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Feature Selection (FS) is an important data management technique that aims to minimize redundant information in a dataset. This work proposes DENGO, an improved version of the Northern Goshawk Optimization (NGO), to address the FS problem. The NGO is an efficient swarm-based algorithm that takes its inspiration from the predatory actions of the northern goshawk. In order to overcome the disadvantages that NGO is prone to local optimum trap, slow convergence speed and low convergence accuracy, two strategies are introduced in the original NGO to boost the effectiveness of NGO. Firstly, a learning strategy is proposed where search members learn by learning from the information gaps of other members of the population to enhance the algorithm's global search ability while improving the population diversity. Secondly, a hybrid differential strategy is proposed to improve the capability of the algorithm to escape from the trap of the local optimum by perturbing the individuals to improve convergence accuracy and speed. To prove the effectiveness of the suggested DENGO, it is measured against eleven advanced algorithms on the CEC2015 and CEC2017 benchmark functions, and the obtained results demonstrate that the DENGO has a stronger global exploration capability with higher convergence performance and stability. Subsequently, the proposed DENGO is used for FS, and the 29 benchmark datasets from the UCL database prove that the DENGO-based FS method equipped with higher classification accuracy and stability compared with eight other popular FS methods, and therefore, DENGO is considered to be one of the most prospective FS techniques. DENGO's code can be obtained at https://www.mathworks.com/matlabcentral/fileexchange/158811-project1 .

Feature selection (FS) is a pivotal technique in big data analytics, aimed at mitigating redundant information within datasets and optimizing computational resource utilization. This study introduces an enhanced zebra optimization algorithm (ZOA), termed FTDZOA, for superior feature dimensionality reduction. To address the challenges of ZOA, such as susceptibility to local optimal feature subsets, limited global search capabilities, and sluggish convergence when tackling FS problems, three strategies are integrated into the original ZOA to bolster its FS performance. Firstly, a fractional order search strategy is incorporated to preserve information from the preceding generations, thereby enhancing ZOA’s exploitation capabilities. Secondly, a triple mean point guidance strategy is introduced, amalgamating information from the global optimal point, a random point, and the current point to effectively augment ZOA’s exploration prowess. Lastly, the exploration capacity of ZOA is further elevated through the introduction of a differential strategy, which integrates information disparities among different individuals. Subsequently, the FTDZOA-based FS method was applied to solve 23 FS problems spanning low, medium, and high dimensions. A comparative analysis with nine advanced FS methods revealed that FTDZOA achieved higher classification accuracy on over 90% of the datasets and secured a winning rate exceeding 83% in terms of execution time. These findings confirm that FTDZOA is a reliable, high-performance, practical, and robust FS method.

Non-governmental organizations form a vital part of the social fabric in contemporary societies. They provide services and support to marginalized groups, enhance advocacy and awareness of social and environmental issues, and influence public policies. Given the lack of studies on non-governmental organizations operating in Jordan, it is crucial to understand their situations and shed the light on their methods to gain a competitive advantage. This study aims to analyze the mediation and sequential moderation linking the advantages of dynamic capabilities, differentiated strategies, social capital, common goals, and organization learning to produce competitive advantage from the perspective of strategy and organizational capacity. The study sample consisted of 100 employees working in non-governmental organizations in Jordan, who were selected in a simple random way.The findings reveal the factors that can influence business competitiveness by developing dynamic capabilities, differential strategic effects, and accumulating social capital. The study supports the organizational idea of learning to compete by emphasizing internal management practices (e.g., common goals, purposes, and social capital) and external attributes (e.g., differential strategies and dynamic capabilities). Finally, Jordanian companies should improve the links between their social capital, common goals, and dynamic capabilities to become competitive. AcknowledgmentThe study is funded by Alhussein Bin Talal University, fund decision (288/2022).

This paper addresses issues of inadequate accuracy and inconsistency between global search efficacy and local development capability in the black-winged kite algorithm for practical problem-solving by proposing a black-winged kite optimization algorithm that integrates the Osprey optimization algorithm and Crossbar enhancement (DKCBKA). Firstly, the adaptive index factor and the fusion Osprey Optimization Algorithm approach are incorporated to enhance the algorithm’s convergence rate, and the probability distribution factor is updated throughout the attack stage. Second, the stochastic difference variant method is implemented to prevent the method from entering the local optima. Lastly, the longitudinal and transversal crossover technique is incorporated to enhance the algorithm’s convergence accuracy and to dynamically alter the population’s global and individual optimal solutions. Fifteen benchmark functions are chosen to test the effectiveness of the enhanced algorithm and to compare the optimization efficiency of each technique. Simulation experiments are performed on the CEC2017 and CEC2019 test sets, revealing that the DKCBKA algorithm surpasses five standard swarm intelligence optimization methods and six improved optimization algorithms regarding solution accuracy and convergence speed. The superiority in meeting real optimization challenges is further demonstrated by the optimization of three real engineering optimization problems by DKCBKA, with optimization capabilities 18.222%, 99.885% and 0.561% higher than BKA, respectively.

The Crested Porcupine Optimizer (CPO), an emerging intelligent optimization algorithm, exhibits considerable potential for addressing complex engineering problems, yet its capabilities remain insufficiently investigated. Nevertheless, the original CPO is susceptible to premature convergence and suffers from insufficient population diversity. To effectively address these limitations, this paper proposes a multi-mechanism enhanced Crested Porcupine Optimizer (SDHCPO). Its core innovation lies in the integration of four key strategies: a Sobol-Opposition-Based Learning (Sobol-OBL) initialization strategy, which combines the Sobol sequence with opposition-based learning to generate an initial population that is more uniformly distributed in the high-dimensional search space; a cosine-annealing-based dynamic adjustment strategy that replaces the original random weights and substantially enhances convergence stability; the incorporation of the DE/rand/1 strategy in the first defense phase to disrupt positional dependence and prevent premature convergence; and a horizontal-vertical crossover strategy employed in the second defense phase to eliminate dimensional stagnation. Experimental results on two authoritative benchmark suites, CEC2017 and CEC2022, demonstrate that the proposed algorithm outperforms seven representative metaheuristic algorithms in terms of global exploration capability, local exploitation accuracy, and convergence robustness. Furthermore, empirical studies on five representative engineering design optimization problems show that SDHCPO consistently attains either the best-known solutions or highly competitive results reported in the literature, thereby further confirming its effectiveness and broad application potential for complex real-world engineering optimization tasks.

High-precision detection of hazardous gases with low refractive indices ranging from 1.000 to 1.100, specifically including methane, carbon monoxide, and sulfur dioxide, is critical for industrial safety, yet conventional sensors often suffer from limited sensitivity and severe thermal cross-sensitivity. This work presents a Magneto-Optical Differential Photonic Crystals Sensor (MO-DPCS) utilizing indium antimonide (InSb) to address these constraints. Employing the Multi-Objective Dragonfly Algorithm (MODA), the system was inversely optimized to maximize magneto-optical polarization splitting while rigorously maintaining an ultra-high transmission efficiency. Crucially, an angular interrogation architecture operating under oblique incidence was established to maximize the magneto-optical non-reciprocity, where the detection was realized by fixing the terahertz source frequency and monitoring the precise angular displacements of the steep spectral edges. A differential detection technique was employed to utilize the non-reciprocal phase changes wherein Transverse Electric (TE) and Transverse Magnetic (TM) modes display contrasting kinematic characteristics in the presence of an external magnetic field. The findings indicate that with an adjusted magnetic field of 0.033 T, the MO-DPCS attains an exceptional differential sensitivity of 30.8°/RIU, much above the 0.8°/RIU seen in the unmagnetized condition. The differential approach efficiently eliminates common-mode thermal noise, minimizing temperature-induced drift to below 0.35° across a 1 K range. The suggested MO-DPCS offers a robust, self-referencing solution for stable and high-sensitivity gas sensing applications with a detection limit of 4.18 × 10−4 RIU.

Through the analysis of the biological wastewater treatment process (WWTP), a multiobjective optimal control strategy is developed with the usage of energy consumption (EC) and effluent quality (EQ) as objectives to be optimized. To effectively handle the multiobjective optimization problem (MOP) with complex Pareto-optimal front (POF), an adaptive multiobjective evolutionary algorithm based on decomposition (AMOEA/D) is proposed in this paper. Since the efficiency of the multiple reference points and two-phase optimization strategies in solving MOPs with complex POFs has been proved. In the proposed AMOEA/D, an auto-switching strategy based on the aggregation function enhancement is designed to automatically make the algorithm switch from the first phase to the second phase. Besides, an adaptive differential evolution strategy is introduced into AMOEA/D to balance exploration and exploitation during the evolutionary process. Finally, the dynamic optimization, intelligent decision and bottom tracking control of the set-points of the dissolved oxygen and nitrate nitrogen in the WWTP are achieved via the combination of AMOEA/D with the self-organizing fuzzy neural network approximator and the self-organizing fuzzy neural network controller. The international benchmark simulation model No. 1 (BSM1) is utilized for experimental verification. Simulation results demonstrate that the proposed AMOEA/D can effectively reduce the EC of the WWTP under the premise of ensuring effluent parameters to meet the effluent discharge standards.

When using a differential evolution algorithm to solve the joint flood optimization scheduling problem of cascade reservoirs, a greedy random optimization strategy is prone to premature convergence. Therefore, a new, improved Elite Conservative Differential Evolution Algorithm (ECDE) was proposed in this study. This algorithm divides a population into elite and general populations. The elite population does not undergo differential mutation, whereas the general population uses an adaptive differential mutation strategy based on successful historical information to participate in differential mutation. This elite conservative strategy effectively improves the diversity of the population evolution process and enhances convergence accuracy and stability. In a numerical experiment involving 10 test functions, the proposed ECDE performed the best overall (seven functions had the best stable convergence solution, while the remaining three performed the best), while in the single-objective flood control optimization scheduling problem of cascade reservoirs in the middle reaches of the Gan River, some algorithms could not even stably converge to feasible solutions (taking the 1973 inflow as an example, the peak shaving rate of the ECDE calculation results was 3.4%, 13.72%, and 11.73% higher than those of SHADE, SaDE, and GA, respectively). The proposed ECDE algorithm outperformed the SHADE, SaDE, GA, PSO, and ABC algorithms in terms of both convergence accuracy and stability. Finally, ECDE was used to analyze the multi-objective flood control scheduling problem of cascade reservoirs in the middle reaches of the Gan River, and it was found that the weight setting in multi-objective optimization should follow an upstream priority program or equilibrium programs. Adopting a downstream priority program results in poor upstream flood control performance. The above analysis fully verifies the superiority of the proposed algorithm, which can be used to solve and analyze the joint optimization scheduling problem of cascade reservoirs.

The gradual deterioration of fresco pictorial information presents a formidable obstacle for conservators dedicated to protecting humanity’s shared cultural legacy. Currently, scholars in the field of mural conservation predominantly focus on image segmentation techniques as a vital tool for facilitating mural restoration and protection. However, the existing image segmentation methods frequently fall short of delivering optimal segmentation results. To address this issue, this study introduces a novel mural image segmentation approach termed NDFNGO, which integrates a nonlinear differential learning strategy, a decay factor, and a Fractional-order adaptive learning strategy into the Northern Goshawk Optimization (NGO) algorithm to enhance segmentation performance. Firstly, the nonlinear differential learning strategy is incorporated to harness the diversity and adaptability of differential tactics, thereby augmenting the algorithm’s global exploration capabilities and effectively improving its ability to pinpoint optimal segmentation threshold regions. Secondly, drawing on the properties of nonlinear functions, a decay factor is proposed to achieve a more harmonious balance between the exploration and exploitation phases. Finally, by integrating historical individual data, the Fractional-order adaptive learning strategy is employed to reinforce the algorithm’s exploitation capabilities, thereby further refining the quality of image segmentation. Subsequently, the proposed method was evaluated through tests on twelve mural image segmentation tasks. The results indicate that the NDFNGO algorithm achieves victory rates of 95.85%, 97.9%, 97.9%, and 95.8% in terms of the fitness function metric, PSNR metric, SSIM metric, and FSIM metric, respectively. These findings demonstrate the algorithm’s high performance in mural image segmentation, as it retains a significant amount of original image information, thereby underscoring the superiority of the technology proposed in this study for addressing this challenge.

The prediction of remaining useful life (RUL) of lithium-ion batteries takes a critical effect in the battery management system, and precise prediction of RUL guarantees the secure and reliable functioning of batteries. For the difficult problem of selecting the parameter kernel of the training data set of the RUL prediction model constructed based on the support vector regression model, an intelligent gray wolf optimization algorithm is introduced for optimization, and owing to the premature stagnation and multiple susceptibility to local optimum problems of the gray wolf algorithm, a differential evolution strategy is introduced to propose a hybrid gray wolf optimization algorithm based on differential evolution to enhance the original gray wolf optimization. The variance and choice operators of differential evolution are designed to sustaining the diversity of stocks, and then their crossover operations and selection operators are made to carry out global search to enhance the prediction of the model and realize exact forecast of the remaining lifetime. Experiments on the NASA lithium-ion battery dataset demonstrate the effectiveness of the proposed RUL prediction method. Experimental results demonstrate that the maximum average absolute value error of the prediction of the fusion algorithm on the battery dataset is limited to within 1%, which reflects the high accuracy prediction capability and strong robustness.