In order to identify tea varieties quickly, efficiently and nondestructively, an adaptive improved possibilistic c‐means (AIPCM) clustering with the fuzzy Mahalanobis distancewas proposed to classify the Fourier transform infrared reflectance (FTIR) spectra of tea samples.Three varieties of tea samples were scanned andFTIR spectra were acquired for 96 tea samples of different geographical originsby using the FTIR‐7600 infrared spectrometer. Multiple scatter correction (MSC) was employed to eliminate light scattering of FTIR spectral data. After that, principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to reduce the dimensionality of FTIR spectra and extract the discriminant information, respectively. Thenthe data were clustered byseveral fuzzy clusteringalgorithms. AIPCM had the highest clustering accuracyand its accuracy achieved up to 98.5%. The experimental results showed that FTIR spectroscopy coupled with AIPCM clustering was superior in classification of tea varieties.

With the expansion of unmanned aerial vehicles (UAVs) into complex three-dimensional (3D) terrains for reconnaissance, rescue, and related missions, traditional path planning methods struggle to meet multi-constraint and multi-objective requirements. Existing swarm intelligence algorithms, limited by the “no free lunch” theorem, also face challenges when the standard Information Acquisition Optimizer (IAO) is applied to such tasks, including low exploration efficiency in high-dimensional search spaces, rapid loss of population diversity, and improper boundary handling. To address these issues, this study proposes a Multi-Strategy Enhanced Information Acquisition Optimizer (MEIAO). First, a Levy Flight-based information collection strategy is introduced to leverage its combination of short-range local searches and long-distance jumps, thereby broadening global exploration. Second, an adaptive differential evolution operator is designed to dynamically balance exploration and exploitation via a variable mutation factor, while crossover and greedy selection mechanisms help maintain population diversity. Third, a globally guided boundary handling strategy adjusts out-of-bound dimensions to feasible regions, preventing the generation of low-quality paths. Performance was evaluated on the CEC2017 (dim = 30/50/100) and CEC2022 (dim = 10/20) benchmark suites by comparing MEIAO with eight algorithms, including VPPSO and IAO. Based on the mean, standard deviation, Friedman mean rank, and Wilcoxon rank-sum tests, MEIAO demonstrated superior performance in local exploitation of unimodal functions, global exploration of multimodal functions, and complex adaptation on composite functions while exhibiting stronger robustness. Finally, MEIAO was applied to 3D mountainous UAV path planning, where a cost model considering path length, altitude standard deviation, and turning smoothness was established. The experimental results show that MEIAO achieved an average path cost of 253.9190, a 25.7% reduction compared to IAO (341.9324), with the lowest standard deviation (60.6960) among all algorithms. The generated paths were smoother, collision-free, and achieved faster convergence, offering an efficient and reliable solution for UAV operations in complex environments.

Accurate parameter estimation of photovoltaic (PV) models is fundamentally a challenging nonlinear optimization problem, characterized by strong nonlinearity, high dimensionality, and multiple local optima. These characteristics significantly hinder the convergence accuracy, stability, and efficiency of conventional metaheuristic algorithms when applied to PV parameter identification. Although the enterprise development (ED) optimization algorithm has shown promising performance in various optimization tasks, it still suffers from slow convergence, limited solution precision, and poor robustness in complex PV parameter estimation scenarios. To overcome these limitations, this paper proposes a multi-strategy enhanced enterprise development (MEED) optimization algorithm for high-precision PV model parameter estimation. In MEED, a hybrid initialization strategy combining chaotic mapping and adversarial learning is designed to enhance population diversity and improve the quality of initial solutions. Furthermore, a historical trend-guided position update mechanism is introduced to exploit accumulated search information and accelerate convergence toward the global optimum. In addition, a mirror-reflection boundary control strategy is employed to maintain population diversity and effectively prevent premature convergence. The proposed MEED algorithm is first evaluated on the IEEE CEC2017 benchmark suite, where it is compared with 11 state-of-the-art metaheuristic algorithms under 30-, 50-, and 100-dimensional settings. Quantitative experimental results demonstrate that MEED achieves superior solution accuracy, faster convergence speed, and stronger robustness, yielding lower mean fitness values and smaller standard deviations on the majority of test functions. Statistical analyses based on Wilcoxon rank-sum and Friedman tests further confirm the significant performance advantages of MEED. Moreover, MEED is applied to the parameter estimation of single-diode and double-diode PV models using real measurement data. The results show that MEED consistently attains lower root mean square error (RMSE) and integrated absolute error (IAE) than existing methods while exhibiting more stable convergence behavior. These findings demonstrate that MEED provides an efficient and reliable optimization framework for PV model parameter estimation and other complex engineering optimization problems.

The Educational Competition Optimizer (ECO) formulates search as a three-stage didactic process—primary, secondary and tertiary learning—but the original framework suffers from scarce information exchange, sluggish late-stage convergence and an unstable exploration–exploitation ratio. We present EECO, which introduces three synergistic mechanisms: a regenerative population strategy that uses the covariance matrix of elite solutions to maintain diversity, a Powell mechanism that accelerates exploitation within promising regions, and a trend-driven update that adaptively balances exploration and exploitation. EECO was evaluated on the 29 benchmark functions of CEC-2017 and nine real-world constrained engineering problems. Results show that EECO delivers higher solution accuracy and markedly smaller standard deviations than eight recent algorithms, including EDECO, ISGTOA, APSM-jSO, LSHADE-SPACMA, EOSMA, GLSRIME, EPSCA, and ESLPSO. Across the entire experimental battery, EECO consistently occupied the first place in the Friedman hierarchy: it attained average ranks of 2.138 in 10-D, 1.438 in 30-D, 1.207 in 50-D, and 1.345 in 100-D CEC-2017 benchmarks, together with 1.722 on the nine real-world engineering problems, corroborating its superior and dimension-scalable performance. The Wilcoxon rank sum test confirms the statistical significance of these improvements. With its remarkable convergence accuracy and reliable stability, EECO emerges as a promising variant of the ECO algorithm.

Pesticide residues exceeding the standard in Chinese cabbage is harmful to human health.

The Educational Competition Optimizer (ECO) shows promise on simple tasks but struggles with high-dimensional and complex landscapes due to rigid stage division and limited search operators. This paper proposes a Hybrid Strategy Enhanced ECO (HSECO) featuring: (i) a self-adaptive parameter evolution mechanism for individual-level flexibility, (ii) a multi-operator adaptive selection scheme switching between learning and differential evolution strategies based on real-time feedback, and (iii) an archive-assisted diversity preservation module to mitigate premature convergence. HSECO is validated on CEC2017, CEC2020 and CEC2022, and a continuous engineering benchmark. Statistical tests confirm its superiority over nine State-of-the-Art and parameter-free algorithms in accuracy, convergence speed, and robustness. Ablation and diversity analyses verify its balanced exploration–exploitation dynamics. Finally, HSECO is applied to a three-dimensional UAV path-planning problem, where path length, altitude variation, and turning smoothness are integrated into a single fitness function using a weighted-sum formulation. Therefore, from a metaheuristic optimization perspective, the UAV case is treated as a single-objective constrained optimization problem rather than a Pareto-based multi-objective problem. Experimental results show that HSECO obtains shorter, safer, and smoother trajectories with lower overall weighted fitness.