Test Functions Research Articles

An improved transient search optimization algorithm for building energy optimization and hybrid energy sizing applications

In this paper, a new algorithm named the improved transient search optimization algorithm (ITSOA) is utilized to solve classical test functions, optimize the consumption of building energy, and optimize hybrid energy system production. The conventional TSOA draws inspiration from the fleeting behavior of electrical circuits with energy storage components. Rosenbrock’s direct rotation technique is used to improve the traditional TSOA performance against exploration and exploitation unbalance. First, the ITSOA performance is investigated in solving 23 classical benchmark functions, and the outcomes have shown the superior capability of the recommended algorithm in comparison with the conventional TSOA, DMO, SHO, GA, MRFO, and PSO methods. Also, the ITSOA proficiency is verified in solving the building energy optimization (BEO) problem for minimizing the energy usage of two simple and detailed buildings. The optimization results showed that the optimized solutions of ITSOA in single and multi-objective optimizations compared to conventional TSOA, DMO, SHO, GA, MRFO, and PSO obtained a lower value of the cost function. Also, the superiority of ITSOA has been confirmed to solve the BEO compared to previous methods. Moreover, the multi-objective optimization results have shown that ITSOA is able to determine the ultimate solution among the Pareto front set based on the fuzzy decision-making approach and building energy utilization decisions.

Optimizing [formula omitted]-coverage in energy-saving wireless sensor networks based on the Elite Global Growth Optimizer

For energy-constrained wireless sensor networks, enhancing network fault tolerance and extending lifetime are of paramount importance. In this study, we focus on addressing the Minimum Power k-Coverage (MinPKC) problem. The objective of MinPKC is to minimize and balance the transmission (Tx) power by sensor nodes (SNs), thus extending network lifetime. Furthermore, MinPKC requires satisfying the constraints of k-coverage for the targets to enhance network fault tolerance. The problem is non-convex, multimodal, and NP-hard. To address this problem, we propose a novel metaheuristic algorithm named Elite Global Growth Optimizer (EGGO), which incorporates efficient global search and elite evolution strategies into the GO and utilizes time-varying multi-strategies for population updates. This study extensively compares EGGO with ten popular algorithms on 20 classic benchmark test functions using the Friedman and Wilcoxon rank-sum tests. The results indicate that EGGO significantly outperforms all the compared algorithms. Ablative experiments confirm the crucial role of the proposed mechanisms in EGGO for improving algorithm performance. Meanwhile, the study implements ten MinPKC methods based on comparative algorithms and compares them with the EGGO-based MinPKC method in terms of total Tx power, network lifetime, and simulation time. The experimental results indicate that the EGGO-based MinPKC method significantly increases the network lifetime by approximately 14.2%, 38.2%, and 1.9% compared to the second-best algorithm, and by 57%, 451%, and 84.8% compared to the GO, while balancing the Tx power among SNs and reducing the total Tx power by approximately 28%, 12.7%, and 0.5% compared to the second-best algorithm, and by 47.1%, 72%, and 30% compared to the GO, respectively, when k=1,2,3. Additionally, a detailed analysis is conducted on the impact of the number of SNs, the maximum target coverage capacity of each SN, as well as the effects of obstacles and their distribution on signal strength attenuation in reducing Tx power. The source code is available at https://github.com/iNet-WZU/EGGO.

Improved multi-objective differential evolution algorithm and its application in the capacity configuration of urban rail photovoltaic hybrid energy storage systems

With the rapid expansion of urban rail transit, energy demand is continuously increasing. Integrating photovoltaic (PV) systems into hybrid energy storage systems (HESS) to form a rail transit PV hybrid energy storage system (RTPHESS) is an effective energy-saving and emission reduction measure. However, the integration changes the energy composition of the rail power supply system, making traditional HESS capacity configuration schemes inadequate to meet the stability requirement. In light of this, an RTPHESS model was established aiming at suppressing traction network voltage fluctuations and minimizing the total life-cycle cost of HESS. The model introduces traction network voltage fluctuation rate and HESS total life-cycle cost as evaluation objectives. Based on this, an improved multi-objective differential evolution algorithm (IMODE) was proposed to optimize HESS capacity. Optimal point set theory was used for population initialization to enhance the uniformity and diversity of the initial population, thereby improving global optimization capabilities. During the mutation phase, a hybrid Gaussian-Cauchy mutation strategy was introduced to improve both global and local search capabilities. The proposed algorithm's effectiveness was evaluated using IGD, SP, MS, and ER metrics on eight benchmark test functions with different characteristics, and compared with SaMODE_LS, MODE, MOPSO, MOALO, and MOMGA. Finally, IMODE was applied to the capacity configuration of RTPHESS. The results demonstrate that IMODE outperforms traditional algorithms in terms of accuracy and stability, reducing traction network voltage fluctuations by approximately 4.1 % and lowering the total life-cycle cost to 3,201,100 yuan.

Performance Analysis of Metaheuristic Algorithms on Benchmark Functions

The discipline of optimization can be used to maximize or minimize several problems. The use of metaheuristic algorithms is a strategy that often works well for global optimization. They are a type of stochastic algorithm that, via trial and error, finds workable solutions to difficult optimization problems in a reasonable amount of time, but they do not provide assurance that the answers are optimal. This paper aims to offer a comparative analysis of several metaheuristics in searching for the optimal solution. The selected metaheuristics are Artificial Bee Colony, Ant Lion Optimizer, Bat, Black Hole, Cuckoo Search, Cat Swarm Optimization, Dragonfly, Differential Evolution, Firefly, Genetic, Gravitational-Based Search, Grasshopper Optimization, Grey Wolf Optimizer, Harmony Search, Krill-Herd, Moth-Flame Optimizer, Particle Swarm Optimization, Sine Cosine, Shuffled Frog-Leaping, and Whale Optimization algorithms. For this evaluation, 18 benchmark test functions, categorized as unimodal, multimodal, and fixed-dimension multimodal are used to examine various properties, such as accuracy, escape from the local optimum, and convergence. As an indicator of how effectively these metaheuristics work, metrics like minimum, maximum, average, and standard deviation of fitness are provided. There are no optimization algorithms that are adequate for all problems, as the No Free Lunch theorem suggests, but the metaheuristics that are more effective than the others will be demonstrated. This study could be helpful for young researchers to identify the most prominent metaheuristics for achieving a better global optimum. Received: 8 June 2024 / Accepted: 25 July 2024 / Published: 29 July 2024Optimization, Metaheuristic algorithm, Benchmark function, Performance metric

Hybrid game theoretic strategy for optimal relay selection in energy harvesting cognitive radio network

SummaryRelay selection plays a crucial role in enhancing the performance of wireless networks particularly in the context of cognitive radio (CR) systems with energy harvesters. In this paper, we propose a novel approach, namely, CGAPSO Shapley, for the best relay selection while simultaneously optimizing the parameters of signal‐to‐interference‐plus‐noise ratio (SINR), throughput, and outage probability. The CGAPSO Shapley algorithm combines the Shapley value, a cooperative game theory concept, with cellular genetic algorithm particle swarm optimization (CGAPSO) to achieve effective and efficient optimization of relay selection. The CGAPSO framework provides a hybrid structure that integrates cellular genetic algorithm (CGA) and particle swarm optimization (PSO), enabling simultaneous evolution of the population and particles within cells. The incorporation of the Shapley value and the hybrid CGAPSO framework enables effective exploration of the solution space and provides decision‐makers with comprehensive insights for relay selection. By utilizing the Shapley value, we assign weights to the relay nodes based on their contributions to the overall optimization objectives, considering their CR capabilities and energy harvesting capabilities. Some benchmark test functions are used to compare the hybrid algorithm with both the standard CGAPSO, Particle swarm optimization gravitational search algorithm (PSOGSA) and PSO algorithms in evolving best solution. The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard algorithms. The novel CGAPSO Shapley approach achieves an outage probability of 0.323324, marking a significant improvement of 60% over the outage probability achieved with conventional approach.

Differential Evolution Algorithm with Three Mutation Operators for Global Optimization

Differential evolution algorithm is a very powerful and recently proposed evolutionary algorithm. Generally, only a mutation operator and predefined parameter values of differential evolution algorithm are utilized to solve various optimization problems, which limits the performance of the algorithm. In this paper, six commonly used mutation operators are divided into three categories according to their own features. A mutation pool is established based on the three categories. A parameter pool with three predefined values is designed. During evolution, three mutation operators are randomly chosen from the three categories, and three parameter values are also randomly selected from the parameter pool. The three groups of mutation operators and parameter values are employed to produce trial vectors. The proposed algorithm makes good use of different mutation operators. Three recently proposed differential evolution variants and three non-differential evolution algorithms are used to make comparisons on the 29 testing functions from CEC. The experimental results have demonstrated that the proposed algorithm is very competitive. The proposed algorithm is utilized to solve three real applications, and the results are superior.

Considering radial basis function neural network for effective solution generation in metaheuristic algorithms

In many engineering optimization problems, the number of function evaluations is severely limited by the time or cost constraints. These limitations present a significant challenge in the field of global optimization, because existing metaheuristic methods typically require a substantial number of function evaluations to find optimal solutions. This paper presents a new metaheuristic optimization algorithm that considers the information obtained by a radial basis function neural network (RBFNN) in terms of the objective function for guiding the search process. Initially, the algorithm uses the maximum design approach to strategically distribute a set of solutions across the entire search space. It then enters a cycle in which the RBFNN models the objective function values from the current solutions. The algorithm identifies and uses key neurons in the hidden layer that correspond to the highest objective function values to generate new solutions. The centroids and standard deviations of these neurons guide the sampling process, which continues until the desired number of solutions is reached. By focusing on the areas of the search space that yield high objective function values, the algorithm avoids exhaustive solution evaluations and significantly reduces the number of function evaluations. The effectiveness of the method is demonstrated through a comparison with popular metaheuristic algorithms across several test functions, where it consistently outperforms existing techniques, delivers higher-quality solutions, and improves convergence rates.

Application of uniform experimental design theory to multi-strategy improved sparrow search algorithm for UAV path planning

The sparrow search algorithm (SSA) is a meta-heuristic optimization algorithm based on the predatory behavior of sparrows. However, SSA tends to fall into the local optimum when solving optimization problems with complex constraints. To improve its optimization efficiency and overall performance, this paper develops a multi-strategy improved SSA (ISSA) based on uniform experimental design theory. Specifically, the wrap-around L2-discrepancy (WD), as a uniformity metric for uniform design of experiments, is fully utilized with macro-regulation, adaptive dynamic management strategy, and boundary redistribution management mechanism to quantify the population uniformity in each iteration of ISSA. Inspired by the concept of uniform design, WD is initially adopted to gauge population uniformity, and the threshold acceptance (TA) algorithm is employed to produce an initial population with improved uniformity, hence augmenting the population’s diversity and quickening the rate of convergence. Secondly, a macroscopical individual iterative strategy of the producer is adjusted to avoid the population converging to the origin. Then, a dynamic population uniformity affiliation function based on WD is introduced to adjust the population uniformity affiliation function according to the relative amount of change in the global optimum, and the number of danger perceivers is adjusted according to the affiliation function. What is more, a new boundary update strategy is also proposed in ISSA based on population uniformity. By comparing ISSA on 23 standard test functions and recently updated validation function set CEC2022 with the original SSA, and some classical as well as newly developed algorithms, the superiority of the present ISSA is thoroughly confirmed. As an application case, the ISSA algorithm is utilized to solve the path planning problem of the complex environment of unmanned aerial vehicle (UAV) based on threat models by applying it to 2D maps containing circular and polygonal obstacles, as well as 3D maps containing mountain peaks and cylindrical obstacles. The simulation results show that ISSA can find more effective routes through various environments with obstacles.

Parameter optimization of the simulated moving bed system based on the IMOSCSO algorithm

AbstractThe optimization of operating parameters for the simulated moving bed (SMB) is complex. A parameter optimization method for the SMB system was proposed based on the improved multi‐objective sand cat swarm optimization (IMOSCSO) algorithm. The multi‐objective sand cat swarm optimization (MOSCSO) algorithm integrated the update and selection mechanism of the repository in the multi‐objective algorithm. Three strategies were proposed to improve the traditional MOSCSO algorithm for increased population diversity, global search capability, and convergence speed. First, the cubic chaotic map was used to initialize the population, which improved the uniformity of the population distribution. Second, including a variable spiral search strategy in the prey search phase enabled the sand cat swarm to explore more search paths to adjust its position. Third, the convergence speed was enhanced by incorporating the alert mechanism of the sparrow search algorithm. The improved algorithm was tested with standard test functions. The IMOSCSO algorithm outperformed other algorithms in terms of convergence and accuracy. Finally, the IMOSCSO algorithm optimized the system parameters of the SMB, demonstrating its practical applications.

Porosity Prediction Based on Ensemble Learning for Feature Selection and an Optimized GRU Improved by the PSO Algorithm

Accurate and reliable prediction of porosity forms the foundational basis for evaluating reservoir quality, which is essential for the systematic deployment of oil and gas exploration and development plans. When data quality of samples is low, and critical model parameters are typically determined through subjective experience, resulting in diminished accuracy and reliability of porosity prediction methods utilizing gated recurrent units (GRU), a committee-voting ensemble learning (EL) method, and an enhanced particle swarm optimization (PSO) algorithm are proposed to optimize the GRU-based porosity prediction model. Initially, outliers are eliminated through box plots and the min–max normalization is applied to enhance data quality. To address issues related to model accuracy and high training costs arising from dimensional complexity, substantial noise, and redundant information in logging data, a committee-voting EL strategy based on four feature selection algorithms is introduced. Following data preprocessing, this approach is employed to identify logging parameters highly correlated with porosity, thereby furnishing the most pertinent data samples for the GRU model, mitigating constraints imposed by single-feature selection methods. Second, an improved PSO algorithm is suggested to tackle challenges associated with low convergence accuracy stemming from random population initialization, alongside the absence of global optimal solutions due to overly rapid particle movement during iteration. This algorithm uses a good-point set for population initialization and incorporates a compression factor to devise an adaptive velocity updating strategy, thereby enhancing search efficacy. The enhanced PSO algorithm’s superiority is substantiated through comparison with four alternative swarm intelligent algorithms across 10 benchmark test functions. Ultimately, optimal hyper-parameters for the GRU model are determined using the improved PSO algorithm, thereby minimizing the influence of human factors. Experimental findings based on approximately 15,000 logging data points from well A01 in an operational field validate that, relative to three other deep learning methodologies, the proposed model proficiently extracts spatiotemporal features from logging data, yielding enhanced accuracy in porosity prediction. The mean squared error on the test set was 7.19 × 10–6, the mean absolute error stood at 0.0082, and coefficient of determination reached 0.99, offering novel insights for predicting reservoir porosity.

Rogue wave, lump, kink, periodic and breather-like solutions of the (2+1)-dimensional KdV equation

In this paper, the ([Formula: see text])-dimensional KdV equation is investigated by using the bilinear neural network method (BNNM). We construct six neural network models, extending beyond single hidden layer models to create deeper and broader network structures (e.g. [3-3-1], [3-4-1], [3-1-3-1], [3-4-1-1], [3-2-2-1] and [3-2-3-1-1] models). Introducing specific activation functions into the neural network model enables the generation of various test functions, resulting in novel solutions for equations that include rogue wave solutions, lump-kink solutions, periodic soliton solution, breather-like solutions and lump solutions. The physical properties of these novel solutions are vividly depicted through three-dimensional plots, density plots, and curve plots. The findings contribute to a better understanding of the propagation behavior of shallow water waves.

Method of Equivalent Error as a Criterion of the Assessment of the Algorithms Used for Estimation of Synchrophasor Parameters Taken from the Power System.

The development of digital techniques in control engineering leads to the creation of innovative algorithms for measuring specific parameters. In the field of electric power engineering these parameters may be amplitude, phase and frequency of voltage or current occurring in the analyzed electric grid. Thus, the algorithms mentioned, applied in relation to the quoted parameters, may provide precise and reliable measurement results in the electric grid as well as ensure better grid monitoring and security. Signal analysis regarding its identification due to the type of interference is very difficult because the multitude of information obtained is very large. In order to indicate the best method for determining errors in measuring synchronous parameters of the measured current or voltage waveforms, the authors propose in this paper a new form of one error for all testing functions, which is called an equivalent error. This error is determined for each error's value defined in the applicable standards for each of selected 15 methods. The use of the equivalent error algorithm is very helpful in identifying a group of methods whose operation is satisfactory in terms of measurement accuracy for various types of disturbances (both in the steady state and in the dynamic state) that may occur in the power grid. The results are analyzed for phasor measurement unit (PMU) devices of class P (protection) and M (measurement).

Harmonic Mean Optimizer (HMO) for global problems solving

This study introduces the Harmonic Mean Optimizer (HMO), a novel meta-heuristic algorithm designed to address complex global optimization problems. The primary objective of this research is to develop an optimization technique that effectively balances exploration and exploitation without requiring extensive parameter tuning, thereby simplifying the optimization process and enhancing its robustness across various problem domains. The HMO employs a unique dual-fitness index that leverages the harmonic mean to assess both the quality and diversity of candidate solutions dynamically. This approach ensures a balanced search process that avoids premature convergence and improves the ability to find global optima. The methodology involves extensive benchmarking of the HMO against a comprehensive set of test functions, including 23 traditional functions from the Congress on Evolutionary Computation (CEC) 2017 test suite. The performance of the HMO is compared with that of established meta-heuristic algorithms, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to validate its efficacy in terms of convergence speed and solution accuracy. Additionally, the HMO is applied to several real-world engineering problems to demonstrate its practical utility. The results show that the HMO consistently outperforms the benchmark algorithms, achieving faster convergence and higher accuracy in finding optimal solutions across a diverse range of test problems. In practical applications, the HMO effectively optimized complex engineering tasks, achieving significant improvements in both solution quality and computational efficiency. These findings highlight the potential of the HMO as a versatile and powerful tool for global optimization tasks. the HMO offers a significant advancement in optimization methodologies by providing a robust, parameter-free approach that effectively balances exploration and exploitation. This study underscores the HMO’s applicability to a wide range of optimization challenges and sets the foundation for future research and development in enhancing and applying this innovative algorithm to more complex and diverse problem domains.

Relationship between quantitative physical activity and deterioration of locomotive function: a cross-sectional study using baseline data from a cohort

BackgroundIn aged society, health policies aimed at extending healthy life expectancy are critical. Maintaining physical activity is essential to prevent the deterioration of body functions. Therefore, it is important to understand the physical activity levels of the target age group and to know the content and intensity of the required physical activity quantitatively. Especially we focused the role of non-exercise activity thermogenesis and sedentary time, which are emphasized more than the introduction of exercise in cases of obesity or diabetes.MethodsA total of 193 patients from 25 institutions were included. Participants underwent a locomotive syndrome risk test (stand-up test, 2-step test, and Geriatric Locomotive Function Scale-25 questionnaire) and were classified into three stages. Physical activity was quantitatively monitored for one week with 3-axial accelerometer. Physical activity was classified into three categories; (1) Sedentary behavior (0 ∼ ≤ 1.5 metabolic equivalents (METs)), (2) Light physical activity (LPA:1.6 ∼ 2.9 METs), and (3) Moderate to vigorous physical activity (MVPA: ≥3 METs). We investigated the relationship between physical activity, including the number of steps, and the stages after gender- and age- adjustment. We also investigated the relationship between social isolation using Lubben’s Social Network Scale (LSNS), as social isolation would lead to fewer opportunities to go out and less outdoor walking.ResultsComparison among the three stages showed significant difference for age (p = 0.007) and Body Mass Index (p < 0.001). After gender-and age-adjustment, there was a significant relation with a decrease in the number of steps (p = 0.002) and with MVPA. However, no relation was observed in sedentary time and LPA. LSNS did not show any statistically significant difference. Moderate to high-intensity physical activity and the number of steps is required for musculoskeletal disorders. The walking, not sedentary time, was associated to the locomotive stages, and this finding indicated the importance of lower extremity exercise.ConclusionsAdjusting for age and gender, the number of steps and moderate to vigorous activity levels were necessary to prevent worsening, and there was no effect of sedentary behavior. Merely reducing sedentary time may be inadequate for locomotive disorders. It is necessary to engage in work or exercise that moves lower extremities more actively.

Optimization of the different controller parameters via OBL approaches based artificial ecosystem optimization involving fitness distance balance guiding mechanism for efficient motor speed regulation of DC motor

This study proposes a new optimization approach, which is called as artificial ecosystem optimization algorithm with fitness-distance balance guiding mechanism by using opposite based learning methods (FDBAEO_OBLs) for the speed regulation of direct current (DC) motor. The performance of the proposed FDBAEO_OBL algorithm is tested in two different experimental studies. In the first experimental study, the proposed approach is tested in the CEC2020 benchmark test functions and the FDBAEO algorithm, which included the best OBL approach, is determined using non-parametric Wilcoxon and Friedman statistical analysis methods. Second, the parameters of proportional integral derivative (PID), tilt integral derivative (TID), proportional integral derivative with filter (PIDF), tilt integral derivative with filter (TIDF), fractional-order proportional integral derivative (FOPID), fractional-order proportional integral derivative with filter (FOPIDF), proportional integral derivative with fractional-order filter (PIDFF) and fractional-order proportional integral derivative with fractional-order filter (FOPIDFF) controller structures to be used in DC motor closed loop speed control are determined with FDBAEO_OBL, and the performances of the controllers are investigated. Integral absolute error (IAE), integral time absolute error (ITAE), integral time squared error (ITSE) and integral squared error (ISE) performance indices are used as the objective function of the operation process in which the control parameters are determined. According to the comparative step response results of the controller structures, the four best controller structures for DC motor speed regulation are determined. The performances of these controllers are examined under different simulation conditions and according to the results obtained, it is seen that the best controller structure is FOPIDFF. The FDBAEO_OBL algorithm, which is used in both benchmark test functions and DC motor speed regulation, shows an effective, durable and superior performance in finding the optimal solution values during the optimization.

A Multi-strategy Improved Grasshopper Optimization Algorithm for Solving Global Optimization and Engineering Problems

This paper presents a multi-strategy improved grasshopper optimization algorithm (MSIGOA), which aims to address the shortcomings of the grasshopper optimization algorithm (GOA), including its slow convergence, vulnerability to trapping into local optima, and low accuracy. Firstly, to improve the uniformity of the population distribution in the search space, the MSIGOA uses circle mapping for the population initialization. A nonlinear decreasing coefficient is utilized instead of an original linear decreasing coefficient to improve the local exploitation and global exploration capabilities. Then, the modified golden sine mechanism is added during the position update stage to change the single position update mode of GOA and enhance the local exploitation capability. The greedy strategy is added to greedily select the new and old positions of the individual to retain a better position and increase the speed of convergence. Finally, the quasi-reflection-based learning mechanism is utilized to construct new populations to improve population multiplicity and the capability to escape from the local optima. This paper verifies the efficacy of MSIGOA by comparing it with other advanced algorithms on six engineering design problems, CEC2017 test functions, and 12 classical benchmark functions. The experimental results show that MSIGOA performs better than the original GOA and other compared algorithms and has stronger comprehensive optimization capabilities.

Application of artificial bee colony algorithm based on homogenization mapping and collaborative acquisition control in network communication security.

In order to optimize the spectrum allocation strategy of existing wireless communication networks and improve information transmission efficiency and data transmission security, this study uses the independent correlation characteristics of chaotic time series to simulate the collection and control strategy of bees, and proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection control. Furthermore, it proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection and control. The method begins by establishing a composite system of uniformly distributed Chebyshev maps. In the neighborhood intervals where the nectar sources are firmly connected and relatively independent, the algorithm then conducts a chaotic traversal search. The research results demonstrated the great performance of the suggested algorithm in each test function as well as the positive effects of the optimization search. The network throughput rate was over 300 kbps, the quantity of security service eavesdropping was below 0.1, and the spectrum utilization rate of the algorithm-based allocation method could be enhanced to 0.8 at the most. Overall, the performance of the proposed algorithm outperformed the comparison algorithm, with high optimization accuracy and a significant amount of optimization. This is favorable for the efficient use of spectrum resources and the secure transmission of communication data, and it encourages the development of spectrum allocation technology in wireless communication networks.

Cerebral arterial blood flow, attention, and executive and cognitive functions in depressed patients after acute hypertensive cerebral hemorrhage.

Intracerebral hemorrhage mainly occurs in middle-aged and elderly patients with hypertension, and surgery is currently the main treatment for hypertensive cerebral hemorrhage, but the bleeding caused by surgery will cause damage to the patient's nerve cells, resulting in cognitive and motor dysfunction, resulting in a decline in the patient's quality of life. To investigate associations between cerebral arterial blood flow and executive and cognitive functions in depressed patients after acute hypertensive cerebral hemorrhage. Eighty-nine patients with depression after acute hypertensive cerebral hemorrhage who were admitted to our hospital between January 2019 and July 2021 were selected as the observation group, while 100 patients without depression who had acute hypertensive cerebral hemorrhage were selected as the control group. The attention span of the patients was assessed using the Paddle Pin Test while executive function was assessed using the Wisconsin Card Sorting Test (WCST) and cognitive function was assessed using the Montreal Cognitive Assessment Scale (MoCA). The Hamilton Depression Rating Scale (HAMD-24) was used to evaluate the severity of depression of involved patients. Cerebral arterial blood flow was measured in both groups. The MoCA score, net scores I, II, III, IV, and the total net score of the scratch test in the observation group were significantly lower than those in the control group (P < 0.05). Concurrently, the total number of responses, number of incorrect responses, number of persistent errors, and number of completed responses of the first classification in the WCST test were significantly higher in the observation group than those in the control group (P < 0.05). Blood flow in the basilar artery, left middle cerebral artery, right middle cerebral artery, left anterior cerebral artery, and right anterior cerebral artery was significantly lower in the observation group than in the control group (P < 0.05). The basilar artery, left middle cerebral artery, right middle cerebral artery, left anterior cerebral artery, and right anterior cerebral artery were positively correlated with the net and total net scores of each part of the Paddle Pin test and the MoCA score (P < 0.05), and negatively correlated with each part of the WCST test (P < 0.05). In the observation group, the post-treatment improvement was more prominent in the Paddle Pin test, WCST test, HAMD-24 score, and MoCA score compared with those in the pre-treatment period (P < 0.05). Blood flow in the basilar artery, left middle cerebral artery, right middle cerebral artery, left anterior cerebral artery, and right anterior cerebral artery significantly improved in the observation group after treatment (P < 0.05). Impaired attention, and executive and cognitive functions are correlated with cerebral artery blood flow in patients with depression after acute hypertensive cerebral hemorrhage and warrant further study.

Flavoring search algorithm with applications to engineering optimization problems and robot path planning

In this paper, a human-based meta-heuristic algorithm, the Flavoring Search Algorithm, is proposed and mathematically modeled with the aim of providing an alternative optimization method for solving practical engineering problems. Flavoring Search Algorithm is inspired by the human behavior of flavoring in everyday life, including basic flavoring, formal flavoring, and auxiliary flavoring. By introducing a unique taste factor, it not only succeeded in making the Flavoring Search Algorithm corresponds to the real flavoring process but also balanced the exploration and exploitation of the algorithm. With the help of the taste factors, Flavoring Search Algorithm performs basic flavoring (initial flavoring and random flavoring) in the exploration phase and formal flavoring and auxiliary flavoring in the exploitation phase. In addition, theoretical analysis and experiments have led to the conclusion that the taste factor can be used as an effective and practical new threshold conversion mechanism for meta-heuristic algorithms. This study also establishes a Markov model to rigorously analyze the Flavoring Search Algorithm as a globally convergent algorithm from a mathematical point of view. Through experimental and analytical comparisons with other excellent optimizers on 30 test functions, as well as on 3 real-world engineering design problems and 1 path planning problem. The results show that the Flavoring Search Algorithm generally outperforms the tested competitors in solving benchmark functions and engineering problems, validating the utility of the proposed optimizer in solving challenging real-world problems.

Optimization research on energy consumption and carbon emissions in hydrogen-blended natural gas pipeline networks considering compressor performance - A case study of the west-east gas pipeline (line 1)

The blending of hydrogen into natural gas can utilize existing natural gas pipeline infrastructure to achieve long-distance, large-scale, and cost-effective hydrogen transportation. However, the introduction of hydrogen poses a series of new technical and safety challenges to the operation of natural gas pipelines. Addressing these issues, this paper focuses on minimizing compressor station energy consumption and carbon emissions as objective functions, conducting a comprehensive analysis of station operation modes. Incorporating the characteristics of the model, the soft frost search strategy, the hard frost penetration mechanism, and the positive greedy selection mechanism are introduced to propose the IRIME algorithm. Comparisons are made with well-performing optimization algorithms using classical test functions to evaluate algorithm performance. To further validate the algorithm's accuracy, the West-East Gas Pipeline (Line 1) is considered, comparing the convergence speed and precision of the IRIME algorithm, leading to its selection for solving the model. Furthermore, consider analyzing the impact of different hydrogen blending ratios on the performance curves and operating points of compressors. Results indicate that compared to conditions without hydrogen blending, the compressor outlet temperature and pressure drop increase for hydrogen blending ratios of 5%, 10%, 15%, and 20%; as the hydrogen blending ratio increases, the efficiency of fuel-driven compressors, efficiency of electrically driven compressors, and average efficiency of compressor units decrease; with an increase in hydrogen blending ratio, carbon emissions from fuel-driven compressors and electric-driven compressors both decrease. In conclusion, with an increasing hydrogen blending ratio, total energy consumption increases by 3.14%, 5.69%, 8.68%, and 10.90%, while total carbon emissions decrease by 2.99%, 5.36%, 8.32%, and 10.68%. These findings provide valuable insights for operational guidance in the field.