A new meta-heuristic algorithm, Cuckoo Catfish Optimizer (CCO), is proposed for numerical optimization problems. It simulates the search, predation, and parasitic behavior observed in cichlids. Early iterations of the algorithm focus on executing a multidimensional enveloping search strategy and a compressed space strategy, combined with an auxiliary search strategy to efectively limit the escape space of cichlids. This phase ensures extensive exploration of the solution space. In the intermediate stage of iteration, the algorithm uses a transition strategy to promote a smooth transition from exploration to exploitation, endowing the algorithm with both a certain degree of exploration capability and exploitation capability. In later stages, the algorithm uses chaotic predation mechanisms to create disturbances around cichlids to improve the exploitation of optimal solutions. Throughout the entire optimization process, the guidance, parasitism, and death mechanisms of individuals are integrated, allowing individuals to adjust their positions in real-time and improve the overall convergence accuracy. This paper rigorously evaluates the performance of CCO through 23 classic test functions and three CEC test suites. The experimental results show that compared with 11 famous algorithms and 10 novel improved algorithms, CCO can obtain the optimal solution in 91.52% of the test functions, demonstrating its excellent ability in solving various numerical optimization problems. Additionally, through the successful application to 6 mechanical optimization problems, 3 photovoltaic cell parameter optimization problems, and 1 path opti- mization problem, the competitiveness of CCO in solving real-world problems is verified and highlighted. The CCO source code can be downloaded here: https://ww2.mathworks.cn/matlabcentral/fileexchange/176828-cuckoo-catfish-optimizer-a-new-meta-heuristic-optimization

In this study, an improved African vulture optimization algorithm (IAVOA) that combines the African vulture optimization algorithm (AVOA) with both quasi-oppositional learning and differential evolution is proposed to address specific drawbacks of the AVOA, including low population diversity, bad development capability, and unbalanced exploration and development capabilities. The improved algorithm has three parts. First, quasi-oppositional learning is introduced in the population initialization and exploration stages to improve population diversity. Second, a differential evolution operator is introduced in the local search position update of each population to improve exploration capability. Third, adaptive parameters are introduced to the differential evolution operator, thus balancing the algorithm exploration and development. A numerical simulation experiment based on 36 different types of benchmark functions showed that while the IAVOA can enhance the convergence speed and solution accuracy of the basic AVOA and two variants of AVOA, IAVOA outperforms the other 7 swarm intelligence algorithms in the mean and best values of 33 benchmark functions.

This paper aims to explain the design of a novel time-varying sliding mode control of variable parameter (VP-TVSMC), which can effectively solve the anti-swing and positioning problem of distributed-mass double pendulum bridge crane system with its quick responsiveness and strong robustness to external interference. More specifically, this model initiates with the establishment of the dynamic equation of double pendulum crane model based on distributed-mass, then followed by the design of a time-varying parameter to realize the dynamic adjustment of the sliding mode surface and enhance the adjustment ability of the sliding mode surface, which is conducive to the global robustness of the double pendulum crane system under VP-TVSMC. With Lyapunov method and LaSalle’s invariance principle, the asymptotic stability of the system can be sufficiently proved. Finally, the adoption of three kinds of external interference signals and uncertain system parameters successfully verified the preeminent control performance and global robustness against external interference of the proposed controller. The simulation results indicate that compared with the conventional CSMC and PDSMC, the proposed control method can reduce the driving force of the trolley, ensure the rapid and precise positioning of the trolley, as well as restrain the load swing angle within 5° in an effective manner. In addition, compared with the symbolic function sgn( $S$ ), the designed continuous function th( $S$ ) possesses a better anti-chattering effect, thus strengthening of the control performance of VP-TVSMC.

A wide application of sliding mode variable structure control as a nonlinear robust control method, has been witnessed in anti-swing positioning control of bridge crane system. Aiming at the problem that the sliding mode variable structure control system of bridge crane is not robust in approaching process, a new Global-Equivalent Sliding Mode Controller (GESMC) based on bridge crane system is proposed. This controller can realize the anti-sway positioning control of the bridge crane system under the condition of uncertain model parameters and external disturbance. The proposed controller, different from the traditional sliding mode control, excels in improving system robustness through keeping the system states in the sliding surface during the whole response process. Specifically, it initiates with the design of a global sliding surface, which can eliminate the sliding mode approach process of the system and achieve global robustness in the system. Afterwards, a new switching function combined with the equivalent sliding mode control method is incorporated to effectively reduce the chattering generated when the system reaches the sliding mode manifold. Its asymptotic stability is proven without a priori knowledge on the bounds of unknown disturbances by using the Lyapunov stability theory. Lastly, the simulation conducted verifies the effectiveness and robustness of the GESMC proposed in this paper and meanwhile demonstrates a comparatively favorable performance for the GESMC in reducing chattering.

To address the issue of insufficient position control accuracy in the servo-hydraulic system of injection molding machines under nonlinear characteristics and external disturbances, this paper proposes a novel adaptive PID control strategy enhanced by the Deep Deterministic Policy Gradient (DDPG) algorithm. An auxiliary servo valve is introduced to improve flow capacity and enhance the system’s dynamic response performance. Meanwhile, the DDPG algorithm is utilized to adjust the PID parameters in real time based on tracking errors and system state feedback, thereby improving the controller’s adaptability to time-varying operating conditions. Compared with traditional control methods, the proposed strategy demonstrates superior tracking accuracy, faster convergence, and stronger robustness. In particular, this work innovatively integrates the DDPG algorithm with an auxiliary servo valve structure for PID parameter optimization and dynamic performance enhancement, offering new ideas and technical pathways for adaptive control of complex hydraulic systems.