The Flexible Job Shop Scheduling Problem (FJSP) is central to smart manufacturing, yet standard algorithms often prioritize productivity (makespan) at the expense of cost and reliability. This paper introduces t-MOHHO, a collaborative optimization framework designed to equilibrate machine load, processing costs, and delivery timeliness alongside throughput. By incorporating an adaptive Student’s t-distribution mutation operator and a non-linear energy escape mechanism, t-MOHHO effectively navigates high-dimensional search spaces. Extensive validation on 10 MK benchmark instances reveals that t-MOHHO demonstrates significant advantages over classic HHO, MOPSO, and MOEA/D across most metrics. Notably, in comparison to the state-of-the-art NSGA-III, t-MOHHO executes a clear trade-off: it trades marginal makespan efficiency for substantial reductions in cost and tardiness. Specifically, on the large-scale MK10 instance, t-MOHHO reduces total tardiness by 56.2% and lowers processing costs by 3.4% compared to NSGA-III. These results demonstrate that t-MOHHO can strategically sacrifice maximum speed to secure superior punctuality and cost-efficiency, making it a robust decision-support tool for Just-in-Time (JIT) production environments.

In response to the decline in population diversity, the imbalance between exploration and exploitation, and the low convergence efficiency in the middle and later stages of the Red-billed Blue Magpie Optimizer (RBMO) when addressing complex optimization problems, this study proposes a multi-strategy enhanced variant termed CLD-RBMO. The proposed algorithm improves the original search mechanism from three perspectives: strengthened global exploration, enhanced local refinement, and directed exploitation in the middle and later stages. During the exploration phase, a hierarchical perturbation mechanism based on Logistic chaotic mapping and Lévy flight is introduced to enhance randomness and spatial coverage in the early search process. In the local exploitation phase, a Cauchy-Gauss hybrid mutation operator is employed to improve the algorithm's capability to escape from local optima. In the middle and later search stages, a stochastic differential mutation strategy is incorporated to provide population-structure-based directional guidance for individuals, thereby accelerating convergence and improving optimization accuracy. Simulation results on the CEC2017 benchmark test functions indicate that CLD-RBMO demonstrates clear superiority over the original algorithm and several representative swarm intelligence optimization algorithms in terms of optimization accuracy, stability, and overall performance ranking. Convergence curve analysis confirms its dynamic performance improvements across different search stages, and the Wilcoxon rank-sum test further statistically validates the significance of the performance enhancement achieved by the proposed improvements compared with the original algorithm. Moreover, evaluations on two representative mechanical engineering optimization case studies further demonstrate the algorithm's strong stability and engineering generalization capability.

This paper presents Ant Colony Optimization (ACO) along with genetic algorithm-based optimization technique for edge detection. The problem of edge detection is formulated as one of choosing a minimum cost edge configuration. ACO can be used to find good solutions to combinatorial optimization problems that can be transformed into the problem of finding good paths through a weighted construction graph. Similarly, the genetic algorithm views edge configurations as two-dimensional chromosomes with fitness values inversely proportional to their costs. The design of the crossover and the mutation operators in the context of the twodimensional chromosomal representation is described. In this paper, an edge detection technique that is based on ACO and genetic algorithm is presented. The proposed method establishes a pheromone matrix that represents the edge information at each pixel based on the routes formed by the ants dispatched on the image. The movement of the ants is guided by the local variation in the image’s intensity values. The proposed ACO-based edge detection method takes advantage of the improvements introduced in ant colony system, one of the main extensions to the original ant system.In genetic algorithm, the design of the crossover and the mutation operators in the context of the two-dimensional chromosomal representation is described. The knowledgeaugmented mutation operator which exploits knowledge of the local edge structure is shown to result in rapid convergence. The incorporation of meta-level operators and strategies such as the elitism strategy and various combinations of meta-level operators can be tested on synthetic and natural images.

When ground transportation is disrupted by natural disasters, airdropped rescue vehicles require energy-absorbing cushioning devices to prevent landing impact damage. Thin-walled circular tubes are preferred for their high energy absorption capacity and structural efficiency. However, to reduce platform force fluctuations and decrease residual stroke after compression, thereby avoiding unbalanced loading and ensuring post-landing mobility, slots are introduced into the tube wall, which renders the mean crushing force (MCF) difficult to predict accurately using conventional methods. To address this issue, this paper proposes a physics–data-driven method for predicting the energy absorption characteristics of slotted thin-walled circular tubes. The engineering scenario is introduced, followed by comparative validation via drop weight tests and impact simulations to obtain a sample set via design of experiments (DOE). A multi-layer perceptron (MLP) neural network then augments the samples to generate a dataset. Dimensional analysis yields candidate MCF prediction equations, whose forms and coefficients are determined via a physics–data-driven approach. Weighted graph encoding transforms the equation-solving problem into a graph optimization problem to reduce the computational complexity, and an improved differential evolution (DE) algorithm with a dual-adaptive mutation operator (DSADE) adjusts the parameters and accelerates convergence. The resulting MCF prediction formula, combined with drop test requirements as the optimization objective, achieves a simulation relative error below 5%. These parameters also satisfy engineering requirements in actual airdrop tests, confirming the method’s effectiveness in predicting the energy absorption characteristics of slotted thin-walled tubes.

Solving constrained multiobjective optimization problems (CMOPs) is highly challenging due to the presence of complicated feasible regions, intense conflicts among objectives, and unevenly distributed constraints. As a result, conventional methods relying on a single constraint-handling mechanism frequently fail to maintain a stable equilibrium among solution feasibility, diversity, and convergence. To overcome these bottlenecks, this article introduces AFFCMO, a novel adaptive feasibility-guided framework tailored for constrained multiobjective optimization. At its core, the proposed approach utilizes a coevolutionary dual-population architecture that divides the search process into two distinct tasks. Specifically, an auxiliary population is tasked with global exploration, while a primary population focuses on the intensive exploitation of discovered feasible areas. To achieve this, the primary population leverages a DE/current-to-pbest/1 differential evolution strategy to closely approximate the constrained Pareto front. Simultaneously, the auxiliary population expands the search space using a mutation operator that adapts to the current evolutionary stage. Furthermore, exploration is bolstered by a multicriterion environmental selection scheme designed for the auxiliary group. By combining Euclidean geometric distributions, constraint relaxation, and value modeling inspired by epidemic dynamics, this strategy successfully preserves valuable infeasible solutions that can guide the search. Additionally, a dynamic resource allocation strategy based on historical search feedback and Thompson sampling is incorporated. This mechanism continuously evaluates the recent search contributions of both populations and adaptively adjusts their offspring sizes, thereby reducing the bias introduced by static allocation schemes. This mechanism continuously assesses the actual search contributions of both populations, allowing for the adaptive resizing of offspring generations and thereby eliminating the inherent biases of static allocation methods. Comprehensive empirical evaluations are conducted on 47 benchmark problems from four distinct test suites. The results indicate that AFFCMO significantly outperforms seven contemporary multiobjective evolutionary algorithms in terms of exploring complex feasible regions, preserving solution diversity, and achieving high convergence accuracy.

To solve the problems of poor fuel atomization effect, low combustion efficiency, and uneven temperature distribution of the evaporator tube of a certain micro turbojet engine, a structural optimization design method based on a genetic algorithm is proposed. Taking the inner diameter of the evaporator tube, the diameter of the nozzle hole, the number of nozzle holes as design variables, the fuel atomization particle size (d50), combustion efficiency (η), and maximum wall temperature (Tmax) as optimization objectives, a multi-objective optimization mathematical model is established. The iterative optimization is carried out through the selection, crossover, and mutation operations of the genetic algorithm, and the optimization effect is verified by combining CFD (Computational Fluid Dynamics) numerical simulation. The results show that when the inner diameter of the evaporator tube is 2.6 mm, the diameter of the nozzle hole is 0.8 mm and the number of nozzle holes is eight, the fuel atomization particle size of the evaporator tube is reduced by 18.3%, the combustion efficiency is increased by 7.6%, and the maximum wall temperature is decreased by 12.4%, which significantly improves the working performance of the evaporator tube and provides an effective reference for the optimization design of key components of micro turbojet engines.

Global optimization of complex, high-dimensional landscapes remains a fundamental challenge in scientific and engineering domains. To mitigate the inherent limitations of premature convergence and diversity loss, this paper proposes CLGMESC, an enhanced variant of the Escape Algorithm (ESC). The proposed algorithm integrates a dimension-wise comprehensive learning (CL) strategy with a hybrid Cauchy-Gaussian mutation (HCGM) operator. The CL strategy reconfigures the learning paradigm for stagnant individuals, enabling them to construct exemplars from multiple high-quality peers and thereby restore population diversity. Synergistically, the HCGM operator utilizes an adaptive weighting mechanism to dynamically balance heavy-tailed Cauchy mutations for global exploration and thin-tailed Gaussian mutations for local refinement, effectively facilitating escapes from local optima. Comprehensive evaluations on the CEC2017 benchmark suite demonstrate that CLGMESC achieves the top rank among ten advanced metaheuristics (including SBO, BBO, PO, DE, PSO, SMA, CPA, and MGO), with Wilcoxon signed-rank tests confirming its statistical superiority ([Formula: see text]) across the majority of test functions. Furthermore, the practical efficacy of CLGMESC was validated through a reservoir production optimization problem using the three-dimensional Egg Model ([Formula: see text] grid). In determining optimal well controls, CLGMESC achieved the highest Net Present Value ([Formula: see text] USD) with the lowest standard deviation, thus substantiating its reliability and robustness in solving computationally intensive real-world engineering problems. The consistently high rankings across diverse benchmarks and the substantial economic gains in the reservoir simulation underscore the algorithm's pronounced capability to maintain a robust exploration-exploitation balance and dynamically escape local optima in demanding parameter spaces.

In the textile industry, the challenge of cutting multiple pieces of equal length from n rolls of varying lengths frequently arises after quality inspection, often resulting in significant material waste. This task is further complicated by the need to optimize fabric quality, posing a major challenge for manufacturers. This paper presents two genetic algorithm (GA)-based approaches that integrate defect distribution into the fabric cutting process. The first method, Genetic Algorithm for Roll Cutting Length (GARCL), employs adapted chromosome structures and customized crossover and mutation operators to evenly disperse defects. The second approach, Defect Locator Genetic Algorithm (DLGA), focuses on identifying defect clusters and strategically removing them to enhance overall fabric quality. Experiments on real-world industrial cases demonstrate that both methods perform effectively across diverse scenarios, achieving up to a 47% improvement in fabric quality and a 5% reduction in fabric utilization compared to traditional cutting practices. These findings highlight the potential of the proposed methods to minimize trim loss while improving product quality in textile manufacturing.

Ontology is a kernel technique of the semantic web, which defines concepts, properties, and their relationships to establish a shared understanding of domain knowledge. Ontology matching identifies semantically similar entities across different ontologies, which uses similarity features to measure their similarity from different perspectives. However, due to the complexity of the entity heterogeneity, no single similarity feature is universally effective. In recent years, genetic algorithms have proven effective in constructing similarity features for ontology matching, but their potential is limited by the reliance on default classification strategies, empirical determination of the number of high-level features, the requirement for manually selecting, combining these features, and tuning the associated combination parameters. To overcome these drawbacks, we propose a multi-layer hybrid genetic programming approach to automatically construct high-level similarity features. This approach includes three novel components. First, a new multi-layer individual representation is designed, which faciliates the algorithm to adaptively explore the search space of constructing high-level similarity features. Second, to enhance the search effectiveness, a new initialization method and a mutation operator are developed, which use a weight-based strategy to adaptively select and construct a more diverse set of similarity features. Third, a compact genetic algorithm-based optimizer is designed to refine the tree structures of elite individuals. The experimental results on the ontology alignment evaluation initiative’s benchmark show that our algorithm can generate high-quality ontology matching results across various matching tasks, significantly outperforming the state-of-the-art ontology matching methods.

Prey capture enhanced Harris hawks optimizer for wrapper-based feature selection in high-dimensional medical data.

Cold-atom absolute gravimeters are widely used for measuring the acceleration of gravity, yet their sensitivity is often limited by ground vibrations. Existing vibration compensation algorithms struggle to strike a balance between search accuracy and computational efficiency and are prone to local optima. Here, we propose an improved whale optimization algorithm (IWOA) to address these issues. By combining Logistic-LHS (Latin hypercube sampling) chaotic initialization, adaptive adjustment, and a Gaussian mutation operator to prevent premature convergence, IWOA achieves higher search efficiency and superior sensitivity than traditional algorithms. The method is validated through multiple simulation studies and further assessed experimentally on the NIM-AGRb-1 cold-atom gravimeter system. The results show that IWOA reduces the uncertainty of the fitted phase parameter by 66%. The Pearson correlation between atomic transition probability and the calculated phase increases to a maximum of 0.98, and the gravity sensitivity improves to 47 μGal/Hz when the evolution time T is 80 ms.

Accurate parameter identification is essential for reliable photovoltaic (PV) modeling. Although metaheuristic algorithms have been widely used for this task, many methods still suffer from slow convergence and suboptimal solutions. To improve both accuracy and efficiency, we propose a two-stage framework that combines maximum power matching (MPM) with a multistrategy enhanced northern goshawk optimization (MSENGO) algorithm. First, the measured current–voltage ( I – V ) data are preprocessed to remove outliers and reduce redundancy. Next, MPM provides initial parameter estimates, which are then refined by MSENGO. MSENGO incorporates three complementary mechanisms: tent-chaotic initialization for enhancing population diversity, a wavelet-based mutation operator for intensified local refinement, and a nonlinear time-varying coordination schedule (sine-decreasing and cosine-increasing) to adaptively regulate the exploration–exploitation trade-off. On the CEC2017 benchmark set (F1–F12), MSENGO attains the theoretical optimum on 11 out of 12 functions and exhibits faster convergence than the compared optimizers. For PV parameter identification under three irradiance levels (379, 590, and 900 W/m 2 ), MSENGO achieves root mean square error (RMSE) values of 0.00717, 0.00655, and 0.00651 A, respectively, with R 2 = 0.9999 in all cases and computation times of 12.15–13.02 s. Compared with the best baseline method in each irradiance case, the RMSE reduction reaches approximately 12–48%, demonstrating clear accuracy and efficiency advantages. The proposed framework also maintains competitive performance when extended to more complex PV models (double-diode model and triple-diode model), indicating good generality.

Increasing uncertainties in electricity prices, load demand, and renewable energy generation pose significant challenges for optimal microgrid operation in deregulated electricity markets. This paper proposes a self-adaptive Gravitational Search Algorithm (SGSA), which enhances the standard GSA by incorporating a self-adaptive mutation operator with two movement strategies to mitigate premature convergence and improve solution quality. To model uncertainties in load demand, market prices, and renewable outputs, the 2 m-Point Estimation Method (PEM) is employed as a computationally efficient alternative to conventional stochastic approaches. The proposed SGSA-PEM framework is applied to a low-voltage microgrid consisting of microturbines, phosphoric acid fuel cells, photovoltaic units, wind turbines, and battery storage. Simulation results indicate that the integration of battery storage reduces the total generation cost by up to 49.7%, while renewable energy penetration increases by approximately 10% during peak demand periods. Furthermore, comparative analysis shows that SGSA achieves lower operating costs and converges about 25% faster than standard GSA and Particle Swarm Optimization (PSO). The results confirm that the proposed framework provides a computationally efficient and robust solution for probabilistic microgrid energy management under uncertainty.

Abstract Multi-objective feature selection (MOFS) aims to identify the most discriminative feature subset by simultaneously optimizing two conflicting objectives: minimizing the number of selected features and reducing the classification error rate. However, current MOFS algorithms face several challenges, including uneven population initialization, mutation operators that overlook feature correlations, and crowding in dense regions that reduces solution diversity. These issues collectively compromise search efficiency and the convergence quality of the Pareto front. In this paper, we present an enhanced differential evolution methodology designed to seek multiple optimal feature subsets. First, a balanced diversity initialization strategy is introduced that leverages feature weights and redundancy indices to enhance both diversity and uniformity within the initial population. Subsequently, a mutation strategy informed by these weights and redundancy guides mutations while employing non-dominated sorting to prioritize solutions with lower classification errors, thereby balancing global exploration with local exploitation. Finally, a grid-aware crowding regulation approach is proposed to identify dense areas in objective space and eliminate redundant solutions. Experimental results derived from 13 UCI datasets of varying complexity illustrate that our proposed method significantly outperforms several state-of-the-art MOFS techniques in terms of feature selection efficacy.

To address the rapid population diversity loss and premature convergence of the Artificial Lemming Algorithm (ALA) in complex optimization problems, this paper proposes an Improved Artificial Lemming Algorithm (IALA) with multi-strategy enhancements inspired by lemming behavior. First, a non-uniform mutation operator and a nonlinear step-size strategy are introduced to strengthen local optima escape capability and optimization precision. Second, inspired by the foraging and positioning behavior of lemmings, a relative advantage learning strategy is designed to reduce dependence on the global best individual, further enhancing the algorithm's exploration ability. Finally, a Q-learning-based adaptive mechanism is integrated to intelligently orchestrate five lemming-inspired behavioral modes through a nonlinear reward function, enabling adaptive switching among search patterns. Comparative experiments on the CEC2022 benchmark suite demonstrate that IALA achieves a Friedman mean rank of 1.25, ranking first with a significant margin. Compared with the original ALA and other six classical and state-of-the-art metaheuristic algorithms, and four recently proposed improved ALA variants (EALA, IALA_Tan, DMSALAs, and MsIALA), the Wilcoxon rank-sum test confirms that IALA is significantly outperformed in only 2 out of 120 pairwise comparisons, exhibiting remarkable advantages in optimization accuracy, convergence speed, and robustness. Ablation experiments validate the synergistic necessity of all three strategies, with the Q-learning adaptive mechanism identified as the most critical contributor. Exploration-exploitation balance analysis and search history visualization further confirm that IALA achieves a smooth adaptive transition from global exploration to local exploitation. Space complexity analysis reveals that the Q-table introduces only approximately 19.5 KB of fixed additional overhead, which becomes negligible for high-dimensional problems. Furthermore, IALA is successfully applied to the parameter tuning of underwater manipulator controllers, verifying its efficiency and reliability in real-world engineering applications.

Vertical farming offers a controlled environment for food production in regions where land scarcity and environmental stress are prevalent. This study presents a bio-inspired optimization strategy for refining the spectral composition of red, green, and blue (RGB) light from LED to enhance crop performance. A genetic algorithm (GA) was employed to iteratively adjust spectral ratios in 2.5-day intervals over a single 25-day practical growth cycle. The algorithm employed selection, crossover, and mutation operators, targeting a weighted-sum fitness function based on key morphological traits, including fresh weight, height, width, and number of leaves. A experimental trial were conducted under controlled conditions, with identical light intensity and photoperiod for both RGB treatments and a cold white LED reference. The primary finding is that the optimization process successfully converged on a stable composition of approximately 67% red, 13% green, and 20% blue, which is consistent with prior studies on photosynthetic efficiency. This convergence validates the GA’s ability to autonomously discover a scientifically backed recipe from a neutral baseline. A final statistical analysis of the individual plant traits revealed a complex, multi-objective response, with plant height being the most statistically responsive parameter, while differences in the final biomass and leaf count were not statistically significant under the tested conditions. These findings demonstrate the potential of evolutionary algorithms for solving complex, multi-objective optimization problems in vertical farming, supporting the development of adaptive lighting strategies.

An improved differential evolution algorithm combined with vector NFP and mixed-integer programming for solving 2D irregular layout problem

Remanufacturing contributes to achieving economical, environmental, and social sustainability, and one of its main steps is disassembly aiming to acquire a set of recyclable and reusable components from end-of-life products. This research considers a multi-objective multi-product disassembly sequence planning problem under uncertain circumstances to realize a trade-off among economic, environmental, and social sustainability. Firstly, a multi-objective chance-constrained programming model is formulized to achieve maximal disassembly profit and minimal noise pollution while satisfying energy consumption requirements and obeying various complex product structures. Secondly, according to the characteristics of the concerned problem, a multi-objective group teaching optimization algorithm combining a stochastic simulation strategy is particularly devised to handle the problem. In the designed approach, the stochastic simulation scheme is utilized to assess the feasibility and performance of the obtained solutions under uncertain environments, and the multi-objective group teaching optimization algorithm is used to find candidate solutions. Specifically, problem-specific encoding and decoding methods are employed to represent and produce feasible solutions. Rank and crowding distance approaches are introduced to realize ability grouping, namely, dividing the population into two groups. Precedence preserving crossover and mutation operators are separately utilized on the two groups to achieve population evolution, and an adaptive local search method is developed to enhance exploitation. Thirdly, comparison experiments on some real-world test problems with different scales are carried out. Through dissecting the experimental results with three performance metrics, it can be observed that the devised approach outperforms its competitors by 9.39%–10.00%, 11.37%–59.86%, and 2.36%–7.73% regarding performance, respectively. The experimental results demonstrate the efficiency and excellence of the devised approach in providing high-quality disassembly schemes for managers and engineers.

Deep Learning (DL) frameworks are fundamental components of DL systems in their development, deployment, and execution, while defects in DL frameworks can cause severe consequences. Ensuring the quality of DL frameworks has therefore become a pressing challenge. Among the various testing techniques, model mutation has emerged as a widely adopted approach. Such methods generate mutants by applying mutation operators to DL models (e.g., structural changes or parameter edits) and then analyzing inconsistencies, crashes, or abnormal behaviors across different frameworks or hardware. Despite its effectiveness, existing methods suffer from the following limitations. First, they mainly reuse operators designed for model testing, raising doubts about their ability to expose framework-level defects. Besides, they insufficiently consider mutation constraints, such as mutation type, position, and order, which directly affect the defect detection ability of generated mutants. Finally, they rely on the limited detection range and narrow test oracles, focusing on functional correctness in model inference while overlooking defects in efficiency, resource usage, and other defects that developers care about in other stages, such as model training or deployment. These limitations result in a weak alignment with the critical defects that developers are most concerned about in practice. Motivated by these observations, this study conducts a comprehensive investigation into the effectiveness of existing mutation-based testing methods. We first collect and classify defect reports from PyTorch and MindSpore according to developers’ priority tags, building a taxonomy of seven categories and 19 sub-categories of HP defects. We then map the defects reported by five state-of-the-art methods into this taxonomy to evaluate their detection abilities. To explain these limitations, we further analyze how three key factors, mutation type, mutation position, and mutation order, affect the generated mutants. Based on the experiment results, we summarize ten findings ranging from revealing the priority of developers on fixing framework defects, evaluating the defect detection ability of existing methods, to how mutation factors affect the generated mutants. Furthermore, we reveal four limitations and their root causes of existing methods and propose four targeted optimization strategies. We further apply these strategies to COMET and successfully uncover six new defects spanning four types, including two previously unreported categories. Overall, our study identifies 38 unique framework defects, of which 30 are confirmed by developers and 12 have been fixed, demonstrating the practical value of our findings.

This paper investigates the multi-objective scheduling problem of intelligent unmanned operations and analyzes the interdependent constraints among the various links of the operation workflow. To address the coupled challenges of job sequencing and resource allocation inherent in unmanned operation scenarios, an integrated scheduling model based on a two-layer particle swarm optimization framework is proposed. A mixed-integer programming formulation is adopted to rigorously characterize the structural constraints and logical dependencies within the scheduling process. Building upon this model, an enhanced two-layer particle swarm optimization algorithm with fragment-based particle encoding is introduced to expand the feasible search space. Moreover, a dynamic inertia weight adjustment mechanism and an adaptive mutation operator are incorporated to strengthen the algorithm’s global exploration capability while accelerating convergence. Simulation experiments verify that the proposed model and algorithm effectively optimize the scheduling of unmanned operations under complex operational constraints, significantly improving system-level support performance. These results demonstrate that the method provides a robust and efficient solution for multi-objective intelligent scheduling tasks in highly constrained unmanned operation environments.