demanding tasks need to allocate dynamic resources to multiple nodes to complete collaboratively, adding the mapping principle of the nearest node in the discrete particle swarm optimization mapping reduces the energy consumption of communication between tasks. An in-depth analysis of the influencing factors of the ice and snow tourism market shows that the per capita disposable income of urban residents and the number of urban residents have a significant impact on the ice and snow tourism market demand. In addition, regression analysis and demand-based forecasting are important methods to analyze the scale and development trend of tourism. At the same time, it shows an important position in the purpose of urban tourism and regional market share so as to provide a basis for decision-making in tourism destination marketing. This paper mainly studies and analyzes the wireless sensor network and further introduces it into dynamic resource allocation and ice and snow tourism, which can promote the continuous development of dynamic resource allocation and ice and snow tourism. [ABSTRACT FROM AUTHOR]

Multi-classification Twin Support Vector Machine model (MTWSVM) is put forward in order to solve the issues of low detection rates and weaken practicability in current network intrusion detection models. In the first place, the Adaptive Synthetic sampling method (ADASYN) is used to balance the dataset. And in the second place, the numerical and normalized methods are used to process the dataset. Ultimately, the data is balanced and simple enough to improve the calculability and convergence speed of the model. For the multi-classification problem, the classification strategy is used to build multi-classification model based on parameter optimization because the parameters have a greater impact on the model. The Improved Particle Swarm Optimization algorithm (IPSO) is used to find the global optimal parameters iteratively in the paper. The KDD’99 dataset is used to perform the network intrusion detection experiments. The experimental results show that the Multi-classification Twin Support Vector Machine model based on parameter optimization can effectively improve the detection performance compared with other models. At the same time, the experimental results show the overall detection accuracy and the detection accuracy of various attacks have been significantly improved. Therefore, the intrusion detection model proposed in the paper has validity and practicability in the network intrusion detection. [ABSTRACT FROM AUTHOR]

• A niching concept is inspired to enhance the original ChOA. • RN-ChOA proposes a novel constraint handling technique. • The RN-ChOA is applied to estimate parameters on the SM55, SW255, and KC200GT. • RN-ChOA is compared with ten well-known algorithms. Researchers are becoming increasingly interested in studying how to accurately estimate the parameters of solar PV models. In this regard, this paper proposes a newly proposed nature-inspired technique named chimp optimization algorithm (ChOA) to create accurate and dependable PV models, such as single diode, double diodes, three diodes, and PV module models. In the PV models' parameters estimation using optimization algorithms, two significant concerns need to be addressed: classifying various local/global optima and preserving these optimum values until the termination. Since ChOA is a general optimizer, it lacks an operator to address the two issues mentioned above. In order to address the mentioned problems, this paper embeds the niching technique in ChOA that includes the personal best qualities of PSO and a local search technique. In addition, a novel constraint handling approach is utilized to ensure that the algorithm is robust in tackling PV Models' parameters estimation constraints. The outcome of RN-ChOA is evaluated using seven well-known optimization algorithms, including the whippy Harris hawks optimization algorithm (WHHOA), performance-guided JAYA (PGJAYA), enriched Harris hawks optimization algorithm (EHHOA), improved JAYA (IJAYA), birds mating optimizer (BMO), flexible particle swarm optimization algorithm (FPSO), chaotic biogeography-based optimizer (CBBO), and generalized oppositional teaching-learning algorithm (GOTLA), as well as dynamic Levy flight ChOA (DLF-ChOA) and weighted ChOA (WChOA) as the most recent modified version of ChOA. Furthermore, the performance of the RN-ChOA method has been assessed in a practical application for parameter evaluation of three widely-used commercial modules, namely, multi-crystalline (KC200GT), polycrystalline (SW255), and mono-crystalline (SM55), under a variety of temperature and irradiance conditions that cause changes in the photovoltaic model's parameters. The findings demonstrate the robustness and excellent performance of the suggested approach. [ABSTRACT FROM AUTHOR]

The convergence performance of existing fractional particle swarm optimization algorithm directly depends on a single fractional-order operator. When its value increases, the convergence speed of particles gets slower. When its value decreases, the probability of the particle swarm trapping into the local optimum increases. In order to solve this problem, an improved fractional particle swarm optimization (IFPSO) algorithm is proposed in this paper. New variables are introduced in this paper to redefine the formula. The fractional-order operator is added to the velocity update formula and the position update formula. The IFPSO algorithm has linearly decreasing inertia weights. Then the proposed IFPSO algorithm is used to optimize the parameters of support vector machine (SVM) and the clustering center of the K-means classifier is selected. Experimental results show that the IFPSO algorithm can effectively avoid falling into the local optimal solution. It has a faster convergence rate and better stability than the original algorithm, which proves the effectiveness of the algorithm. Examples verify that the IFPSO algorithm can improve the classification accuracy of SVM in practical application. The IFPSO algorithm effectively solves the problem that K-means algorithm is overly dependent on the initial center and may have empty classes. [ABSTRACT FROM AUTHOR]

Grasshopper optimization algorithm (GOA) is a meta-heuristic algorithm for solving optimization problems by modeling the biological habit and social behavior of grasshopper swarms in nature. Compared with other optimization algorithms, GOA still has room to improve its performance on solving complex problems. Therefore, this paper proposes an improved grasshopper optimization algorithm (EMGOA) based on dynamic dual elite learning and sinusoidal mutation. First of all, dynamic elite learning strategy is adopted to improve the influence of elites on the update process, enabling the algorithm to have a faster convergence speed. Then, sinusoidal function is utilized to guide the mutation of the current global optimal individual during each iteration to avoid the algorithm falling into the local optimum and improve the convergence accuracy of the algorithm. In order to investigate the performance of the proposed EMGOA algorithm, experiments are conducted on 26 benchmark functions and CEC2019 in this paper. The experimental results show that the optimization performance of EMGOA is obviously better than GOA, and EMGOA is competitive with six state-of-the-art meta-heuristic optimization algorithms. [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]

Global optimization is an active area of research in atomistic simulations, and many algorithms have been proposed to date. A prominent example is basin hopping Monte Carlo, which performs a modified Metropolis Monte Carlo search to explore the potential energy surface of the system of interest. These simulations can be very demanding due to the high-dimensional configurational search space. The effective search space can be reduced by utilizing grids for the atomic positions, but at the cost of possibly biasing the results if fixed grids are employed. In this paper, we present a flexible grid algorithm for global optimization that allows us to exploit the efficiency of grids without biasing the simulation outcome. The method is general and applicable to very heterogeneous systems, such as interfaces between two materials of different crystal structures or large clusters supported at surfaces. As a benchmark case, we demonstrate its performance for the well-known global optimization problem of Lennard-Jones clusters containing up to 100 particles. Despite the simplicity of this model potential, Lennard-Jones clusters represent a challenging test case since the global minima for some "magic" numbers of particles exhibit geometries that are very different from those of clusters with only a slightly different size. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a popular optimization technique known for its simplicity and effectiveness. This paper introduces a variant that achieves a better balance between exploration and exploitation, named DiPSO. DiPSO incorporates a novel strategy based on trends in mean distance between individuals for local exploitation control. Experiments on 29 benchmark functions demonstrate that DiPSO consistently outperforms the state‐of‐the‐art variant of PSO. Convergence analysis reveals that DiPSO achieves faster convergence and superior solutions. These results highlight the effectiveness of DiPSO in solving optimization problems. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The multiobjective optimization problems are a common problem in various fields in the real society. Therefore, solving the multiobjective optimization problems are one of the important problems studied by many researchers in recent years. From the research in recent years, it can be seen that there is still a lot of room for development of particle swarm optimization in solving multiobjective optimization problems. This paper proposes a novel multiobjective particle swarm optimization combining hypercube and distance, called HDMOPSO. The particle velocity update part in this paper uses a combination of hypercube and distance. In order to prevent the algorithm from falling into the local optimum, the part also uses the nonlinear decreasing opposite mutation strategy, which enables the particles to explore a more area. Finally, a control strategy is used for external archive to improve the convergence and diversity of the algorithm. The algorithm has been simulated in 22 test problems and compared with multiobjective particle swarm optimization algorithms (MOPSOs) and multiobjective evolutionary algorithms (MOEAs). The results show that the HDMOPSO can effectively improve the convergence and diversity, so it is an effective improvement. [ABSTRACT FROM AUTHOR]

Aiming at the poor prediction performance of the traditional GM(1,N) model, this paper proposes the GM(1,N) model with expression optimization: firstly, unknown parameters are introduced into the coefficient matrix of the traditional GM(1,N) model to obtain the model expression with unknown parameters: Then the average relative error function with unknown parameters is constructed: Finally, the particle swarm optimization algorithm is used to obtain the parameter column with the smallest average relative error. Taking the carbon emission of the logistics industry in Hubei Province as an example, this paper forecasts the carbon emission by using the traditional GM(1,N) model, GM(1, N) model with background value optimization, and GM(1, N) model with expression optimization. By comparing and analyzing the prediction results of the three models, it is concluded that GM(1,N) model with expression optimization has better prediction performance. [ABSTRACT FROM AUTHOR]

According to the characteristics of energy limitation in wireless sensor networks, this paper proposed a clustering protocol based on spatiotemporal correlation weighted objective function particle swarm optimization algorithm (SC-WOFPSO). Firstly, this protocol used Kohonen neural network to extract the data similarity between nodes. In the process of clustering, this protocol comprehensively considered the data similarity between nodes, the distance between nodes and the residual energy of cluster head nodes, and used PSO algorithm for iterative optimization to find the optimal cluster head set. In the process of cluster, the non cluster head nodes in the network calculated the goal function value for each cluster head, and chose to join the cluster head with the maximum function value. Finally, this paper started from the three performance indicators of total network energy consumption, network life and network throughput to verify the protocol. The result shows that this protocol can effectively reduce network energy consumption, increase network life and network throughput. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of the sparrow search algorithm such as slow convergence speed, insufficient optimization accuracy and easy to fall into the local optimum, this paper proposed an improved sparrow search algorithm that combined the sine search strategy and the diversity mutation processing strategy. Through the introduction of a sine search strategy, adaptive adjustment of individual weights improved the convergence speed of the algorithm. Aiming at the problem of excessive individual aggregation, this paper adopted diversity mutation processing, introduced the concept of population aggregation degree in biology and Cauchy mutation to disturb the optimal solution, and improved the possibility of the algorithm escaping from the local optimal. By testing 9 benchmark functions with different characteristics, the test results show that the improved algorithm can converge to the optimal value faster, with better average and standard deviation, indicating that it has better convergence speed, convergence stability, and the ability to escape local optimal values. By applying the improved optimization algorithm to the parameter tuning of the fractional PID controller, the experimental results further verify the effectiveness and feasibility of the improved strategy. [ABSTRACT FROM AUTHOR]