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As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm–grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.

In the modern educational environment, rational and efficient course scheduling is of great significance in ensuring teaching quality and improving resource utilization. The traditional sports scheduling methods are faced with the challenges of diversified demands and complex constraints. For this reason, the study proposes a new physical education course scheduling model after mathematically modeling the physical education scheduling model and improving the genetic environment based on chaotic genetic algorithm, and then proposes a new physical education course scheduling model. The experiment outcomes denote that the average computing time of the improved chaotic genetic algorithm is 28 s, and when the number of iterations is 175–200, the optimal number of individuals is 35 at most, and the value of the optimal fitness is 9.4. The simulation test outcomes denote that the new scheduling model can reasonably arrange the course in the 5th-6th section, which meets the needs of students. Meanwhile, when the amount of scheduling teachers is 4 and the amount of courses is 5, the average utilization rate of scheduling resources at this time is the highest 82.5%. Compared with the same type of scheduling model, the new model has the highest superiority of 90.7%, the highest stability of 90.1%, and the highest robustness of 91.6%. The proposed method can meet the diversified and dynamically changing teaching needs, and provides an effective optimization tool for physical education scheduling.

To efficiently realize backfilling mining with medium-deep hole caving in a gold mine, the rational determination of stope dimensions is essential. The Vlazov plate theory was employed to analyze the stress state and investigate the relationship between roof thickness and maximum tensile stress under varying stope spans. Since the latter serves as a criterion for evaluating roof strength failure, it is imperative to establish the appropriate range of parameters that ensure the rock mechanics stability during mining operations. Through central composite testing, numerical simulations were conducted to obtain mechanical response characteristics under different stope dimensions. Simultaneously, roof stress distributions and stope stability were analyzed. A second-order response surface model was constructed based on these findings, enabling the formulation of a comprehensive optimization framework. The interaction between variables within this framework was carefully considered when defining the objective functions for optimization. By integrating chaotic mapping into genetic algorithms, a multi-objective optimization approach was implemented, yielding 17 Pareto-optimal solutions. Ultimately, the optimized stope geometry was determined to have a chamber span of 31.89 m, a pillar span of 29.14 m, and a roof thickness of 5.35 m. This configuration represents the optimal balance between mechanical performance and mining efficiency in the context of medium-deep hole caving operations within the gold mine.

RPL (routing protocol for low-power and lossy networks) is an important candidate routing algorithm for low-power and lossy network (LLN) scenarios. To solve the problems of using a single routing metric or no clearly weighting distribution theory of additive composition routing metric in existing RPL algorithms, this paper creates a novel RPL algorithm according to a chaotic genetic algorithm (RPL-CGA). First of all, we propose a composition metric which simultaneously evaluates packet queue length in a buffer, end-to-end delay, residual energy ratio of node, number of hops, and expected transmission count (ETX). Meanwhile, we propose using a chaotic genetic algorithm to determine the weighting distribution of every routing metric in the composition metric to fully evaluate candidate parents (neighbors). Then, according to the evaluation results of candidate parents, we put forward a new holistic objective function and a new method for calculating the rank values of nodes which are used to select the optimized node as the preferred parent (the next hop). Finally, theoretical analysis and a series of experimental consequences indicate that RPL-CGA is significantly superior to the typical existing relevant routing algorithms in the aspect of average end-to-end delay, average success rate, etc.

The classification and recognition of radar clutter is helpful to improve the efficiency of radar signal processing and target detection. In order to realize the effective classification of uniform circular array (UCA) radar clutter data, a classification method of ground clutter data based on the chaotic genetic algorithm is proposed. In this paper, the characteristics of UCA radar ground clutter data are studied, and then the statistical characteristic factors of correlation, non-stationery and range-Doppler maps are extracted, which can be used to classify ground clutter data. Based on the clustering analysis, results of characteristic factors of radar clutter data under different wave-controlled modes in multiple scenarios, we can see: in radar clutter clustering of different scenes, the chaotic genetic algorithm can save 34.61% of clustering time and improve the classification accuracy by 42.82% compared with the standard genetic algorithm. In radar clutter clustering of different wave-controlled modes, the timeliness and accuracy of the chaotic genetic algorithm are improved by 42.69% and 20.79%, respectively, compared to standard genetic algorithm clustering. The clustering experiment results show that the chaotic genetic algorithm can effectively classify UCA radar’s ground clutter data.

Clustering in LEACH and its successors has been proved to be effective for not only improving energy efficiency but also extending network lifetime of wireless sensor networks. However, minimization of network energy consumption is still the most important topic in the research of hierarchical protocols based on LEACH. In this paper, an improved multi-hop LEACH protocol based on chaotic genetic algorithm (ICGA-LEACH) is proposed to obtain the optimal solution for energy efficiency and load balance at the same time. In ICGA-LEACH, cluster heads (CHs) are selected by a modified probability equation similar to LEACH, and then chaotic genetic algorithm is used to find the optimal routing paths and cluster members for the CHs according to a new constructed fitness function. Additionally, an adaptive round time is presented to further reduce energy consumption and prolong network lifetime. Simulation results in Matlab indicate that ICGA-LEACH is significantly superior to the existing relevant counterparts.

Efficient operation of electric vehicle (EV) charging stations is vital in the development of green transportation infrastructure. To address the challenge of balancing profitability, resource utilization, user behavior, and grid stability, this paper proposes a multi-objective dynamic pricing optimization framework based on a chaotic genetic algorithm (CGA). The model jointly maximizes operator profit and charging pile utilization while incorporating price-responsive user demand and grid load constraints. By integrating chaotic mapping into population initialization, the algorithm enhances diversity and global search capability, effectively avoiding premature convergence. Empirical results show that the proposed strategy significantly outperforms conventional methods: profits are 41% higher than with fixed pricing and 40% higher than with traditional time-of-use optimization, while charging pile utilization is 32.27% higher. These results demonstrate that the proposed CGA-based framework can efficiently balance multiple objectives, improve operational profitability, and enhance grid stability, offering a practical solution for next-generation charging station management.

A sea-rail automated container terminal (SRACT) plays a crucial role in the global logistics network, combining the benefits of sea and railway transportation. However, addressing the challenges of multi-equipment cooperative scheduling in terminal and railway operation areas is essential to ensure efficient container transportation. For the first time, this study addresses the cooperative scheduling challenges among railway gantry cranes, yard cranes, and automated guided vehicles (AGVs) under the loading and unloading mode in SRACTs, ensuring efficient container transportation. This requires the development of a practical scheduling model and algorithm. In this study, a mixed integer programming model was established for the first time to study the multi-equipment cooperative scheduling problem of a SRACT under the loading and unloading mode. A self-adaptive chaotic genetic algorithm was designed to solve the model, and the practicability and effectiveness of the model and algorithm were verified by simulation experiments. Furthermore, this study also proposes an AGV number adjustment strategy to accommodate changes in vessel arrival delays and train container types. Simulation experiments demonstrated that this strategy significantly reduces loading and unloading time, decreases equipment energy consumption, and improves the utilization rate of AGVs. This research provides valuable guidance for ongoing SRACT projects and advances and methodological approaches in multi-equipment co-operative scheduling for such terminals.

Reasonable scheduling of a train’s loading and unloading equipment can reduce the energy consumption of production operations; this has great value for the green development of terminals. The collaborative scheduling model of the Automated Rail Mounted Gantry (ARMG) and Automated Guided Vehicle (AGV) is used to minimize the energy consumption of equipment in a scenario of a vertical railway entering a port and a shared storage yard existing between the port and railway under the mixed operation mode of “train–ship” and “train–yard–ship”. According to the characteristics of the model, the two-layer scheduling rule and the self-adaptive chaos genetic algorithm (SCGA) were proposed to solve the problem of placing the ARMG and the AGV on the same schedule. Simulation experiments verified the effectiveness of the model and algorithm. The effects of the delayed arrival of vessels, the proportion of “transshipment” containers, and the number of automated ARMGs and AGVs on total energy consumption were analyzed. The results showed that when all containers are “train–ship” containers, the number of ARMG and AGV at 1:4 will minimize the total operational energy consumption. Furthermore, as ships take longer to arrive, reducing the number of AGVs can cut energy use by 15% for the same number of ARMG.

Fault diagnosis in complex software systems remains a critical challenge in software engineering, particularly under multi-fault and reliability-critical settings. To address the limitations of traditional spectrum-based fault localization techniques, such as poor search efficiency and susceptibility to local optima, this paper proposes a hybrid artificial intelligence framework that integrates Artificial Bee Colony (ABC) optimization with a Chaotic Genetic Algorithm (CGA). Experimental results on the Defects4J benchmark demonstrate a 12.1% improvement in Top-5 accuracy and a 38% reduction in EXAM score compared to particle swarm optimization-based fault localization methods. The proposed framework exploits the global exploration capability of ABC and the intensified local refinement enabled by chaotic operators in CGA to improve fault ranking while avoiding premature convergence. A dynamically weighted fitness function combines information from spectrum-based fault localization metrics, ABC, and CGA to compute final suspicion scores. Statistical significance analysis using the Wilcoxon signed-rank test (p = 0.007) confirms the effectiveness of the proposed approach, highlighting its potential to support automated debugging in large-scale and distributed software systems.