Initial Population Distribution Research Articles

Sound Insulation Characteristics of Sandwich Thin-Plate Acoustic Metamaterial: Analysis and Optimization

Addressing the demand for low-frequency noise control in ships and aiming to overcome the limitations imposed by the mass law of traditional materials, a sandwich thin-plate acoustic metamaterial (SPAM) was developed in this study. This structure is characterized by its simplicity and superior sound insulation performance. A sound insulation test was conducted to validate the exceptional low-frequency acoustic suppression capability of SPAM, and the effectiveness of the analytical model was also demonstrated. Subsequently, based on this model, a sensitivity analysis of the sound insulation characteristics of the SPAM relative to structural parameters was carried out. For the optimization process, the Logistic-Sine fusion chaotic mapping was employed to refine the dung beetle optimizer (DBO), enhancing the initial population distribution. The improved dung beetle optimizer (IDBO) was then used to further optimize and elevate the sound insulation performance of the SPAM. The outcomes indicate that the IDBO boasts superior optimization proficiency and accelerated convergence, while the optimized SPAM exhibits more remarkable sound insulation capabilities. Within the frequency range of 100–315[Formula: see text]Hz, the average sound transmission loss (STL) has increased by 11.94[Formula: see text]dB when compared to the pre-optimization values.

Combined improved tuna swarm optimization with graph convolutional neural network for remaining useful life of engine

AbstractAccurate prediction of the engine's remaining useful life (RUL) is essential to ensure the safe operation of the aircraft because. However, traditional deep‐learning based methods for RUL prediction has been limited by interpretability and adjustment for hyperparameters in practical applications due to the intricate potential relations during the degradation process. To address these dilemmas, an improved multi‐strategy tuna swarm optimization‐assisted graph convolutional neural network (IMTSO‐GCN) is developed to achieve RUL prediction in this work. Specifically, mutual information is used to describe potential relationships among measured parameters so that they could be utilized in building edges for these parameters. Besides, considering that not all relational nodes will positively affect the RUL prediction and the inherent hyperparameters of the GCN are high‐dimensional. Inspired by “No Free Lunch (NFL)”, IMTSO is proposed to reduce the optimization cost of hyperparameters, in which cycle chaotic mapping is employed to achieve initialization of the population for improving the uniformity of the initial population distribution. Besides, a novel adaptive approach is proposed to enhance the exploration and exploitation of tuna swarm optimization (TSO). The CMAPSS dataset was used to validate the effectiveness and advancedness of IMTSO‐GCN, and the experimental results show that the R2 of the IMTSO‐GCN is greater than 0.99, RMSE is less than 3, the Score error is within 1.

Real-Time Optimal Scheduling of a Water Diversion System Using an Improved Wolf-Pack Algorithm and Scheme Library

A water diversion system (WDS) with cascade pumping stations (CPSs) plays an important role in the application of water resources. However, high energy consumption is reported due to unreasonable scheduling schemes and long decision times. Herein, this paper presents a new method to achieve optimal scheduling schemes effectively, including the head allocation of CPSs, the number of running pumps, and pump blade angles. A double-layer mathematical model for a WDS was established with the goal of achieving minimal energy consumption, considering the constraints of flow rate, water level, and the characteristics of pump units. The inner-layer model was used to obtain scheduling schemes of single-stage pumping stations, as well as the water levels and flow rates of water channels, while the outer-layer model was used to optimize inter-stage head allocation. An improved wolf-pack algorithm (IWPA) was proposed to solve the model, using a Halton sequence to obtain the uniform initial population distribution and introducing simulated annealing (SA) to improve the global searchability. Moreover, an idea for a pre-established scheme library was suggested for inner-layer models to obtain the solutions in real time with less calculation workload. Taking an actual project as a case, in contrast with the actual schemes, the optimal scheduling method could result in energy savings of 14.37–20.39%, a CO2 emission reduction of 13–32 tons per day, and water savings of 0.14–18.34%. Moreover, the time complexity decreased to square order, and the CPU time of the optimal method was about 1% that of the traditional method. This study provides an efficient method for the high-value utilization of energy and water resources for a WDS.

Assessment of loss of life owing to dam-failure flooding considering population distribution and evacuation

Dam failures pose a serious threat to the safety of downstream populations. Scientifically assessing loss of life (LOL) from dam-failure flooding contributes to the emergency response. Population distribution varies spatially and temporally even within a day, and has different information dissemination and response speeds, which impacts the chances of safe evacuation. This study established an LOL assessment model that considers the initial population distribution and population evacuation process. Shared bike big data and other multi-source data were used to identify daily population activity. The evacuation process includes receiving warnings, responses, and horizontal or vertical evacuation choices, and the evacuation results were then evaluated. This model was applied to a dam in China. During nighttime, commuting, and working hours, the issuance of warnings 214 min, 137 min, and 0 min prior to dam failure, respectively, significantly mitigated LOL. Under the same communication and response speeds, the commuting period poses the highest risk of LOL. Assessments of LOL due to dam failure flooding need to consider the impact of the timing of dam failure and population distribution. The model used in this study proposes varying effective early warning times based on the time of dam failure occurrence, which can contribute to effective dam risk management, helping prevent personnel loss.

Capacity optimization configuration of multi-source independent microgrids based on TLC-GOLD-GWO algorithm

Abstract To guarantee the economic efficiency and reliability of standalone microgrids, this paper introduces an enhanced Grey Wolf optimization algorithm (GWO) [1] that leverages chaos mapping techniques, nonlinear convergence factor, and golden operator for solving multi-source capacity allocation problems. Taking the reliability of the power supply as a significant constraint, an optimization model is constructed with the minimization of the annual total economic cost as the objective function. Given the issues of local convergence and premature convergence encountered during the solution process of the fundamental Grey Wolf optimization algorithm, we employ chaotic mapping technology to address the challenge of unequal initial population distribution. Subsequently, a nonlinear convergence factor is introduced to expedite the convergence rate, enhancing the algorithm’s performance. Lastly, to update the grey wolf’s position, the golden sine operator is used, thereby refining the solution space and enhancing the effectiveness of the optimization process. The simulation outcomes reveal that the enhanced Grey Wolf optimization algorithm notably enhances the precision of the objective function, delivering superior performance. Compared to the Grey Wolf optimization algorithm and Whale optimization algorithm (WOA) [2], the enhanced algorithm demonstrates superiority in terms of reducing economic costs, enhancing the capability for global optimal search, and accelerating the optimization process.

A multi-strategy improved sparrow search algorithm for mobile robots path planning

Abstract Path planning for mobile robots plays a vital role in task execution, given the constraints imposed by environments and energy resources. It poses a significant challenge for mobile robots, requiring them to find a feasible path between the start point and target point that is obstacle-free and as short as possible. To address the challenge of path planning, a multi-strategy improved sparrow search algorithm with crazy operator (CMISSA) is proposed. Firstly, Tent chaos mapping and reverse learning are introduced into the population initialization of sparrow search algorithm (SSA) to enhance the uniformity and effectiveness of the initial population distribution. Secondly, adaptive parameters are applied in SSA to maintain a balance between exploitation and exploration. Thirdly, to prevent SSA from getting trapped in local optima, the crazy operator is used to perturb the population position. Finally, a novel adaptive boundary control strategy is introduced to handle the location of individuals that have crossed the boundary. In addition, the experimental results on 15 classical benchmark functions show that CMISSA has better optimization performance than other 10 comparison algorithms. Furthermore, in the path planning experimental results, the results of comparing CMISSA with 5 comparison algorithms on 5 different environments reveal CMISSA's average path shortening rates were 34.90%, 20.11%, 29.01%, 51.97%, 37.42%, respectively. It is further demonstrated that CMISSA has superior availability for solving mobile robots path planning.

The role of labor discrimination in spatial sorting: the USA as an example of ethnic groups staying apart

AbstractDespite the vast research about discrimination, there is little evidence about how space interacts with it. Our main hypothesis is that a discriminated group could have incentives to stay together, even if the location is less dynamic—avoiding areas where firms do not usually hire workers of their group. A virtuous and/or vicious circle emerges for each group, even in the long run. Using USA as an example, this article introduces a theoretical economic model to explain the incentives of ethnic groups in terms of location. We extend the well-known model of discrimination with imperfect information to a spatial framework. The results seem to indicate that the initial population distribution and the barriers to agglomerate activity (transport costs), as well as the behavior of employees, are key elements to determine the equilibrium. As a general conclusion, discrimination processes could clearly modify the location pattern of the population. Hence, the discriminated group could suffer from lower incentives to move from non-agglomerated areas to dynamic areas. These processes could help to explain why some populations prefer to maintain their traditional location in poor areas even if there are places with higher wages or better quality of life.

Physics-Informed Graph Neural Operator for Mean Field Games on Graph: A Scalable Learning Approach

Mean-field games (MFGs) are developed to model the decision-making processes of a large number of interacting agents in multi-agent systems. This paper studies mean-field games on graphs (G-MFGs). The equilibria of G-MFGs, namely, mean-field equilibria (MFE), are challenging to solve for their high-dimensional action space because each agent has to make decisions when they are at junction nodes or on edges. Furthermore, when the initial population state varies on graphs, we have to recompute MFE, which could be computationally challenging and memory-demanding. To improve the scalability and avoid repeatedly solving G-MFGs every time their initial state changes, this paper proposes physics-informed graph neural operators (PIGNO). The PIGNO utilizes a graph neural operator to generate population dynamics, given initial population distributions. To better train the neural operator, it leverages physics knowledge to propagate population state transitions on graphs. A learning algorithm is developed, and its performance is evaluated on autonomous driving games on road networks. Our results demonstrate that the PIGNO is scalable and generalizable when tested under unseen initial conditions.

Damage detection and location using a simulated annealing-artificial hummingbird algorithm with an improved objective function

Swarm intelligence algorithms and finite element model update technology are important issues in the field of structural damage detection. However, the complexity of engineering structural models normally leads to low computational efficiency and large detection errors in structural damage detection. To solve these problems, a simulated annealing-artificial hummingbird algorithm (SA-AHA) is proposed based on the artificial hummingbird algorithm (AHA). The Sobol sequence is used to improve the identification efficiency by optimizing the initial population distribution of the AHA. Then, the simulated annealing strategy is introduced to improve the detection accuracy by enhancing the global search ability of the AHA. In addition, a novel objective function is presented by combining modal flexibility residual, natural frequency residual, and trace sparse constraint of the structural model. Numerical simulations of a simply supported beam and a two-story rigid frame are carried out to verify the superiority of the proposed SA-AHA and the objective function. Simulation results demonstrate that the SA-AHA is better than the AHA in terms of damage computational efficiency and damage identification accuracy. Moreover, the new objective function can be more excellently applied to the SA-AHA than the previous one, which can be effectively used to locate and estimate the damage of the proposed SA-AHA in structure. Finally, experimental studies are carried out to verify the proposed method.

Evaluation of axis straightness error in the machining of hole and shaft parts based on improved exponential distribution optimizer

The straightness error of hole and shaft parts is one of the important parameters to reflect machining quality. The modeling process of traditional mathematical methods is complex, and the solution precision is not high. The intelligent optimization algorithm has significant advantages in solving this kind of problem. It can depend on random operators jumping out of local optima and does not need to calculate a large gradient information. Therefore, this paper proposes an improved exponential distribution optimization (IEDO) algorithm to achieve the minimum zone evaluation of straightness error. Firstly, the mathematical model of minimum zone method for axis straightness evaluation is established as the objective function. Secondly, the principle of the basic exponential distribution optimization (EDO) algorithm is described, and the exponential distribution optimizer is improved in three aspects: in the initialization, the interval shortening strategy is introduced to solve the problem of uneven initial population distribution; the adaptive switch probability is proposed to replace the constant value (0.5) to balance the ability of global exploration and local exploitation; a guidance strategy based on weight is proposed to guide the search process to reach the global optimal quickly. Then, nine typical benchmark functions are utilized to test the performance of the improved algorithm, which reveals satisfactory results. Finally, IEDO successfully applies to evaluation of axis straightness error with good accuracy.

Remaining useful life prediction framework of equipment based on improved golden jackal algorithm assisted-LSTM

It provides a challenge for remaining useful life prediction due to the complexity of the engine degradation process. Therefore, this paper proposes an improved method for engine remaining useful life prediction with long and short memory neural networks (LSTM) and extraction of health indicators for measured parameters. In order to overcome the limitation of measured parameters, a second-order polynomial approach is implemented to construct novel virtual parameters based on the existing parameters and improve the representativeness of the data to the engine degradation process. Then, random forests are used to score the importance of these parameters on the basis of which the higher rated parameters are filtered to reduce the computational burden. For the hyperparameter optimization problem of LSTM, an improved golden jackal optimization method is proposed in this paper, in which chaotic mapping is used to initialize the population to increase the uniformity of the initial population distribution in space. An adaptive method is introduced to improve the exploration and exploration capabilities of the golden jackal algorithm. Finally, the effectiveness of the proposed method is verified by NASA’s public dataset. The experimental results show that the R2 of the proposed method is greater than 0.99, the error of mean absolute percentage error is within 3%, and the root mean square error is smaller than 4. The proposed method can provide better predicted performance compared with the traditional methods.

Decomposition of Differentials in Health Expectancies From Multistate Life Tables: A Research Note

Multistate modeling is a commonly used method to compute healthy life expectancy. However, there is currently no analytical method to decompose the components of differentials in summary measures calculated from multistate models. In this research note, we propose a derivative-based method to decompose the differentials in population-based health expectancies estimated via a multistate model into two main components: the proportion resulting from differences in initial health structure and the proportion resulting from differences in health transitions. We illustrate the method using data on activities of daily living from the U.S. Health and Retirement Study to decompose the sex differential in disability-free life expectancy (HLE) among older Americans. Our results suggest that the sex gap in HLE results primarily from differences in transition rates between disability states rather than from the initial health distribution of female and male populations. The methods introduced here will enable researchers, including those working in fields other than health, to decompose the relative contribution of initial population structure and transition probabilities to differences in state-specific life expectancies from multistate models.

Enhanced Whale Optimization Algorithm for Improved Transient Electromagnetic Inversion in the Presence of Induced Polarization Effects

The transient electromagnetic (TEM) method is a non-contact technique used to identify underground structures, commonly used in mineral resource exploration. However, the induced polarization (IP) will increase the nonlinearity of TEM inversion, and it is difficult to predict the geoelectric structure from TEM response signals in conventional gradient inversion. We select a heuristic algorithm suitable for nonlinear inversion—a whale optimization algorithm to perform TEM inversion with an IP effect. The inverse framework is optimized by opposition-based learning (OBL) and an adaptive weighted factor (AWF). OBL improves initial population distribution for better global search, while the AWF replaces random operators to balance global and local search, enhancing solution accuracy and ensuring stable convergence. Tests on layered geoelectric models demonstrate that our improved WOA effectively reconstructs geoelectric structures, extracts IP information, and performs robustly in noisy environments. Compared to other nonlinear inversion methods, our proposed approach shows superior convergence and accuracy, effectively extracting IP information from TEM signals, with an error of less than 8%.

Model optimization of a high-power commercial PEMFC system via an improved grey wolf optimization method

Proton exchange membrane fuel cell (PEMFC) models are conventionally established with a set of parameters identified under steady-state operating conditions. However, such an approach is insufficient to accurately capture the dynamic characteristics of multi-parameter changes in real-world scenarios. This paper develops a semi-empirical model for a 110-kW commercial PEMFC system based on its dynamic operation data to remedy the defects. To improve the fitting accuracy of the semi-empirical PEMFC model, an improved grey wolf optimization (IGWO) algorithm is proposed for model parameter identification. The IGWO algorithm adopts chaotic mapping to optimize the initial population distribution, and a random walk strategy is incorporated to boost the local search ability of the traditional grey wolf optimization (GWO) algorithm. The effectiveness of this IGWO algorithm in optimizing the semi-empirical model is experimentally verified on the 110-kW PEMFC system under highly dynamic operating conditions. Results show that the proposed IGWO algorithm can effectively identify the semi-empirical model’s parameters, establishing a stable and robust model that outperforms those based on traditional metaheuristic algorithms such as GWO, particle swarm optimization, and genetic algorithm. The demonstrated improvement renders it as better suited for optimizing PEMFC semi-empirical models under real-world operating conditions.

A modified reptile search algorithm for parametric estimation of fractional order model of lithium battery

AbstractIn this paper, a Levy reptile search algorithm (LRSA) is proposed to improve the global search capability and convergence speed of reptile search algorithm which has advantages in solving single‐modal, multi‐modal and composite problems. Firstly, circle chaotic mapping is introduced to make the initial distribution of population more uniform and diversified. Secondly, Levy flight strategy is employed in the global search, which can improve the accuracy and convergence speed. In order to test and verify the optimization performance of the LRSA, 12 benchmark functions are tested and compared with four other intelligent optimization algorithms. It can be seen that LRSA is effective and advantageous in average convergence speed. In addition, the proposed LRSA is applied to a fractional order model identification of lithium battery with a very small error (less than 2%). The experimental results show that the LRSA can effectively estimate the parameters of the fractional order model and aid to state of charge and state of health estimation.

Investigating the effects of initial concentration and population distribution on the transport of aggregating nanoparticles in porous media

In order to investigate the migration of nanoparticles in porous media, the model developed by Katzourakis and Chrysikopoulos (2021) is applied to simulate the transport of aggregating nanoparticles under various initial conditions. In the aforementioned model, nanoparticles may collide with each other and form larger particle structures with different mobility and reactivity characteristics. Individual particles as well as aggregates can be found suspended in aqueous phase or attached, reversibly and/or irreversibly, on the solid matrix. The aggregation process modelled after the Smoluchowski population balanced equation (PBE), is coupled with the conventional advection-dispersion-attachment (ADA) equation to form a system of coupled equations that govern the transport of aggregating nanoparticles. Particle collisions are expected to increase exponentially with increasing initial number of injected particles (N0). Therefore, substantially pronounced aggregation is expected when N0 is increased. Similarly, the initial particle diameter distribution of the injected particles is expected to affect the average size of aggregates and in turn influence their mobility in a porous medium. Several model simulations were performed with different N0 and particle diameter distributions. The results indicated the strong importance of taking into account the initial particle concentration and realistic particle diameter population distribution into consideration.

Sustainable Internet of Vehicles System: A Task Offloading Strategy Based on Improved Genetic Algorithm

“Smart transportation” promotes urban sustainable development. The Internet of Vehicles (IoV) refers to a network with huge interaction, which comprises location, speed, route information, and other information about vehicles. To address the problems that the existing task scheduling models and strategies are mostly single and the reasonable allocation of tasks is not considered in these strategies, leading to the low completion rate of unloading, a task offloading with improved genetic algorithm (GA) is proposed. At first, with division in communication and calculation models, a system utility function maximization model is objectively conducted. The problem is solved by improved GA to obtain the scheme of optimal task offloading. As GA, in the traditional sense, inclines to a local optimum, the model herein introduces a Halton sequence for uniform initial population distribution. Additionally, the authors also adapt improved GA for the problem model and global optimal solution guarantee, thus improving the rate of task completion. Finally, the proposed method is proven through empirical study in view of scenario building. The experimental demonstration of the proposed strategy based on the built scenario shows that the task calculation completion rate is not less than 75%, and when the vehicle terminal is 70, the high-priority task completion rate also reaches 90%, which can realize reasonable allocation of computing resources and ensure the successful unloading of tasks.

Inadequate load output diagnosis of ultra-supercritical thermal power units based on MIWOA multi-label random forest

Aiming at the problem of considerable economic losses caused by the large fluctuation range of response load and Automatic Generation Control (AGC) commanded load of ultra-supercritical thermal power units, an inadequate load output diagnosis model of multi-label random forest with multi-improved whale optimization algorithm (MIWOA-MLRF) is proposed. Thermal power units are a kind of high-dimensional, nonlinear, and complex industrial complex, which brings difficulty for conventional mechanistic models in comprehensively analyzing the inadequate output causes of the units. With the help of extensive data analysis and artificial intelligence algorithms, a multi-label random forest (MLRF) for inadequate output cause analysis is constructed. To improve the accuracy of WOA for MLRF classification, a good point set strategy is used to optimize the initial population distribution of WOA, and an improved convergence factor is used to control the speed of WOA search. Three Gaussian simulation datasets with a different number of features and labels and three sets of thermal power operation data with varying periods are utilized. The test results show that the missing alarm rates (MAR) of MIWOA-MLRF are 0.301, 0.621, and 0.802 under three sets of Gaussian data tests, which are the smallest values among all analytical models. Under three sets of thermal power unit operation data, the average values of MIWOA-MLRF's MAR are 2.2%, 0.2%, and 1.5%, respectively, which have lower missing alarm rates compared with other algorithms. Furthermore, in the false alarm rate (FAR) comparison, the FAR of MIWOA-MLRF decreases significantly compared to MLRF. In the optimization time comparison, the optimization time required for MIWOA-MLRF is about 1000 s, which saves 33% on average compared to the optimization time of other algorithms. The result indicates that MIWOA-MLRF can guarantee the same better real-time performance with reduced FAR and MAR of inadequate output cause classification.

Hybrid Strategy Improved Sparrow Search Algorithm in the Field of Intrusion Detection

Aiming at the problem that sparrow search algorithm(SSA) may fall into local optima and has slow convergence speed, a hybrid strategy improved Sparrow Search Algorithm(HSISSA) is proposed in this paper, and it is applied to feature selection and model optimization of intrusion detection. Firstly, the hybrid circle-piecewise map is proposed to initialize the population and improve the uniformity of the initial population distribution; Secondly, merging the spiral search method in the vulture search algorithm and Levy’s flight formula to update the positions of the discoverer and the scouter respectively, to expand the population search range and enhance the search capability; Finally, simplex method and pinhole imaging method are used to optimize the position of sparrows with poor fitness and optimal fitness, to avoid stagnation in the population search and falling into local optima. The performance of the algorithm is optimized through the above methods. The algorithm is tested on 10 classical benchmark functions and combined with Wilcoxon rank sum test analysis to verify the effectiveness of the algorithm, which showed improvements in convergence speed and accuracy. Finally, it is applied to feature selection and model optimization of intrusion detection. On average, 7.6 features and 10.1 features are retained on the CIC-IDS2017 dataset and UNSW-NB15 dataset, respectively, and 99.5% and 96.01% accuracy are achieved. The number and accuracy of the optimized features are better than the original algorithm. For DenseNet and random forest models, HSISSA can achieve 99.34% and 97.22% accuracy after optimization, which improves the performance of the model, thus, the algorithm shows better performance than other algorithms.

A novel improved whale optimization algorithm for optimization problems with multi-strategy and hybrid algorithm

Whale optimization algorithm (WOA), as an advanced optimization algorithm with simple structure, has been favored by various fields. However, there are some disadvantages of WOA, such as slow convergence speed, low precision and falling into local optimal value easily. In this paper, a novel improved whale optimization algorithm (IWOA) with multi-strategy and hybrid algorithm is proposed to overcome above shortcomings. Firstly, IWOA initializes the population by chaotic mapping to avoid the initial population distribution of WOA deviating from the optimal value. Secondly, IWOA combines the pheromone of the black widow algorithm and the opposition-based learning strategy to modify the population, which improves the convergence speed and the global performance of WOA respectively. Finally, the adaptive coefficients and the new update modes replace the original update modes, which makes the structure of WOA simpler and more accurate. In addition, the convergence of IWOA is also proved in this paper. On the one hand, to demonstrate the effectiveness of IWOA, 23 benchmark functions are used to test various performance of the algorithm. On the other hand, in order to prove the superiority of IWOA, the experimental results are compared and analyzed with other optimization algorithms. Simulation results show that IWOA proposed in this paper owns excellent performance in convergence speed, stability, accuracy and global performance, compared with other algorithms.