Acceleration Coefficients Research Articles

Fitness and historical success information-assisted binary particle swarm optimization for feature selection

Feature selection is a critical preprocessing step in machine learning aimed at identifying the most relevant features or variables from a dataset. Although conventional particle swarm optimization (PSO) has shown efficiency for feature selection tasks, developing an effective PSO algorithm for this task is still challenging. This study proposes a fitness and historical success information-assisted binary particle swarm optimization, denoted by FPSO. The FPSO is developed by integrating different search strategies, including a weighted center-based approach, historical information-based acceleration coefficients, and selection operation. These strategies are embedded into the FPSO to enhance the levels of exploration and exploitation based on the fitness value of particles and their historical search status. In the FPSO, the transfer function is also added to transform the continuous search space into binary search space. Experimental validation and comparison with seven other metaheuristic algorithms on 24 datasets verify the effectiveness of the FPSO in eliminating irrelevant and redundant features.

Fuel constrained sustainable water-gas-heat-power generation expansion planning of isolated microgrid

On account of slowly reducing of fossil-fuel, profitable utilization of available fossil-fuel to produce electric power is a major concern for electric power producers. Here, short-period fuel constrained power, heat, natural gas and clean water augmentation planning (FCPHGWAP) of isolated microgrid considering carbon capture is presented. FCPHGWAP finds out the most economical and consistent augmentation plan to meet the prophesied power, heat, natural gas and clean water demand over a short-term time horizon whilst satisfying several technical as well as societal constraints. An archetypical system having extant diesel generators (DGs), PVT panel, wind turbine generator (WG), micro-cogeneration unit (MCU), battery energy storage system (BESS), plug-in electric vehicles (PEVs), thermal energy storage system, natural gas storage unit, carbon capture unit (CCU), electrolyzer, hydrogen storage unit and aspirant PVT panel, WGs, MCUs and PEVs is taken into consideration. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients and grey wolf optimization are used to solve this FCPHGWAP problem. It is observed that net present value having fuel constraints is less than that of without fuel constraints.

Insight into the seismic response of gravity retaining walls

An extensive study of gravity walls subjected to strong seismic excitation is presented, exploring the role of parameters such as the width of wall, the type of excitation (idealised wavelets or recorded accelerograms), the peak ground acceleration (PGA), the dominant period (TP) of the wavelets, the accelerogram’s polarity and the coefficient of friction of the wall–soil interface. The retaining and foundation soil consists of dry sand layers modeled by a refined constitutive model. Appropriate interface elements, allowing separation and sliding, model the wall–soil interface. Results are presented mainly in terms of time-histories and dimensionless soil pressure distributions. Both horizontal and vertical seismic soil pressures are explored and compared with the classical Mononobe-Okabe–type and Rankine–type pseudostatic analytical solutions. It is shown that reduced acceleration coefficients must be input in such solutions to obtain reasonable results.

U-AEFA: Online and offline learning-based unified artificial electric field algorithm for real parameter optimization

Optimization problems in real-world scenarios require algorithms that effectively balance exploration and exploitation to avoid local optima and achieve global solutions. To address this, we propose a unified artificial electric field algorithm (U-AEFA) that integrates both offline learning (inspired by high-performing metaheuristic algorithms) and online learning (through historical data generated during evolution). U-AEFA introduces a unique three-layer population structure to enhance search efficiency, consisting of the best agent in the first layer, the top-performing agents in the second layer, and the remaining agents in the third layer. Key features of U-AEFA include (i) an effective Coulomb’s constant for improved exploration, (ii) a non-uniform mutation operator to mitigate premature convergence, and (iii) two acceleration coefficients for enhanced performance. These three factors constitute offline learning and have been implemented to improve different design elements of the algorithm. As part of online learning, it employs a difference vector reuse (DVR) strategy to evolve the first-layer agents. The algorithm is evaluated using the CEC 2017 test suite across multiple dimensions (10, 30, 50, 100), where it consistently outperforms seven state-of-the-art algorithms, demonstrating superior accuracy and convergence speed. Moreover, U-AEFA’s robustness is validated on 12 high-dimensional feature selection problems, further highlighting its effectiveness in solving complex optimization tasks. The source code of U-AEFA is available at

A Novel EPSO Algorithm Based on Shifted Sigmoid Function Parameters for Maximizing the Energy Yield from Photovoltaic Arrays: An Experimental Investigation

This paper presents a new method for maximizing the energy production of photovoltaic (PV) arrays, utilizing the Enhanced Particle Swarm Optimization (EPSO) algorithm. In contrast with the classical PSO, the proposed EPSO algorithm employs shifted sigmoid functions in order to adapt the acceleration coefficients and the inertia weight instead of using constant coefficients. The EPSO algorithm proves its effectiveness even in challenging either condition, such as abrupt variations in solar irradiance or instances of partial shading conditions (PSCs). The proposed EPSO-based Maximum Power Point Tracking (MPPT) controller has undergone testing and a comparative analysis alongside well-known metaheuristic algorithms, which include the PSO, Bat Algorithm (BAT), Grasshopper Optimization Algorithm (GOA), and Grey Wolf Optimizer (GWO). The results obtained through simulations consistently demonstrate that the EPSO-based MPPT method surpasses other metaheuristic approaches in terms of energy yield and the speed at which it tracks the Maximum Power Point (MPP) under PSCs. In addition, the proposed EPSO algorithm demonstrates robustness, maintaining consistent tracking of the MPP during sudden solar radiation changes. Experimental validation on a real PV system affirms these findings, demonstrating that the EPSO algorithm surpasses its counterparts by achieving a high MPPT efficiency of approximately 99.19% in a fast-tracking time of 2.4 s while maintaining minimal power oscillations of around 2.04 %.

Learning to Guide Particle Search for Dynamic Multiobjective Optimization.

Dynamic multiobjective optimization problems (DMOPs) are characterized by multiple objectives that change over time in varying environments. More specifically, environmental changes can be described as various dynamics. However, it is difficult for existing dynamic multiobjective algorithms (DMOAs) to handle DMOPs due to their inability to learn in different environments to guide the search. Besides, solving DMOPs is typically an online task, requiring low computational cost of a DMOA. To address the above challenges, we propose a particle search guidance network (PSGN), capable of directing individuals' search actions, including learning target selection and acceleration coefficient control. PSGN can learn the actions that should be taken in each environment through rewarding or punishing the network by reinforcement learning. Thus, PSGN is capable of tackling DMOPs of various dynamics. Additionally, we efficiently adjust PSGN hidden nodes and update the output weights in an incremental learning way, enabling PSGN to direct particle search at a low computational cost. We compare the proposed PSGN with seven state-of-the-art algorithms, and the excellent performance of PSGN verifies that it can handle DMOPs of various dynamics in a computationally very efficient way.

Hybrid artificial neural network models for bearing capacity evaluation of a strip footing on sand based on Bolton failure criterion

This paper employs the Bolton failure criterion, incorporating strength-dilatancy relationships, to analyze the bearing capacity factor of a strip footing on dense sand. Utilizing finite element limit analysis (FELA) based on the lower and upper bound theorems, the study presents the results as average bound solutions. By using the Bolton model, the b parameter is first calibrated and found that it should be about 3.50 to align the ultimate bearing capacity (qu) from FELA to have a good agreement with that from experimental test results from previous studies. The influence of parameters relevant to the Bolton failure criterion is analysed, showing that an increase in relative density (DR) significantly affects the variation in the bearing capacity factor (Nγ) at higher Q values, while lower Q values inhibit dilatancy due to soil crushing. The width of the strip footing (B) has a decreasing effect on Nγ at higher Q values, and the unit weight (γ) changes minimally impact Nγ within the range of 16–22 kN/m3. Additionally, an increase in the critical state friction angle (ϕcv) consistently increases Nγ, highlighting its direct correlation with soil shear strength. A hybrid artificial neural network (ANN) model integrates machine learning with four optimization algorithms: Imperialist Competitive Algorithm (ICA), Ant Lion Optimization (ALO), Teaching Learning Based Optimization (TLBO), and New Self-Organizing Hierarchical Particle Swarm Optimizer with Jumping Time-Varying Acceleration Coefficients (NHPSO-JTVAC). Comparative rank analysis of hybrid ANN models based on the selection of the optimal number of hidden neurons demonstrates that the ANN-TLBO model excels in predicting the bearing capacity factor, achieving a score of 48. This conclusion is corroborated by an error heatmap matrix, which indicates a minimized percentage of error relative to other hybrid ANN models. Importance analysis identifies particle crushing strength (Q) as the most significant factor influencing the bearing capacity factor (Nγ).

Quantitative evaluation of the degradation acceleration of insulation at local area in multilayer ceramic capacitors

The degradation acceleration based on dielectric thickness was evaluated by analyzing the local insulation degraded area in prototype Ni-BaTiO3 multilayer ceramic capacitors. Locally thinned dielectric layers, resulting from nickel internal electrode bulges, were associated with shorter lifetimes than the mean time to failure observed in highly accelerated life test. A strong correlation was observed between the minimum thickness of the dielectric layer at the degraded area and the lifetime. The electric field acceleration coefficient was derived from the correlation between the electric field strength, calculated from the dielectric thickness at the degraded area, and the lifetime. The impact of dielectric thinning on degradation acceleration was quantified by analyzing these local degraded areas. The factors influencing degradation acceleration were also discussed based on these findings.

Efficient optimization parameter calibration method-based DEM simulation for compacted loess slope under dry–wet cycling

Dry–wet cycles can cause significant deterioration of compacted loess and thus affect the safety of fill slopes. The discrete element method (DEM) can take into account the non-homogeneous, discontinuous, and anisotropic nature of the geotechnical medium, which is more capable of reflecting the mechanism and process of instability in slope stability analysis. Therefore, this paper proposes to use the DEM to analyze the stability of compacted loess slopes under dry–wet cycles. Firstly, to solve the complex calibration problem between macro and mesoscopic parameters in DEM models, an efficient parameter optimization method was proposed by introducing the chaotic particle swarm optimization with sigmoid-based acceleration coefficients algorithm (CPSOS). Secondly, during the parameter calibration, a new indicator, the bonding ratio (BR), was proposed to characterize the development of pores and cracks in compacted loess during dry–wet cycles, to reflect the impact of dry–wet action on the degradation of bonding between loess aggregates. Finally, according to the results of parameter calibration, the stability analysis model of compacted loess slope under dry–wet cycling was established. The results show that the proposed optimization calibration method can accurately reflect the trend of the stress–strain curve and strength of the actual test results under dry–wet cycles, and the BR also reflects the degradation effect of dry–wet cycles on compacted loess. The slope stability analysis shows that the DEM reflects the negative effect of dry–wet cycles on the safety factor of compacted loess slopes, as well as the trend of gradual stabilization with dry–wet cycles. The comparison with the finite element analysis results verified the accuracy of the discrete element slope stability analysis.

An improved particle swarm optimisation algorithm for optimum placement and sizing of biogas-fuelled distributed generators for seasonal loads in a radial distribution network

Optimal allocation is critical for effective planning and operation of grid connected distributed generator(s) (DGs) subject to efficient optimisation approach(es). In this paper, an improved particle swarm optimisation (IPSO) algorithm based on weighted randomised acceleration coefficients and adaptive inertia weight for dynamic movement of the particles towards an optimal solution is proposed. The technique is expected to determine optimal placement, sizing and power factor of biogas-fuelled distributed generators (B-DGs such as internal combustion engine (ICE) and fuel cells (FC)) in a radial distribution network. The proposed IPSO is applied to simultaneously optimise three technical objectives namely total active power loss (TAPL), voltage deviation (VD) and voltage stability index (VSI) considering load growth and seasonal voltage dependence. The proposed IPSO is validated using an IEEE 33 bus radial distribution network to evaluate its effectiveness through various combinations of B-DGs for different scenarios and cases with a maximum of 3 B-DGs. Some of the key findings indicate that operating 3 ICEs at optimal power factor reduces TAPL by 93.52 %, reduces VD by 99.62 % and improves VSI by 23.32 % compared to 61.80 % TAPL reduction, 94.77 % VD reduction and 17.89 % VSI improvement for 3 FCs operating at unity power factor. The proposed IPSO gives better results than the standard PSO in terms of solution quality, convergence speed and statistical results. It is also perceived to be better compared to most of the recent state-of-the-art optimization algorithms found in literature.

Assessment of Models for Nonlinear Oscillatory Flow Through a Hexagonal Sphere Pack

We review models for unsteady porous media flow in the volume-averaging framework and we discuss the theoretical relations between the models and the definition of the model coefficients (and the uncertainty therein). The different models are compared against direct numerical simulations of oscillatory flow through a hexagonal sphere pack. The model constants are determined based on their definition in terms of the Stokes flow, the potential flow and steady nonlinear flow. Thus, the discrepancies between the model predictions and the simulation data can be attributed to shortcomings of the models’ parametrisation. We found that an extension of the dynamic permeability model of Pride et al. (PRB 47(9):4964–4978, 1993) with a Forchheimer-type nonlinearity performs very well for linear flow and for nonlinear flow at low and medium frequencies, but the Forchheimer term with a coefficient obtained from the steady-state overpredicts the nonlinear drag at high frequencies. The model reduces to the unsteady Forchheimer equation with an acceleration coefficient based on the static viscous tortuosity for low frequencies. The unsteady Forchheimer equation with an acceleration coefficient based on the high-frequency limit of the dynamic tortuosity has large errors for linear flow at medium and high frequencies, but low errors for nonlinear flow at all frequencies. This is explained by an error cancellation between the inertial and the nonlinear drag.

Differential Privacy-Preserving IoT Data Sharing Through Enhanced PSO

ABSTRACT With the proliferation of Internet of Things (IoT) devices and the generation of massive amounts of sensitive data, preserving privacy while enabling data sharing has become a critical concern. In this research, we propose a novel approach for achieving differential privacy in IoT data sharing through an improved Particle Swarm Optimization (PSO) algorithm. Our objective is to find the optimal configuration of privacy-preserving mechanisms that maximizes privacy while maintaining data utility. The research begins with a comprehensive overview of differential privacy in IoT data sharing, highlighting the limitations of existing optimization algorithms. We then present our proposed improvements to the traditional PSO algorithm, including the design of a suitable fitness function, the use of a dynamic inertia weight, exploration of different neighborhood topologies, and adaptive acceleration coefficients. We define a set of performance metrics, including privacy metrics (e.g. ε-differential privacy parameter) and utility measures (e.g. accuracy, utility loss), to assess the algorithm’s effectiveness. The results of our experiments demonstrate that the improved PSO algorithm achieves higher privacy guarantees while maintaining competitive levels of data utility compared to existing approaches. The proposed algorithm exhibits faster convergence, better exploration of the search space, and improved scalability. Our research contributes to the field of privacy-preserving IoT data sharing by providing an efficient and effective optimization algorithm that enables secure and privacy-aware data sharing while facilitating valuable insights and analysis.

Seismic Stability Study of Bedding Slope Based on a Pseudo-Dynamic Method and Its Numerical Validation

Earthquakes are one of the main causes of bedding slope instability, and scientifically and quantitively evaluating seismic stability is of great significance for preventing landslide disasters. This study aims to assess the bedding slope stability under seismic loading and the influences of various parameters on stability using a pseudo-dynamic method. Based on the limit equilibrium theory, a general solution for the dynamic safety factor of bedding slope is proposed. The effects of parameters such as slope height, slope angle, cohesion, internal friction angle, vibration time, shear wave velocity, seismic acceleration coefficient, and amplification factor on stability are discussed in detail. To evaluate the validity of the pseudo-dynamic solution, the safety factors are compared with those given by early cases, and the results show that the safety factors calculated by the present formulation coincide better with those of previous methods. Moreover, a two-dimensional numerical solution of bedding slope based on Mohr–Coulomb’s elastic–plastic failure criterion is also performed by using the finite element procedure, and the minimum safety factor is essentially consistent with the result of the pseudo-dynamic method. It is proved that the pseudo-dynamic method is effective for bedding slope stability analyses during earthquakes, and it can overcome the limitations of the pseudo-static method.

Upper Bound Analysis of Two-Layered Slopes Subjected to Seismic Excitations Using the Layer-Wise Summation Method

Due to natural sedimentation and artificial filling, slopes exhibit heterogeneity in the form of multi-layer soils, namely, layered slopes. Compared with homogenous slopes, the failure mechanism of layered slopes is more complex owing to the different shear strengths of each soil layer. Therefore, it is of great importance to gain insight into the stability of layered slopes. In this study, the upper bound theorem of limit analysis incorporated with a pseudo-static approach is utilized to investigate the seismic stability of two kinds of two-layered slopes: one with a stiff lower soil layer and the other with a weak lower soil layer. Three failure patterns, namely face failure, toe failure and base failure, are taken into account. A depth coefficient (Δ) is introduced to describe the distribution of two soil layers. The layer-wise summation method is adopted to calculate the safety factor and yield acceleration coefficient more conveniently. Based on Newmark’s method, the earthquake-induced horizontal displacement is estimated. The calculated results are validated by comparisons with published literature and the numerical method in terms of safety factor, critical failure surface and yield acceleration coefficient. The results show that the depth coefficient has a significant influence on the failure mechanism of two-layered slopes by determining whether the stability of upper-layered soil is dominant in the overall slope stability or not. Inaccurately identifying the failure patterns will overestimate the seismic performance of two-layered slopes in the aspects of safety factor and yield acceleration coefficient, leading to an underestimation of earthquake-induced horizontal displacement.

Амплитудно-частотные характеристики виброзащитной системы сиденья с трехсегментной статической силовой характеристикой и участком квазинулевой жесткости

Vibration protection for heavy equipment operators is important. Exposure to vibration is harmful to health. Vibration protection seats are used for protection. Reliable passive systems with quasi-zero stiffness are promising. For the developed design of the seat on the basis of parallelogram mechanism, spring, cable and rollers, the study of vibration-protective properties is carried out. The design scheme has been developed, key parameters have been selected, and their study on vibration protection parameters has been carried out. The static force response was approximated by three linear segments with a horizontal mean. The known dynamic model describing forced oscillations of the mass was used. The displacement of the base was given by a harmonic function. Vibration protection systems with three-segment and with one-segment static force response were compared. The comparison was carried out in terms of transmission coefficients, accelerations and suspension displacement. The results of the computational experiment are presented in the form of graphs: amplitude-frequency characteristics in the form of transmission coefficients in acceleration and displacement, at different amplitudes of base vibrations, damp-ing and stiffness coefficients. Dependences of average values of transmission coefficients in the investigated frequency range are given. Vibration damping is effective when the maximum suspension travel does not exceed the middle section of the static characteristic. The most effective vibration damping, according to the average value of the acceleration transfer coefficient, is achieved at minimum, and for a single-segment characteristic — zero values of the stiffness coefficient. The presence of extreme segments in the characteristic significantly increases the average values of the accelera-tion and displacement transfer coefficients of the suspension. Seat vibration protection systems in real conditions should have a limitation of suspension displacement for ergonomic reasons. The values of the stiffness coefficients of the extreme parts of the static force response, according to the criterion of minimizing the average value of the acceleration transfer coefficient, should be minimized. To substantiate the optimal values of the stiffness coefficients of the extreme sections of the characteristic and viscous friction coefficients, it is advisable to conduct additional studies under step and stochastic impacts.

Seismic behavior of modular buildings with reinforced concrete (RC) structural walls as seismic force resisting system

Modular construction is becoming more popular worldwide due to its reduced construction time, lower construction waste, decreased on-site labor requirements, among other benefits. However, the unique structural characteristics of modular buildings, such as discrete floor diaphragms, limited connections between modules, may result in different behavior during earthquakes compared to conventional buildings. However, the seismic response of modular buildings is not well understood, and the force demand in seismic collectors (i.e., connections between modules and seismic force resisting elements) is not quantified. To address this problem, a 9-story prototype building with reinforced concrete (RC) walls was used to investigate the structural properties and responses of modular buildings. Firstly, a nonlinear numerical model was created to accurately model the RC walls, modular frames, inter-module connections, seismic collectors, and other key structural elements. Elastic modal analysis and nonlinear response history analyses were conducted to assess the building's behavior under seismic loads. Subsequently, a simplified numerical model was developed to investigate the effect of three key parameters: the stiffness of seismic collectors, RC wall strength, and earthquake intensity. It was found that modular buildings with RC walls in the center are torsional flexible. Reducing the seismic collector's rigidity can lead to greater force response in the collector. The maximum forces experienced by seismic collectors at the first floor are sensitive to earthquake intensity but not significantly affected by the RC wall strength. Both an increase in the of RC wall strength and earthquake intensity can lead to an increase in the maximum forces experienced by seismic collectors. The method in ASCE 7–16 substantially underestimated the maximum acceleration coefficients at stories below 80 % of the structural height.

Artificial-weathering test requirements for fire-retardant-treated wood to reproduce optimal chemical retention and moisture conditions

Despite the increasing outdoor use of fire-retardant-treated wood, methods for predicting its service life remain poorly established. With the aim of establishing a method to predict chemical losses from fire-retardant-treated (FRT) wood caused by humid atmospheres and rain outdoors, this study examined the preferable conditions for artificial-weathering tests and demonstrated the acceleration coefficients in these tests (i.e., the ratio of equivalent time to reach the same retention of chemicals in artificial weathering and outdoors) based on the EN927-6 standard. To determine the moisture absorption and desorption levels of FRT exposed to outdoor conditions, an outdoor exposure experiment was conducted. The moisture content was higher in the FRT wood than in untreated wood, regardless of the type of coating, and ranged between 11% (in March) and 50% (in September) of the untreated wood’s weight. EN927-6 artificial weathering tests were performed on two groups of specimens with initial moisture contents of 0% and 25%. Retention rates of fire-retardant chemicals after a 2520-h test were compared with those retrieved from 4-year outdoor exposure reported elsewhere. Comparison of these two experiments demonstrated that the acceleration coefficients were 4.1–11.3 in the case of specimens with 0% initial moisture content and 5.1–11.4 in the case of specimens with 25% initial moisture content. The higher initial moisture content produced a more uniform acceleration coefficient. Nevertheless, larger acceleration coefficients were derived from specimens with penetrating or semi-film-forming coatings in both cases. The relationships between the uniformity of this acceleration coefficient and the initial moisture content are discussed from the moisture absorption experiment under constant temperature and humidity and under condensation conditions.

Efficient aerodynamic optimization of turbine blade profiles: an integrated approach with novel HDSPSO algorithm

PurposeThis paper delves into the aerodynamic optimization of a single-stage axial turbine employed in aero-engines.Design/methodology/approachAn efficient integrated design optimization approach tailored for turbine blade profiles is proposed. The approach combines a novel hierarchical dynamic switching PSO (HDSPSO) algorithm with a parametric modeling technique of turbine blades and high-fidelity Computational Fluid Dynamics (CFD) simulation analysis. The proposed HDSPSO algorithm introduces significant enhancements to the original PSO in three pivotal aspects: adaptive acceleration coefficients, distance-based dynamic neighborhood, and a switchable learning mechanism. The core idea behind these improvements is to incorporate the evolutionary state, strengthen interactions within the swarm, enrich update strategies for particles, and effectively prevent premature convergence while enhancing global search capability.FindingsMathematical experiments are conducted to compare the performance of HDSPSO with three other representative PSO variants. The results demonstrate that HDSPSO is a competitive intelligent algorithm with significant global search capabilities and rapid convergence speed. Subsequently, the HDSPSO-based integrated design optimization approach is applied to optimize the turbine blade profiles. The optimized turbine blades have a more uniform thickness distribution, an enhanced loading distribution, and a better flow condition. Importantly, these optimizations lead to a remarkable improvement in aerodynamic performance under both design and non-design working conditions.Originality/valueThese findings highlight the effectiveness and advancement of the HDSPSO-based integrated design optimization approach for turbine blade profiles in enhancing the overall aerodynamic performance. Furthermore, it confirms the great prospects of the innovative HDSPSO algorithm in tackling challenging tasks in practical engineering applications.

Nonlinear crossing strategy-based particle swarm optimizations with time-varying acceleration coefficients

In contemporary particle swarm optimization (PSO) algorithms, to efficiently explore global optimum solutions, it is common practice to set the inertia weight to monotonically decrease over time for stability, while allowing the two acceleration coefficients, representing cognitive and social factors, to adopt decreasing or increasing functions over time, including random variations. However, there has been little discussion on a unified design approach for these time-varying acceleration coefficients. This paper presents a unified methodology for designing monotonic decreasing or increasing functions to construct nonlinear time-varying inertia weight and two acceleration coefficients in PSO, along with a control strategy for exploring global optimum solutions. We first construct time-varying coefficients by linearly amplifying well-posed monotonic functions that decrease or increase over normalized time. Here, well-posed functions ensure satisfaction of specified conditions at the initial and terminal points of the search process. However, many of the functions employed thus far only satisfy well-posedness at either the initial or terminal points of the search time, prompting the proposal of a method to adjust them to virtually meet specified initial or terminal points. Furthermore, we propose a crossing strategy where the developed cognitive and social acceleration coefficients intersect within the search time interval, effectively guiding the search process by pre-determining crossing values and times. The performance of our Nonlinear Crossing Strategy-based Particle Swarm Optimization (NCS-PSO) is evaluated using the CEC2014 (Congress on Evolutionary Computation in 2014) benchmark functions. Through comprehensive numerical comparisons and statistical analyses, we demonstrate the superiority of our approach over seven conventional algorithms. Additionally, we validate our approach, particularly in a drone navigation scenario, through an example of optimal 3D path planning. These contributions advance the field of PSO optimization techniques, providing a robust approach to addressing complex optimization problems.

Reliability Study of Metal-Oxide Semiconductors in Integrated Circuits.

This paper is devoted to the study of CMOS IC parameter degradation during reliability testing. The paper presents a review of literature data on the issue of the reliability of semiconductor devices and integrated circuits and the types of failures leading to the degradation of IC parameters. It describes the tests carried out on the reliability of controlled parameters of integrated circuit TPS54332, such as quiescent current, quiescent current in standby mode, resistance of the open key, and instability of the set output voltage in the whole range of input voltages and in the whole range of load currents. The calculated values of activation energies and acceleration coefficients for different test temperature regimes are given. As a result of the work done, sample rejection tests have been carried out on the TPS54332 IC under study. Experimental fail-safe tests were carried out, with subsequent analysis of the chip samples by the controlled parameter quiescent current. On the basis of the obtained experimental values, the values of activation energy and acceleration coefficient at different temperature regimes were calculated. The dependencies of activation energy and acceleration coefficient on temperature were plotted, which show that activation energy linearly increases with increasing temperature, while the acceleration coefficient, on the contrary, decreases. It was also found that the value of the calculated activation energy of the chip is 0.1 eV less than the standard value of the activation energy.