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With the rapid proliferation of electric vehicles, accurate charging load forecasting has become critical for grid stability and infrastructure planning. In light of the limitations of traditional forecasting methods in addressing the complex nonlinear coupling relationships between charging loads and multi-source influencing factors, as well as their inadequacies in handling distribution heterogeneity across different spatial regions, this paper proposes a hybrid model based on the IVY-optimized Temporal Convolutional Network (TCN) and Bidirectional Long Short-Term Memory (BiLSTM). The IVY optimization algorithm is employed to adaptively determine the hyperparameters of Variational Mode Decomposition (VMD), mitigating over-decomposition or under-decomposition caused by empirical settings. This process decomposes the original load series into multiple stationary modal components. These components are then fed into the TCN-BiLSTM model, where TCN extracts local temporal features and BiLSTM captures bidirectional long-term dependencies, enabling collaborative modeling of multi-scale temporal characteristics. Experimental results based on a real electric vehicle charging load dataset collected from Shenzhen, a major metropolitan city in China, demonstrate that the proposed model significantly outperforms the comparative models in terms of prediction accuracy and stability. The achieved Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R 2 ) are 0.98308, 1.3208, and 0.99039, respectively, confirming the model’s high-precision forecasting capability and strong generalization performance across different spatial regions with heterogeneous charging characteristics.

The Adaptive Cross Ivy (ACIVY) algorithm is a novel bio-inspired metaheuristic that emulates ivy plant growth behaviors for complex optimization problems. While the original Ivy Optimization Algorithm (IVYA) demonstrates a competitive performance, it suffers from limited inter-individual information exchange, inadequate directional guidance for local optima escape, and abrupt exploration–exploitation transitions. To address these limitations, ACIVY integrates three strategic enhancements: the crisscross strategy, enabling horizontal and vertical crossover operations for improved population diversity; the LightTrack strategy, incorporating positional memory and repulsion mechanisms for effective local optima escape; and the Top-Guided Adaptive Mutation strategy, implementing ranking-based mutation with dynamic selection pools for smooth exploration–exploitation balance. Comprehensive evaluations on the CEC2017 and CEC2022 benchmark suites demonstrate ACIVY’s superior performance against state-of-the-art algorithms across unimodal, multimodal, hybrid, and composite functions. ACIVY achieved outstanding average rankings of 1.25 (CEC2022) and 1.41 (CEC2017 50D), with statistical significance confirmed through Wilcoxon tests. Practical applications in engineering design optimization and UAV path planning further validate ACIVY’s robust performance, consistently delivering optimal solutions across diverse real-world scenarios. The algorithm’s exceptional convergence precision, solution reliability, and computational efficiency establish it as a powerful tool for challenging optimization problems requiring both accuracy and consistency.

The Ivy Optimization Algorithm (IVY) is a novel swarm intelligence optimization algorithm that simulates the phototropic growth mechanism of plants. To comprehensively improve the overall optimization performance, this paper proposes an enhanced Ivy Optimization Algorithm (LSIVY) integrating improved Logistics chaotic mapping, sine–cosine operator, and adaptive t-distribution mutation strategy. Firstly, an improved cascaded Logistics chaotic mapping is used for population initialization. The double arcsine transformation improves the ergodicity and uniformity of chaotic sequences, so that initial solutions are distributed more evenly in the search space, population diversity is enhanced, and premature convergence is suppressed. Secondly, the sine–cosine operator is embedded into the position update mechanisms of IVY growth, climbing, and propagation evolution. Nonlinearly decreasing control parameters realize adaptive switching between global exploration and local exploitation and accelerate convergence. Thirdly, an adaptive t-distribution mutation strategy is designed to dynamically adjust mutation intensity according to the iteration cycle and implement directional perturbation at the optimal solution position. It combines the large-scale exploration advantage of the Cauchy distribution and the local fine search merit of the Gaussian distribution, which significantly improves the ability to escape from local optima. Comparative experiments with eight mainstream metaheuristics (DE, WOA, GWO, HHO, DBO, MBWO, AOO, native IVY) are conducted with 30 independent runs on 30-dimensional CEC 2014 (30 test functions) and CEC 2020 (10 composite functions). Quantitatively, LSIVY achieves 20~30 orders of magnitude higher optimization accuracy than standard IVY on unimodal functions, and its average standard deviation across all benchmarks drops by 4–6 orders of magnitude. LSIVY ranks first on all CEC 2020 composite functions, reducing over 30% of iterations compared with native IVY. Three classical constrained mechanical design problems (three-bar truss, cantilever beam, pressure vessel) are adopted for engineering verification. In the pressure vessel case, the average manufacturing cost of LSIVY is reduced by 9.2% against standard IVY, and the standard deviation of three engineering cases decreases by 2–3 orders on average, demonstrating remarkable robustness. The proposed algorithm not only improves the theoretical system of plant-inspired swarm intelligence algorithms but also has great application prospects in mechanical structure lightweight design, industrial equipment cost optimization, and other practical engineering fields.

To overcome the limitations of the original Ivy Algorithm (IVYA), including insufficient population diversity, limited step-size adaptability, and premature convergence, this paper proposes a multi-strategy enhanced Ivy optimization algorithm (MEIVYA). The proposed method integrates chaotic population initialization, adaptive growth-rate regulation, and an elite-guided cooperative search strategy to improve global exploration, local exploitation, and convergence stability. Experimental results on the CEC2014 and CEC2017 benchmark suites show that MEIVYA achieves competitive convergence accuracy, robustness, and stability compared with several state-of-the-art metaheuristic algorithms. In addition, MEIVYA is applied to multi-threshold image segmentation based on the Otsu criterion, where it produces clearer segmentation structures and better visual quality. The results demonstrate that MEIVYA is an effective and robust approach for both numerical optimization and artistic image segmentation.

Traditional manual phase adjustment technology has limitations in maintaining long-term three-phase balance, and the large-scale integration of new energy into low-voltage distribution networks further exacerbates the challenges of voltage limit violations and imbalance mitigation. This paper presents the design of a flexible self-balancing switch to convert single-phase loads into three-phase equivalents and proposes a configuration strategy for intelligent phase-shifting switches (IPSs) and flexible self-balancing switches (FSBSs) in distribution areas based on an improved ivy optimization algorithm. First, users are classified and their features are extracted. Then, the data features are integrated as boundary constraints for the switch siting model to form a candidate location set. An optimization model for switch configuration is constructed with the objectives of minimizing the three-phase-imbalance rate and maximizing lifecycle economic benefits. The ivy optimization algorithm is improved to achieve efficient solving of the model. Validated with measured data from a 10 kV distribution area of a power supply company, the proposed scheme can maintain the imbalance rate below 2%, reduce the voltage violation rate, and decrease the number of installed switches, thereby balancing the operational economy of distribution areas with improved power supply quality.

Vision is a fundamental sense that profoundly impacts daily life and independence. For visually impaired people (VIP), the absence or impairment of this sense presents significant challenges, particularly in navigating their environment and identifying objects independently. A considerable challenge for visually impaired individuals is the inability to navigate independently and identify objects, which restricts their daily activities and routines. Various investigations have been conducted in the domain of real-time object detection (OD) using deep learning (DL). DL-based techniques are shown to attain performance in OD. In this manuscript, an Intelligent Object Detection-Based Assistive System Using Ivy Optimisation Algorithm (IODAS-IOA) model is proposed for VIPs. The aim is to develop an effective OD system to help visually impaired persons navigate their environment safely and independently. To achieve this, the image pre-processing stage initially employs the Gaussian filtering (GF) method to eliminate noise. Moreover, a novel YOLOv12 method is employed for the OD process. Furthermore, the DenseNet161 method is used for the feature extraction process. Additionally, the bidirectional gated recurrent unit with attention mechanism (BiGRU-AM) method is implemented for classifying the extracted images. Finally, the parameter tuning process is performed using the Ivy optimization algorithm (IOA) method to improve classification performance. Extensive experimentation was performed to validate the performance of the IODAS-IOA approach under the indoor OD dataset. The experimental results of the IODAS-IOA approach emphasized the superior accuracy value of 99.74% over recent techniques.

In recent years, metaheuristic algorithms have garnered significant attention for their efficiency in solving complex optimization problems. However, their performance critically depends on maintaining a balance between global exploration and local exploitation; a deficiency in either can result in premature convergence to local optima or low convergence efficiency. To address this challenge, this paper proposes an enhanced ivy algorithm guided by a particle swarm optimization (PSO) mechanism, referred to as IVYPSO. This hybrid approach integrates PSO’s velocity update strategy for global searches with the ivy algorithm’s growth strategy for local exploitation and introduces an ivy-inspired variable to intensify random perturbations. These enhancements collectively improve the algorithm’s ability to escape local optima and enhance the search stability. Furthermore, IVYPSO adaptively selects between local growth and global diffusion strategies based on the fitness difference between the current solution and the global best, thereby improving the solution diversity and convergence accuracy. To assess the effectiveness of IVYPSO, comprehensive experiments were conducted on 26 standard benchmark functions and three real-world engineering optimization problems, with the performance compared against 11 state-of-the-art intelligent optimization algorithms. The results demonstrate that IVYPSO outperformed most competing algorithms on the majority of benchmark functions, exhibiting superior search capability and robustness. In the stability analysis, IVYPSO consistently achieved the global optimum across multiple runs on the three engineering cases with reduced computational time, attaining a 100% success rate (SR), which highlights its strong global optimization ability and excellent repeatability.

The combination of bentonite and conventional plastic concrete is an effective method for projecting structures and adsorbing heavy metals. Determining the compressive strength (CS) is a crucial step in the design of bentonite plastic concrete (BPC). Traditional experimental analyses are resource-intensive, time-consuming, and prone to high uncertainties. To address these challenges, several machine learning (ML) models, including support vector regression (SVR), artificial neural network (ANN), and random forest (RF), are generated to forecast the CS of BPC materials. To improve the prediction accuracy, a meta-heuristic optimization, called the Ivy algorithm, is integrated with Bayesian optimization (BOIvy) to optimize the ML models. Several statistical indices, including the coefficient of determination (R2), root mean square error (RMSE), prediction accuracy (U1), prediction quality (U2), and variance accounted for (VAF), are adopted to evaluate the predictive performance of all models. Additionally, Shapley additive explanation (SHAP) and sensitivity analysis are conducted to enhance model interpretability. The results indicate that the best model is the BOIvy-ANN model, which achieves the optimal indices during the testing. Moreover, water, curing time, and cement are found to be more influential on the prediction of the CS of BPC than other features. This paper provides a strong example of applying artificial intelligence (AI) techniques to estimate the performance of BPC materials.

The Ivy Algorithm (IVYA), a swarm intelligence algorithm inspired by plant growth, presents a novel framework for optimization. To unlock its full potential in complex, high-dimensional problems, it is crucial to address the fundamental challenge of balancing exploration and exploitation, which can impact overall search efficiency and solution quality. To this end, this paper proposes an Enhanced Ivy Algorithm (E-IVYA) that integrates three synergistic mechanisms. First, a dynamic perturbation framework combining symmetric and asymmetric exploration is introduced to maintain population diversity. Second, a dynamic escape mechanism based on elite differential mutation is employed to prevent search stagnation and effectively escape from local optima. Third, an adaptive movement strategy inspired by the Sine-Cosine Algorithm is integrated to achieve a more adaptive balance between global exploration and local exploitation. The performance of the proposed E-IVYA was rigorously evaluated through two distinct phases. Initially, its optimization capabilities were benchmarked against a wide range of classic and advanced algorithms on the challenging IEEE CEC 2014 and 2017 test suites. Subsequently, its practical utility was validated by applying it to the complex task of automating the hyperparameter optimization of Generative Adversarial Networks (GANs) for imbalanced data classification. The experimental results demonstrate E-IVYA’s superior performance. On the standard benchmarks, E-IVYA consistently ranked as a top-performing algorithm. In the practical application, the E-IVYA-optimized GAN model achieved a minority class F1-Score of 0.87 on the highly imbalanced Credit-Card Fraud dataset, significantly outperforming models augmented with standard techniques like SMOTE (0.71). These findings confirm that E-IVYA is a robust and efficient tool for tackling complex optimization problems, particularly in the domain of automated machine learning.

Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant challenges to conventional predictive models. Traditional approaches often fail to adequately capture these intricate relationships, resulting in limited prediction accuracy and poor generalization. Moreover, the high dimensionality and noisy nature of HPC mix data increase the risk of model overfitting and convergence to local optima during optimization. To address these challenges, this study proposes a novel bio-inspired hybrid optimization model, AP-IVYPSO-BP, which is specifically designed to handle the nonlinear and complex nature of HPC strength prediction. The model integrates the ivy algorithm (IVYA) with particle swarm optimization (PSO) and incorporates an adaptive probability strategy based on fitness improvement to dynamically balance global exploration and local exploitation. This design effectively mitigates common issues such as premature convergence, slow convergence speed, and weak robustness in traditional metaheuristic algorithms when applied to complex engineering data. The AP-IVYPSO is employed to optimize the weights and biases of a backpropagation neural network (BPNN), thereby enhancing its predictive accuracy and robustness. The model was trained and validated on a dataset comprising 1030 HPC mix samples. Experimental results show that AP-IVYPSO-BP significantly outperforms traditional BPNN, PSO-BP, GA-BP, and IVY-BP models across multiple evaluation metrics. Specifically, it achieved an R2 of 0.9542, MAE of 3.0404, and RMSE of 3.7991 on the test set, demonstrating its high accuracy and reliability. These results confirm the potential of the proposed bio-inspired model in the prediction and optimization of concrete strength, offering practical value in civil engineering and materials design.