Case adaptation is crucial for case-based reasoning (CBR) because the solutions of old cases are not always the ideal answer for the encountered new problem. It is employed to solve new problems by utilizing the adaptation knowledge extracted from similar ones encountered in the past. The traditional adaptation method solves a new problem in the principle of k- nearest neighbor ( $k$ -NN), and the adaptation model was built based on $k$ similar cases. Yet, the $k$ similar cases retrieved by new case may locate in different case clusters in the case base composed of multiple case clusters. This article presents a new case adaptation method by the combination of multi-adaptation engines from different case clusters to improve the adaptation accuracy. First, the input and output of the cluster-based adaptation engine are established from the old cases to distill the adaptation knowledge in each case cluster. Then, the multivariable CBR adaptation engine based on multiple-output support vector regression (MSVR) is built for case adaptation. Furthermore, inspired by the fact that the training sample which contains two closet cases can provide more useful information than others, and reduce the impact of outliers, this study adds the hybrid weight into MSVR, and allocates high weights to the information provided by such high sample density and similarity samples during multi-dimensional regression estimation. Finally, the solution of the target case is gathered by incorporating the output of different adaptation engines. The proposed method was applied to the equipment maintenance cost prediction and compared with traditional statistical-based and machine learning-based methods. Empirical comparison results indicated that the proposed adaptation method could achieve the best performance by utilizing the adaptation knowledge in different clusters under multi-case clusters environment.

The scheduled maintenance cost of warships is the essential prerequisite and economic foundation to guarantee the effective implementation of maintenance, which directly influences the quality and efficiency of maintenance operations. This paper proposes a multi-target regression algorithm based on multi-layer sparse structure (MTR-MLS) algorithm, to achieve simultaneous prediction of the subentry costs of warship scheduled maintenance, and the total cost of the maintenance is estimated by summing the predicted values of the different subentry costs. In MTR-MLS, the kernel technique is employed to map the inputs to the higher dimensional space for decoupling the complex input–output nonlinear relationships. By deploying the structure matrix, MTR-MLS achieves a latent variable model which can explicitly encode the inter-target correlations via l_2,1-norm-based sparse learning. Meanwhile, the noises are encoded to diminish the influence of noises while exploiting the correlations among targets. An alternating optimization algorithm is proposed to solve the objective function. Extensive experimental evaluation on real-world datasets and datasets of warships scheduled maintenance cost show that the proposed method consistently outperforms the state-of-the-art algorithms, which demonstrates its great effectiveness for cost prediction of warships scheduled maintenance.

To address the challenge of multimodal anomaly data governance in ship maintenance-cost prediction, this study proposes a three-stage hybrid data-cleansing framework integrating physical constraints and intelligent optimization. First, we construct a multi-dimensional engineering physical constraints rule base to identify contradiction-type anomalies through ship hydrodynamics validation and business logic verification. Second, we develop a Feature-Weighted Isolation Forest Algorithm (W-iForest) algorithm that dynamically optimizes feature selection strategies by incorporating rule triggering frequency and expert knowledge, thereby enhancing detection efficiency for discrete-type anomalies. Finally, we create a Genetic Algorithm-Ant Colony Optimization Collaborative Random Forest (GA-ACO-RF) to resolve local optima issues in high-dimensional missing data imputation. Experimental results demonstrate that the proposed method achieves a physical compliance rate of 88.2% on ship-maintenance datasets, with a 25% reduction in RMSE compared to conventional prediction methods, validating its superior data governance capability and prediction accuracy under complex operating conditions. This research establishes a reliable data preprocessing paradigm for maritime operational assurance, exhibiting substantial engineering applicability in real-world maintenance scenarios.

The development of equipment research projects is replete with uncertainties, complicating the provision of accurate and objective evaluations of their development costs. To enhance the precision of cost predictions for such projects, it is critical to assess and integrate the cost‐risk level. This study introduces an improved two‐dimensional cloud model (TDCM) that effectively integrates qualitative concepts and quantitative data for analyzing equipment projects. First, the principal factors influencing cost are thoroughly evaluated, and an assessment index system is structured with three primary and twelve secondary indicators. The probability of budget overrun and its impact are designated as the primary variables for assessing each indicator, aligning with the definition of cost risk. Second, this research merges the fuzzy analytic hierarchy process (FAHP) with the criteria importance though intercriteria correlation (CRITIC) method, employing coalitional game theory to ascertain the weights of each assessment indicator. Third, a TDCM is developed to derive cloud eigenvalues, and a two‐dimensional cloud diagram is constructed through MATLAB to preliminarily ascertain the risk level, with the degree of nearness subsequently calculated to refine these results. Fourth, each indicator in the assessment system is treated as a node to construct a Bayesian network (BN) based on logical relationships, and a sensitivity analysis is conducted to identify sensitive indicators. Fifth, the development of a specific mine countermeasure (MCM) weapon system is examined as a case study, incorporating relevant existing data into the improved model. The validity and feasibility of the model are corroborated by comparing it with traditional methods. The results affirm that the enhanced TDCM effectively navigates the ambiguity and randomness inherent in cost‐risk assessment data, providing a reference for similar scientific research projects.

Software effort estimation (SEE) serves as a cornerstone of effective software project management, and case-based reasoning (CBR) stands out as one of the most extensively adopted approaches within this domain. Nevertheless, CBR-based SEE models are still plagued by two critical challenges: conventional case retrieval mechanisms lack the ability to differentiate the relative importance of various features, and data scarcity remains a persistent bottleneck. Both issues significantly compromise the estimation accuracy and interpretability of the models. To address these limitations, we propose a SHAP–Mixup synergistic framework that enhances both feature-aware similarity learning and data distribution modeling. Specifically, we introduce (1) a stability-aware SHAP-weighted similarity metric that integrates both the magnitude and variance of feature contributions to improve retrieval robustness, and (2) a density-aware Mixup augmentation strategy that generates synthetic samples guided by local data manifold structure rather than random interpolation. Experimental results on seven benchmark datasets demonstrate that the proposed method reduces MAE and MSE by up to 20.2% on average compared to baseline CBR models, while consistently improving Pred(0.25). Furthermore, by enhancing model interpretability, the proposed method equips project managers with actionable insights into the key drivers of software effort, thereby facilitating more informed and efficient resource allocation. Building on these findings, this study provides a novel and effective pathway for developing SEE models that are more accurate, robust, and transparent.

To address the data quality challenges in ship maintenance data, such as high missing rates, anomalous noise, and multi-source heterogeneity, this paper proposes a data quality enhancement method based on a multi-strategy enhanced Harris Hawks Optimization algorithm for optimizing the Kolmogorov–Arnold Stacked Denoising Autoencoder. First, leveraging the Kolmogorov–Arnold theory, the fixed activation functions of the traditional Stacked Denoising Autoencoder are reconstructed into self-learnable B-spline basis functions. Combined with a grid expansion technique, the KASDAE model is constructed, significantly enhancing its capability to represent complex nonlinear features. Second, the Harris Hawks Optimization algorithm is enhanced by incorporating a Logistic–Tent compound chaotic map, an elite hierarchy strategy, and a nonlinear logarithmic decay mechanism. These improvements effectively balance global exploration and local exploitation, thereby increasing the convergence accuracy and stability for hyperparameter optimization. Building on this, an IHHO-KASDAE collaborative cleaning framework is established to achieve the repair of anomalous data and the imputation of missing values. Experimental results on a real-world ship maintenance dataset demonstrate the effectiveness of the proposed method: it achieves an 18.3% reduction in reconstruction mean squared error under a 20% missing rate compared to the best baseline method; attains an F1-score of 0.89 and an AUC value of 0.929 under a 20% anomaly rate; and stabilizes the final fitness value of the IHHO optimizer at 0.0216, which represents improvements of 31.7%, 25.6%, and 12.2% over the Particle Swarm Optimization, Differential Evolution, and the original HHO algorithm, respectively. The proposed method outperforms traditional statistical methods, deep learning models, and other intelligent optimization algorithms in terms of reconstruction accuracy, anomaly detection robustness, and algorithmic convergence stability, thereby providing a high-quality data foundation for subsequent applications such as maintenance cost prediction and fault diagnosis.