This article presents a multistrategy hybrid dung beetle algorithm (MHDBO) algorithm to tackle the complexities of Autonomous tractor path planning, a problem characterized by intricate optimization objectives and constraints. The proposed MHDBO effectively reduces operational costs, minimizes path lengths, and enhances coverage efficiency in agricultural operations. To improve optimization performance, a novel quantum-assisted offset estimation strategy is introduced, which significantly expands the solution space, improves population diversity, and effectively guides the evolutionary direction of the algorithm to avoid local optima. Additionally, an enhanced double-helix search strategy is designed to strengthen global search capabilities, improving both accuracy and convergence. A reverse learning strategy is integrated, in which a quasi-oppositional learning mechanism is used to avoid large positional deviations typical of traditional reverse learning, thereby maintaining solution stability while enhancing exploration. This helps prevent premature convergence and improves robustness in dynamic environments. To further optimize multitractor coordination, a multistack multitractor scheduling and field path planning model (MSPPM) is developed, addressing the challenges of large-scale agricultural operations. Experimental evaluations show that the proposed MHDBO algorithm achieves a total dispatch path length of 29 093.4007 km, an overall operational cost of 2.622543 million RMB, an execution time of only 4.128759 s, and a memory consumption of$3.588~{\times }~10{^{{9}}}$bytes in the MATLAB environment. Furthermore, MHDBO demonstrates strong performance in coverage, achieving over 95% effective farmland utilization. Compared with state-of-the-art algorithms, including adaptive elite differential evolution algorithm (AEDE), improved 2-opt ant colony optimization (IACO), and adaptive elite chaotic genetic algorithm (AECGA), MHDBO achieves a 10.28% reduction in operational costs, a 5.859% decrease in path length, and executes 15.46% faster, while consuming 16.06% less memory. These results confirm the algorithm’s superiority, efficiency, and scalability in real-time agricultural automation applications.

Clustering routing is one of the most prevailing approaches for saving energy in Wireless Sensor Networks (WSNs). However, many existing clustering protocols have the problem of node premature death. This is primarily due to the frequent selection of some advantageous nodes as cluster heads (CHs), which bear the responsibility of data aggregation and forwarding, resulting in higher energy consumption. To address this problem, this paper proposes a dual-CH clustering routing model and introduces an interval-based CH reelection mechanism to reduce the energy consumption associated with frequent cluster formation. In addition, a comprehensive consideration of residual energy and base station distance in CH election facilitates the design of an improved hybrid swarm intelligence optimization algorithm termed GWOA-CH, which combines Gray Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA) to obtain a more effective CH election scheme. To verify the effectiveness of the proposed algorithm, extensive experiments are conducted between GWOA-CH and some state-of-the-art WSNs routing protocols, including ModifyGA, NCOGA, ARSH-FATICHS and MMROR. Experimental results demonstrate the superior energy saving capabilities of the proposed clustering protocol compared to its alternatives.

Underwater acoustic sensor network (UASN) is a highly practical and popular sensor network extensively utilized for marine environmental monitoring and underwater exploration. Due to the high frequency of node usage, limited energy resources and high QoS performance requirements in UASN, it is necessary to adopt a comprehensive and integrated routing algorithm. In this paper, an innovative comprehensive clustering routing model is designed to accurately reflect the practical usage of UASN. Furthermore, a novel clustering routing algorithm, namely multi-objective differential chaotic shuffled frog leaping algorithm (MDCSFLA), is proposed. This algorithm effectively extends the network lifetime, greatly optimizes network energy balance, and improves network QoS performance. Additionally, by designing a novel differential local search strategy and chaotic perturbation strategy, the global optimization capability is significantly enhanced, and the convergence speed of the algorithm is effectively improved. Through a series of experiments, it is demonstrated that compared to LEACH-C, Q-LEACH, OptGACHE, and UCPSO, the proposed approach demonstrates a significant improvement in network lifetime and throughput, with an increase of at least 22.70% and 27.51%, respectively. Additionally, it achieves substantial reductions of at least 5.92% in average data transmission latency and 16.04% in packet loss rate.

The wireless sensor network (WSN) profoundly impacts the routing technology of the Internet of Things, which has received tremendous attention in terms of energy cost, quality of service (QoS) and security. In this way, it is particularly significant to find a multi-hop path with low energy consumption, delay, delay jitter, packet loss rate and high bandwidth, credibility in WSN. However, the existing energy-efficient secure QoS routing problem has been proven to be an NP-hard that forces a trade-off between energy cost, communication quality and security. To address this problem, a new energy-efficient secure QoS routing model is designed, which precisely replicates the communication scenario of WSN and comprehensively considers energy cost, latency, delay jitter, bandwidth, credibility, and packet loss rate. Subsequently, a novel energy-efficient secure QoS routing algorithm based on elite niche clonal evolutionary computing (ESQRA-ENCEC) is proposed, which includes three novel operators named niche selection, clone operator and elite optimization. These operators are designed to significantly increase the quality of solutions, vigorously develop convergence speed and successfully avoid local optima. The suggested algorithm not only considerably lowers energy consumption, delay, delay jitter and packet loss rate, but also effectively increases bandwidth and credibility. The simulation of ESQRA-ENCEC is performed in different scenarios. Experiment results reveal that the ESQRA-ENCEC has improved by 5.21%, 11.26% in energy cost, 5.31%, 7.94% in bandwidth, 6.44%, 10.80% in delay, 5.85%, 12.79% in delay jitter, 8.58% 14.39% in packet loss rate and 8.03%, 15.58% in credibility compared with two existing algorithms, respectively.

With the widespread application of the Industrial Internet of Things (IIoT), the requirements for sensing equipment to collect data and information continue to increase, and industrial wireless sensor networks (IWSNs) with industrial information perception capabilities have emerged as the times require. The data stream transmission of important value information requires the network to provide safe and reliable service quality assurance. Therefore, it is imperative to meet the requirements of end-to-end delay and reliable service between nodes and solve the problems of high energy consumption and poor security of the existing Quality-of-Service (QoS) routing protocols. To this end, considering the QoS constraints of end-to-end delay, security, and energy consumption, a multiobjective secure routing model based on trust awareness is designed. Subsequently, using the advantages of the proposed model, a novel QoS-secure routing algorithm based on multiobjective chaotic elite adaptive ant colony optimization (ACO) (i.e., MCEAACO-QSRP) is proposed. Specifically, a chaotic optimization strategy is designed to initialize the population, which increases the diversity of the population and enhances the ability of the algorithm to jump out of the local optimal. In addition, the adaptive optimization strategy is designed to dynamically adjust the algorithm trend, which effectively improves the algorithm convergence speed. The performance of the proposed algorithm is evaluated in different scale scenarios. The simulation results show that compared with other state-of-the-art QoS routing solutions, the proposed MCEAACO-QSRP has obvious advantages, which can effectively improve routing security, reduce network energy consumption, and satisfy multi-QoS constrains for the end-to-end delay and reliable service.

Compared with conventional wireless sensor networks (WSNs), industrial WSNs (IWSNs) have stricter requirements in real-time data transmission, energy consumption, and energy uniformity. To fulfill these requirements, a new energy-efficient clustering approach using quantum-related bioinspired optimization, i.e., quantum elite gray wolf optimization (QEGWO), is proposed to improve the performance of IWSNs. Innovatively, a new quantum operator, including quantum probability amplitude, quantum rotation gate, and quantum NOT gate, is designed in QEGWO to enhance its global search capability. This quantum operator need not query the quantum rotation angle table in updating quantum probability amplitude with the quantum rotation gate, which reduces the computational complexity of introducing quantum optimization into the clustering problem of IWSNs. Moreover, to enhance the convergence performance of the clustering algorithm, a multielite strategy is proposed to preserve the historical optimal individuals generated in the iterative process by establishing a dynamic elite pool. Compared with the state-of-the-art clustering approaches, extensive simulations in four different scenarios are carried out, and the results demonstrate that the proposed QEGWO outperforms other comparison approaches in delay, energy consumption, and energy uniformity.

With the rapid development of Internet technology, network attacks have become more frequent and complex, and intrusion detection has also played an increasingly important role in network security. Intrusion detection is real‐time and proactive, and it is an indispensable technology under the diversified trend of network security issues. In terms of network security, neural networks have the characteristics of self‐learning, self‐adaptation, and parallel computing, which are very important in intrusion detection. This paper combines back propagation neural network (BPNN) and elite clone artificial bee colony (ECABC) to propose a new ECABC‐BPNN, which updates and optimizes the settings of traditional BPNN weights and thresholds. Then, apply ECABC‐BPNN to network intrusion detection. Use the attack data samples of KDD CUP 99 and water pipe for attack classification experiments using GA‐BPNN, PSO‐BPNN, and ECABC‐BPNN. The results show that the ECABC‐BPNN proposed in this paper has an accuracy rate of 98.08% on KDD 99 and 99.76% on water pipe data. ECABC‐BPNN effectively improves the accuracy of network intrusion classification and reduces classification errors. In addition, the time complexity of using ECABC‐BPNN to classify network attacks is relatively low. Therefore, ECABC‐BPNN has superior performance in network intrusion detection and classification.