Fitness Function Research Articles

Railway wagon bearing fault diagnosis method based on improved sparrow search algorithm optimizing variational mode decomposition and multi-level convolutional neural network.

Ensuring the safe operation of trains hinges on precise bearing condition monitoring, given the pivotal role bearings play in railway wagons. The status and maintenance of wagon bearings are of paramount concern, necessitating a shift from traditional maintenance approaches reliant on schedules and experience, which often lack real-time precision and efficiency. To address this challenge, our research focuses on enhancing the sparrow search algorithm by incorporating logistic chaos mapping and the levy flight strategy. This enhanced algorithm optimizes variational mode decomposition parameters, utilizing intrinsic mode components' average dispersion entropy as the fitness function. This optimization is integrated with a multi-level convolutional neural network for bearing fault diagnosis. Our findings demonstrate the improved algorithm's enhanced spatial search capabilities and reduced modal aliasing in the frequency components. Experimental validation on public datasets and the group's experimental platform for railway wagons shows that multi-level convolutional neural networks have higher diagnostic accuracy and faster convergence speeds than traditional models such as LeNet-5, AlexNet, and convolutional neural network. Our research introduces a highly accurate and widely applicable methodology for mechanical equipment fault diagnosis, aligning with the requirements of the "smart" era.

SA-Based QoS Aware Workflow Scheduling of Collaborative Tasks in Grid Computing

Scheduling workflow tasks in grid computing is a complex process, especially if it is associated with satisfying the user's requirements to complete tasks within a specified time, with lowest possible cost. This paper presents a proposed Simulated Annealing (SA) based Grid Workflow Tasks Scheduling Approach (SA-GWTSA) that takes into account users’ QoS (quality of service) constraints in terms of cost and time. For a given set of inter-dependent workflow tasks, it generates an optimal schedule, which minimizes the execution time and cost, such that the optimized time is within the time constraints (deadline) imposed by the user. In SA-GWTSA, the workflow tasks, which are modeled as a DAG, are divided into task divisions, each of which consists of a set of sequential tasks. Then, the optimal sub-schedules of all task divisions are computed applying SA algorithm, and used to obtain the execution schedule of the entire workflow. In the proposed algorithm, the sub-schedule of each branch division is represented by a vector, in which each element holds the ID of the service provider chosen from a list of service providers capable of executing the corresponding task in the branch. The algorithm uses a fitness function that is formulated as a multi-objective function of time and cost, which gives users the ability to determine their requirements of time against cost, by changing the weighting coefficients in the objective function. The paper also exhibits the experimental results of assessing the performance of SA-GWTSA with workflows samples of different sizes, compared to different scheduling algorithms: Greedy-Time, Greedy-Cost, and Modified Greedy-Cost.

Bio inspired heuristic computing scheme for the human liver nonlinear model

In this research, a bio-inspired heuristic computing approach has been developed to solve the nonlinear behavior of the human liver, which is categorized into the liver and blood. The solutions of the human liver model are presented by using the stochastic computation procedure based on the artificial neural network (ANN) along with the optimization of genetic algorithm (GA) and interior-point (IP). A fitness function is designed through the differential form of the nonlinear human liver model and then optimized by using the hybrid competency of GAIP scheme. The correctness and exactness of the proposed approach are observed through the overlapping of the obtained (GAIP) and reference (Adams scheme) solutions, while the calculated absolute error values in good order enhance the worth of the proposed solver. The log-sigmoid transfer function together with ten numbers of neurons is executed to perform the solutions of the human liver nonlinear model. Furthermore, the statistical approaches have been applied in order to observe the reliability of the designed approach for solving the nonlinear human liver model.

Intelligent optimal control of nonlinear diabetic population dynamics system using a genetic algorithm

Diabetes is a chronic disease affecting millions of people worldwide. Several studies have been carried out to control the diabetes problem, involving both linear and non-linear models. However, the complexity of linear models makes it impossible to describe the diabetic population dynamic in depth. To capture more detail about this dynamic, non-linear terms were introduced into the mathematical models, resulting in more complicated models strongly consistent with reality (capable of re-producing observable data). The most commonly used methods for control estimation are Pantryagain’s maximum principle and Gumel’s numerical method. However, these methods lead to a costly strategy regarding material and human resources; in addition, diabetologists cannot use the formulas implemented by the proposed controls. In this paper, the authors propose a straightforward and well-performing strategy based on non-linear models and genetic algorithms (GA) that consists of three steps: 1) discretization of the considered non-linear model using classical numerical methods (trapezoidal rule and Euler–Cauchy algorithm); 2) estimation of the optimal control, in several points, based on GA with appropriate fitness function and suitable genetic operators (mutation, crossover, and selection); 3) construction of the optimal control using an interpolation model (splines). The results show that the use of the GA for non-linear models was successfully solved, resulting in a control approach that shows a significant decrease in the number of diabetes cases and diabetics with complications. Remarkably, this result is achieved using less than 70% of available resources.

Genetic Algorithm-Based Framework for Optimization of Laser Beam Path in Additive Manufacturing

In this study, a genetic algorithm-based laser beam (LB) path optimization method is presented to improve laser-based additive manufacturing (LBAM). To emulate the LBAM process, LB irradiation of a thin metal substrate is applied. The LB path generation is formulated as the search for the optimal sequence of LB irradiation into the cells on the substrate that minimizes the fitness function, which is composed of two components, i.e., thermal fitness and process fitness. The thermal fitness is expressed by the average thermal gradient, and a simple thermal model is developed to simulate the effects of laser-induced heat input on the temperature distribution in the substrate. The process fitness regulates the suitability of the proposed LB path for the implementation of the LBAM process. In addition to standardized tool paths (i.e., raster, spiral, etc.), novel LB path generators are proposed to define the initial population of LB path solutions. To implement a genetic algorithm-based LB path optimization, a framework is proposed, and custom initialization, crossover, and mutation operators are developed for application in LBAM. The effectiveness of the proposed approach is demonstrated through a simulation case study aiming to identify LB paths that minimize the fitness function and thus provide more suitable LB path solutions with respect to the defined fitness function. Compared with the traditional trial-and-error LB path formulations, the proposed approach provides an improved and automated method for an efficient laser beam path selection in LBAM.

Weighted Visibility Graph Based WiFi Indoor Positioning Method Using Heuristic Optimization

With the widespread use of wireless communication technologies and IoT applications, researchers are developing approaches that utilize WiFi signals for indoor location determination. In this study, indoor positioning process based on heuristic optimization-based methods was performed by creating weighted visibility matrices of access points based on WiFi signal strength (RSSI) values. In the proposed method, the PSO and GA approaches determine the position of the mobile user using a common fitness function based on the visibility weight matrices. The proposed method has been tested on a virtual scenario where position ranges based on RSSI ranges are determined. Both heuristic optimization methods are compared according to different criteria and the positioning process is performed with a maximum error of 3m for the GA based method and a maximum of 1.5m for the PSO based method.

Firm failure prediction using genetic programming generated features

Many studies on predicting firm failure have focused on finding new features that improve the accuracy of the models. In this paper, genetic programming (GP) is used for this purpose. The main problem in GP is to specify a function that evaluates the fitness of the feature. Direct optimization of a machine learning (ML) model that uses a generated feature in most cases leads to high computational costs since evolving a population of N programs over G generations while evaluating each model using K-fold cross-validation requires N*G*K model learning cycles. Thus, many researchers use scores that measure the relationship of the generated features to the class label. However, our empirical analysis shows that most such scores correlate poorly with ML model performance. The novelty of our work is that we introduce several ways of combining different scores into a single measure of expected model performance. Experimental results on data from Hungarian firms (7167 observations, class imbalance 9.37) using five ML models (Logistic Regression, Random Forest, Gradient Boosting, Histogram Boosting, and AdaBoost) prove that the proposed way of setting the fitness function increases the ROC AUC of the listed models by 6.6%, 5.2%, 6.8%, 5.5% and 5.2% respectively. Moreover, by applying the found formula to the data from Czech firms (3872 observations, class imbalance of 74.92), which were not used for the feature search, we obtained increases in ROC AUC by 13.1%, 11.8%, 14.9%, 9.9%, and 8.2%, respectively. This indicates that the proposed method allows to find universal features, which opens the way to build effective models in case of insufficient data (small number of observations, extreme imbalance, etc.).

Ensemble classifiers using multi-objective Genetic Programming for unbalanced data

Genetic Programming (GP) can be used to design effective classifiers due to its built-in feature selection and feature construction characteristics. Unbalanced data distributions affect the classification performance of GP classifiers. Some fitness functions have been proposed to solve the class imbalance problem of GP classifiers. However, with the evolution of GP, single-objective GP classifiers evaluated by a single fitness function have poor generalization ability. Moreover, using the best evolved GP classifier for decision-making can easily lead to the possibility of misclassification. In this paper, multi-objective GP is used to optimize multiple fitness functions including AUC approximation (Wmw), Distance (Dist), and Complexity to evolve ensemble classifiers, which jointly determines the class labels of unknown instances. Experiments on sixteen datasets show that our multi-objective GP can significantly improve classification performance compared with single-objective GP, and our proposed ensemble classifiers evolved by multi-objective GP can further improve the classification performance than the single best GP classifier. Comparisons with six GP-based and five traditional machine learning algorithms show that our proposed approaches can achieve significantly better classification performance on most cases.

State Feedback Control for Vehicle Electro-Hydraulic Braking Systems Based on Adaptive Genetic Algorithm Optimization

In traditional state feedback control, the difficulty in determining the coefficient matrix is a significant factor that prevents achieving optimal control. To address this issue, this paper proposes the integration of adaptive genetic algorithms with state feedback control. The effectiveness of the proposed algorithm is validated via an electro-hydraulic braking system. Firstly, a model of the electro-hydraulic braking system is introduced. Next, a state feedback controller optimized by parameter-adaptive genetic algorithm is designed. Additionally, a penalty term is introduced into the fitness function to suppress overshoots. Finally, simulations are conducted to compare the convergence speed of parameter-adaptive genetic algorithm with genetic algorithm, ant colony optimization, and particle swarm optimization. Furthermore, the performance of the proposed algorithm, the state feedback control, and the proportional-integral control are also compared. The comparison results show that the proposed algorithm effectively accelerates the settling time of the electro-hydraulic braking system and suppresses the overshoots.

Optimization Design of Medium-Voltage High-Frequency Transformers Based on Parallel Sampling Kriging Surrogate Model

In the design process of medium-voltage high-frequency transformers, numerical simulation methods often result in long design cycles and significant computational resource consumption. To achieve rapid and high-precision design for medium-voltage high-frequency transformers, a parallel kriging surrogate model is proposed based on the maximum expected improvement criterion. Initially, Latin hypercube sampling is employed to establish the initial sampling points, and corresponding three-dimensional models of medium-voltage high-frequency transformers are constructed for each sampling point configuration. Finite element simulation software is then used to compute the transformer loss and volume for each model. Subsequently, a kriging surrogate model is developed based on the initial sampling point configuration, and an enhanced parallel sampling strategy is implemented to enhance the fitting accuracy of the kriging surrogate model. Finally, the high-precision kriging surrogate model is utilized as the fitness function for a particle swarm optimization algorithm, with the objectives of minimizing transformer loss and volume. The Pareto optimal solution is sought under multi-objective optimization. The effectiveness of the proposed optimization method is verified through experimental measurements of the loss in a prototype of a medium-voltage high-frequency transformer.

A grey wolf optimization-based modified SPWM control scheme for a three-phase half bridge cascaded multilevel inverter

The Multilevel inverter (MLI) plays a pivotal role in Renewable Energy (RE) systems by offering a cost-effective and highly efficient solution for converting DC from Photovoltaic (PV) sources into AC at high voltages. In addition, an innovative technology holds immense significance as it not only enables the seamless integration of PV systems into the grid but also ensures optimal power generation, thereby contributing to the widespread adoption of RE and fostering a sustainable future. This paper presents a modified sinusoidal pulse width modulation (SPWM) control scheme for a three-phase half-bridge cascaded MLI-powered PV sources. The selection of the MLI configuration is motivated by its reduced number of switching components, which enhances system reliability and simplifies experimental implementation. Compared to the SPWM schemes which require (m−1) carriers that make the generation of the pulse circuit very complex, the proposed control scheme requires only three signals: a carrier signal, a triangular waveform, and a modulating signal. This approach significantly reduces the complexity of control and facilitates practical implementation. The proposed control scheme simulation is verified using MATLAB/SIMULINK Software. The grey wolf optimization (GWO) algorithm is implemented to determine the optimal switching angles of the proposed control scheme. The Total Harmonic Distortion (THD) objective is selected to be the fitness function to be minimized for improving the quality of the output waveforms. For verification, the results of the proposed GWO-based modified SPWM control scheme are compared with those obtained using both the Particle swarm Optimization (PSO) and Genetic algorithm (GA) used in the literature. Simulation results declared that the proposed control scheme improves performance, especially THD which is minimized to 6.8%. Experimental validation has been conducted by building a laboratory prototype of the proposed system. The experimental and simulation results gave acceptable and limited convergent results considering the experimental difficulties.

EG-NAS: Neural Architecture Search with Fast Evolutionary Exploration

Differentiable Architecture Search (DARTS) has achieved a rapid search for excellent architectures by optimizing architecture parameters through gradient descent. However, this efficiency comes with a significant challenge: the risk of premature convergence to local optima, resulting in subpar performance that falls short of expectations. To address this issue, we propose a novel and effective method called Evolutionary Gradient-Based Neural Architecture Search (EG-NAS). Our approach combines the strengths of both gradient descent and evolutionary strategy, allowing for the exploration of various optimization directions during the architecture search process. To begin with, we continue to employ gradient descent for updating network parameters to ensure efficiency. Subsequently, to mitigate the risk of premature convergence, we introduce an evolutionary strategy with global search capabilities to optimize the architecture parameters. By leveraging the best of both worlds, our method strikes a balance between efficient exploration and exploitation of the search space. Moreover, we have redefined the fitness function to not only consider accuracy but also account for individual similarity. This inclusion enhances the diversity and accuracy of the optimized directions identified by the evolutionary strategy. Extensive experiments on various datasets and search spaces demonstrate that EG-NAS achieves highly competitive performance at significantly low search costs compared to state-of-the-art methods. The code is available at https://github.com/caicaicheng/EG-NAS.

Freezing and Mechanical Failure of a Habitat-Forming Kelp in the Rocky Intertidal Zone.

Kelp and other habitat-forming seaweeds in the intertidal zone are exposed to a suite of environmental factors, including temperature and hydrodynamic forces, that can influence their growth, survival, and ecological function. Relatively little is known about the interactive effect of temperature and hydrodynamic forces on kelp, especially the effect of cold stress on biomechanical resistance to hydrodynamic forces. We used the intertidal kelp Egregia menziesii to investigate how freezing in air during a low tide changes the kelp's resistance to breaking from hydrodynamic forces. We conducted a laboratory experiment to test how short-term freezing, mimicking a brief low-tide freezing event, affected the kelp's mechanical properties. We also characterized daily minimum winter temperatures in an intertidal E. menziesii population on San Juan Island, WA, near the center of the species' geographic range. In the laboratory, acute freezing events decreased the strength and toughness of kelp tissue by 8-20% (change in medians). During low tides in the field, we documented sub-zero temperatures, snow, and low canopy cover (compared to summer surveys). These results suggest that freezing can contribute to frond breakage and decreased canopy cover in intertidal kelp. Further work is needed to understand whether freezing and the biomechanical performance in cold temperatures influence the fitness and ecological function of kelp and whether this will change as winter conditions, such as freezing events and storms, change in frequency and intensity.

Mayfly Optimization Algorithm: A Nature-Inspired Approach with Advanced Termination for Efficient Minimization

The Mayfly Optimization Algorithm (MO), inspired by the swarming and mating behavior of adult mayflies, offers a promising approach for tackling minimization problems. However, traditional MO can suffer from unnecessary iterations and exploration of suboptimal regions in the search space. This paper addresses these limitations by introducing two key advancements: greedy selection and auto-termination. Greedy selection ensures the algorithm prioritizes solutions with lower fitness values during position updates, guiding the search towards the minimum more effectively. Auto-termination monitors fitness function changes over a defined window and terminates the algorithm if improvement stagnates, reducing computation time. We evaluate the performance of the enhanced MO algorithm on various benchmark minimization functions. The results demonstrate that the incorporation of greedy selection and auto-termination significantly improves the convergence speed and efficiency of MO compared to the traditional approach. This paves the way for MO to be a more competitive and efficient tool for tackling various real-world minimization problems. Index Terms— Mayfly Optimization Algorithm (MO), Minimization Problems, Greedy Selection, Auto-Termination, Fitness Function, Convergence Speed, Efficiency, Swarm Intelligence, Nature-Inspired Optimization

Abstract 3969: Alzheimer's disease-associated amyloid precursor protein improves anti-tumor immunity by inhibiting ceramide-mediated lethal mitophagy and restoring mitochondrial fumarate metabolism in aging T-cells

Abstract The association between Alzheimer’s disease (AD) and lower cancer incidence has been observed in several epidemiological studies without much mechanistic insight. We aimed to understand this cancer resistance mechanism linked to AD and exploit it for broader cancer therapies. A hallmark of AD is the accumulation of amyloid precursor protein (APP) in neuronal mitochondria, inhibiting mitophagy, abrogating the clearance of dysfunctional mitochondria, and contributing to AD pathology. In addition to neurons, mitophagy plays an important role in T-cells, where excessive ceramide-dependent mitophagy in aging T-cells inhibits their energy metabolism and limits their anti-tumor functions. Thus, attenuation of ceramide-dependent mitophagy plays a favorable role in enhancing aging T-cell anti-tumor function. Given that APP is also processed in T-cells, we hypothesized that APP accumulates aberrantly in aging Alzheimer’s T-cells, inhibiting mitophagy and enhancing T-cell anti-tumor activity. Employing techniques of lipidomics, metabolomics, imaging, molecular biology, and genetic models, we show that AD T-cells from a mouse model and human patients are protected against aging-associated ceramide-dependent mitophagy, which enhanced their secretion of inflammatory anti-tumor cytokines. The excessive localization of APP to the T-cell mitochondria, where it inhibits the accumulation of ceramide synthase 6 (CerS6) and C14/C16-ceramides, attenuated the levels of mitophagy and protected the Alzheimer’s T-cells from metabolic defects observed in aging, namely protecting against the exhaustion of fumarate pools. Both in vitro and in vivo re-supplementation of fumarate to aging WT mouse T-cells effectively rescued their viability, cytokine production, and tumor killing capacity both in vitro and in adoptive T-cell transfers to mice with melanoma, functionally mimicking the AD phenotype. The fumarate supplementation also led to a decrease in ceramide-dependent mitophagy in the WT aging T-cells, indicating an interplay between ceramide-dependent mitophagy and fumarate metabolism which contributes to the protection of the anti-tumor function in aging AD T-cells. Moreover, we show that Alzheimer’s mice that express APP in the brain and T-cells, but not AD mice that express APP only in the brain, are protected against the growth of both carcinogen-induced and allografted tumors. Finally, transfer of mitochondria from AD T-cells to aging WT T-cells improved their metabolic fitness and anti-cancer functions. In summary, we show that AD-associated APP improves anti-tumor immunity of aging T-cells by inhibiting ceramide-mediated lethal mitophagy and restoring mitochondrial fumarate metabolism. These data provide a novel mechanism to explain reduced cancer incidences in AD patients. Citation Format: Mohamed Faisal Kassir, Han G. Lee, Natalia Oleinik, Wyatt Wofford, Chase Walton, F. Cansu Atilgan, Paramita Chakraborty, Kubra Calisir, Ozgur Sahin, Shikhar Mehrotra, Besim Ogretmen. Alzheimer's disease-associated amyloid precursor protein improves anti-tumor immunity by inhibiting ceramide-mediated lethal mitophagy and restoring mitochondrial fumarate metabolism in aging T-cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3969.

Exploring the Entropy-Based Classification of Time Series Using Visibility Graphs from Chaotic Maps

The classification of time series using machine learning (ML) analysis and entropy-based features is an urgent task for the study of nonlinear signals in the fields of finance, biology and medicine, including EEG analysis and Brain–Computer Interfacing. As several entropy measures exist, the problem is assessing the effectiveness of entropies used as features for the ML classification of nonlinear dynamics of time series. We propose a method, called global efficiency (GEFMCC), for assessing the effectiveness of entropy features using several chaotic mappings. GEFMCC is a fitness function for optimizing the type and parameters of entropies for time series classification problems. We analyze fuzzy entropy (FuzzyEn) and neural network entropy (NNetEn) for four discrete mappings, the logistic map, the sine map, the Planck map, and the two-memristor-based map, with a base length time series of 300 elements. FuzzyEn has greater GEFMCC in the classification task compared to NNetEn. However, NNetEn classification efficiency is higher than FuzzyEn for some local areas of the time series dynamics. The results of using horizontal visibility graphs (HVG) instead of the raw time series demonstrate the GEFMCC decrease after HVG time series transformation. However, the GEFMCC increases after applying the HVG for some local areas of time series dynamics. The scientific community can use the results to explore the efficiency of the entropy-based classification of time series in “The Entropy Universe”. An implementation of the algorithms in Python is presented.

CBDA: Chaos-based binary dragonfly algorithm for evolutionary feature selection

The goal of feature selection in machine learning is to simultaneously maintain more classification accuracy, while reducing lager amount of attributes. In this paper, we firstly design a fitness function that achieves both objectives jointly. Then we come up with a chaos-based binary dragonfly algorithm (CBDA) that incorporates several improvements over the conventional dragonfly algorithm (DA) for developing a wrapper-based feature selection method to solve the fitness function. Specifically, the CBDA innovatively introduces three improved factors, namely the chaotic map, evolutionary population dynamics (EPD) mechanism, and binarization strategy on the basis of conventional DA to balance the exploitation and exploration capabilities of the algorithm and make it more suitable to handle the formulated problem. We conduct experiments on 24 well-known data sets from the UCI repository with three ablated versions of CBDA targeting different components of the algorithm in order to explain their contributions in CBDA and also with five established comparative algorithms in terms of fitness value, classification accuracy, CPU running time, and number of selected features. The results show that the proposed CBDA has remarkable advantages in most of the tested data sets.

Performance of rime-ice algorithm for estimating the PEM fuel cell parameters

The process of employing optimization techniques to identify the optimum unknown variables appropriate for the creation of a precision fuel-cell performance forecasting model is known as parameter identification of a PEM fuel cell (PEMFC). The manufacturer's datasheet may not always provide these parameters, thus it is necessary to ascertain them to precisely estimate and forecast the fuel cell's performance. six unknown parameters of a PEMFC are computed using six optimization techniques: The Rime-Ice algorithm (RIME), the Grey Wolf Optimizer (GWO), the Moth Flam Optimizer (MFO), the Tunicate Swarm Algorithm (TSA), the Sine Cosine Algorithm (SCA), and the Osprey Optimization Algorithm (OOA). These six parameters serve as choice variables during optimization, and the sum square error (SSE) between the estimated and measured cell voltages is the fitness function that needs to be minimized. Ned Stack PS6, a real-world PEM fuel cell model, is used to verify the functionality of all comparator algorithms, including the suggested RIME method. The RIME algorithm yielded an SSE of 1.945417827, which was followed by MFO, GWO, TSA, SCA, and OOA. In addition, it was concluded that RIME's convergence speed was quicker than that of the other methods examined. The comparative analysis is conducted using the same dataset and the same computation burden for each of the several optimization techniques to provide a fair performance evaluation. The performance of the suggested RIME against the alternative optimization algorithms is further examined using statistical analysis. The results demonstrate a good degree of agreement between the experimental data and the estimated voltage-current curves produced by the suggested RIME.

A hybrid optimization algorithm for improving load frequency control in interconnected power systems

The objective of this study is to develop an algorithm named Modified Artificial Bee Colony and Particle Swarm Optimization (MHABC-PSO) to address load frequency control (LFC) challenges in a two-area interconnected power system. The proposed MHABC-PSO algorithm is designed with two key modifications to enhance global exploration capability and improve convergence speed. Hence, a decision block is introduced in the employed bee (EB) phase incorporating a control parameter “limit” to allow each candidate solution (CS) to explore itself up to the “limit” value and boost local exploration. In addition, an introduction of a novel selection mechanism utilizing heuristic information (η) in EBs phase guides the onlooker bee (OB) phase to select better solutions based on success and failure history, thus promoting exploitation and reducing biased exploration. To address the efficacy of the proposed algorithm at the system level, three different two-area power systems are studied, incorporating various complexities, linearity, and non-linearity such as thermal-hydro, reheat thermal, and thermal-hydro-gas turbine configurations with HVDC link, SSSC, and CES. The algorithm is applied to optimize four objective functions (i.e. ITAE, IAE, ISAE, and ITE). The fitness function maximizes controller gains by utilizing the integral time multiplied absolute error (ITAE). Other objective functions like IAE, ISAE, and ITE are employed for a comprehensive analysis. Evaluation of MHABC-PSO effectiveness is conducted through ITAE values, peak deviations, and settling times of frequency and power deviations in different two-area systems. Results demonstrate that MHABC-PSO settles the system more quickly with zero steady-state error under step load perturbations (SLPs) of 1% and 2%. Comparative analysis with ABC, PSO, SFLA-TLBO, and OHABC-PSO using ITAE index and controller settling times shows the superiority of MHABC-PSO for LFC analysis. In conclusion, the proposed MHABC-PSO algorithm proves to be an efficient and effective solution for LFC, outperforming other algorithms in terms of the specified objective functions and exhibiting rapid convergence and optimal gains for controllers, effectively addressing LFC issues by combining exploration and exploitation techniques.

Automatic Knowledge Graph matching via Self-adaptive Designed Genetic Programming

Knowledge Graph (KG) provides a structured representation of domain knowledge by formally defining entities and their relationships. However, distinct communities tend to employ different terminologies and granularity levels to describe the same entity, leading to the KG heterogeneity issue that hampers their communications. KG matching can identify semantically similar entities in two KGs, which is an effective solution to this problem. Similarity Measures (SMs) are the foundation of the KG matching technique, and due to the complexity of entity heterogeneity, it is necessary to construct a high-level SM by selecting and combining the basic SMs. However, the large number of SMs and their intricate relationships make SM construction an open challenge. Inspired by the success of Evolutionary Algorithms (EA) in addressing the entity matching problem, this work further proposes a novel Self-adaptive Designed Genetic Programming (SDGP) to automatically construct the SM for KG matching. To overcome the drawbacks of the classic EA-based matching methods, a new individual representation and a novel fitness function are proposed to enable SDGP automatically explore the SM selection and combination. Then, a new Adaptive Automatic Design (AAD) method is introduced to adaptively trade off SDGP’s exploration and exploitation, which can determine the timing of AAD and efficiently determine the suitable breeding operators and control parameters for SDGP. The experiment uses the Ontology Alignment Evaluation Initiative’s Knowledge Graph (KG) data set to test the performance of SDGP. The experimental results show that SDGP can effectively determine high-quality KG alignments, which significantly outperform state-of-the-art KG matching methods.