With the development of artificial intelligence, numerous researchers are attracted to study new heuristic algorithms and improve traditional algorithms. Artificial bee colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the foraging behavior of honeybees, which is one of the most widely applied methods to solve optimization problems. However, the traditional ABC has some shortcomings such as under-exploitation and slow convergence, etc. In this study, a novel variant of ABC named chaotic and neighborhood search-based ABC algorithm (CNSABC) is proposed. The CNSABC contains three improved mechanisms, including Bernoulli chaotic mapping with mutual exclusion mechanism, neighborhood search mechanism with compression factor, and sustained bees. In detail, Bernoulli chaotic mapping with mutual exclusion mechanism is introduced to enhance the diversity and the exploration ability. To enhance the convergence efficiency and exploitation capability of the algorithm, the neighborhood search mechanism with compression factor and sustained bees are presented. Subsequently, a series of experiments are conducted to verify the effectiveness of the three presented mechanisms and the superiority of the proposed CNSABC, the results demonstrate that the proposed CNSABC has better convergence efficiency and search ability. Finally, the CNSABC is applied to solve two engineering optimization problems, experimental results show that CNSABC can produce satisfactory solutions.

Offshore drilling operations face the critical challenge of maintaining precise weight-on-bit (WOB) control during automatic drilling feed while subjected to vessel heave disturbances. This study investigates an integrated closed-circuit hydraulic cylinder lifting system that combines full-stroke drill string compensation with potential energy recovery capabilities, addressing the control coupling problem inherent in traditional split-design systems. A longitudinal vibration model of the drill string is established using lumped mass, stiffness, and damping principles incorporating the Rayleigh method. A co-simulation model implementing nested PID control logic is developed on the AMESim platform to evaluate automatic drilling feed performance under both passive and semi-active compensation modes. Simulation results demonstrate that the proposed integrated control strategy effectively mitigates bottom-hole WOB fluctuations, with top drive velocity accurately tracking set drilling feed rates (0.01–0.02 m/s) within a response time of approximately 10 s. The system maintains operational stability under sea conditions up to Grade 6 (heave wave height ≤ 4.578 m, period 14 s), beyond which accumulator piston limit-stroke collision risks emerge. These findings validate the feasibility of integrated hoisting-compensation design and establish quantitative operational limits, providing theoretical foundations for next-generation marine drilling systems targeting ultra-deepwater and natural gas hydrate exploitation.

With the development of artificial intelligence, numerous researchers are attracted to study new heuristic algorithms and improve traditional algorithms. Artificial bee colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the foraging behavior of honeybees, which is one of the most widely applied methods to solve optimization problems. However, the traditional ABC has some shortcomings such as under-exploitation and slow convergence. In this study, a novel variant of ABC named chaotic and neighborhood search-based ABC algorithm (CNSABC) is proposed. The CNSABC contains main three improved mechanisms including Bernoulli chaotic mapping with mutual exclusion mechanism, new neighborhood search mechanism and sustained bees. In detail, Bernoulli chaotic mapping with mutual exclusion mechanism is introduced to enhance the diversity and traversal of initial nectar sources and scout bees to find nectar sources, further to improve the exploration ability of peripatetic bees. A new neighborhood search mechanism and sustained bees are proposed to enhance the convergence efficiency and local exploitation capability of the algorithm. Subsequently, a series of experiments are conducted to verify the effectiveness of the three presented mechanisms and the superiority of the proposed CNSABC algorithm. Compared with 8 existing approaches for testing 25 typical benchmark functions, including eight variants of ABC (ABC, CABC, NABC, qABC, SBABC, MPGABC, GABC and NGABC), and five other original basic algorithms (PSO, ABC, GWO, WOA and BOA), the results demonstrate that the proposed CNSABC has better convergence and search ability. Finally, the CNSABC is applied to solve two engineering optimization problems, experimental results show that CNSABC can produce satisfactory solutions.

Reliability technology has significant importance to ensure the safe operation of the system. To address the problems of complex system structure and lack of reliability data, a combination of Failure Mode and Effects Analysis (FMEA) and Fuzzy Fault Tree Approach (FFTA) is introduced. Firstly, the FMEA method is used to complete the qualitative analysis of the system reliability from the perspective of multi-factor failure modes, analyze the potential failure modes and failure causes of the system. And the risk matrix diagram is used to determine the degree of harm of different failure modes to the system function. Then, the system reliability is quantitatively analyzed by FFTA, and a fault tree model is established by dividing the system into "system-subsystem-component" and solving for the minimum cut set. In addition, by introducing fuzzy set theory to blur the failure probability of each bottom event and introducing triangular fuzzy function to quantitatively calculate the failure probability of the top event and the probabilistic importance of the bottom event, the weak points of each subsystem are identified. Finally, a qualitative and quantitative reliability analysis is conducted by using FMEA-FFTA method for subsea control system. Measures should be taken to focus on preventive protection and regular testing for the high risk, medium-high risk and medium risk modes of the subsea control system.

The drill pipe handling system is characterized by large working load, wide range of load variation and high control accuracy requirements, which put certain requirements on the stability and reliability of the hydraulic system. In this paper, the research on the optimized control of electro-hydraulic servo system of drill pipe handling system is mainly conducted. Firstly, the mathematical model of the electro-hydraulic servo system is established and the transfer function of the electro-hydraulic servo system is solved. Secondly, the Cauchy mutation mechanism is introduced to improve the traditional particle swarm optimization (PSO) algorithm for the defects of local optimum, and the algorithm is verified by a series of experiments. The results show that the improved PSO has faster convergence speed and higher convergence accuracy, and the solution performance is greatly improved. Finally, Simulink is used to build a simulation model and the improved PSO algorithm is applied to the optimization of PID parameters. It is found that the improved PSO algorithm obtains better control effect through simulation.