In high dimensional environment, the interaction among particles significantly affects their movements in searching the vast solution space and thus plays a vital role in assisting particle swarm optimization (PSO) to attain good performance. To this end, this paper designs a random contrastive interaction (RCI) strategy for PSO, resulting in RCI-PSO, to tackle large-scale optimization problems (LSOPs) effectively and efficiently. Unlike existing interaction mechanisms for low-dimensional problems, RCI randomly chooses several different peers from the current swarm to construct a random interaction topology for each particle. Then, it lets the particle interact with the selected peers based on their current evolutionary information instead of their historical evolutionary information. Within the topology, RCI only propagates the evolutionary information of two contrastive dominators with the largest difference in fitness to direct the evolution of the particle. Therefore, particles with no more than two dominators in their topologies are not updated. Furthermore, a dynamic topology size adjustment scheme is devised to gradually enlarge the interaction topology. In this way, the swarm gradually switches from exploring the immense search space dispersedly to exploiting the found optimal regions intensively as the evolution continues. With these two strategies, RCI-PSO expectedly compromises search diversity and search convergence well at the swarm level and the particle level. At last, extensive experiments executed on two public LSOP suites verify that RCI-PSO performs competitively with or even much better than totally 40 state-of-theart large-scale approaches and preserves a good capability and scalability in tackling complex LSOPs.

The closed‐loop wireless power transfer (WPT) system can realize constant voltage output in the presence of perturbation. However, the parameter design of the controller is a difficult problem. The traditional trial‐and‐error method is time‐consuming and difficult to find optimal parameters. A parameter optimization strategy of control systems for uncertain WPT systems using the modified genetic algorithm (MGA) is proposed. Firstly, because the system has different characteristics at different periods, the simulation process is divided into three stages. The first one is the start‐up stage, in which we mainly consider the overshoot and the rate of the voltage rise. The second one is the tracking stage, in which the tracking time and switching loss are mainly considered. The third one is the stabilisation stage, in which the steady‐state error and switching loss are mainly considered. Secondly, three cost functions are designed according to the characteristics of the three stages, and then the optimal controller parameters of each stage are obtained by using MGA. Finally, the effectiveness of the proposed method is verified by simulation. The optimization results show that compared with the previous parameter optimization method, the optimal controller parameters obtained by the proposed method make the WPT system achieve better performance.

Evidences have manifested that unidirectional ring neural networks lack the ability to generate desired chaos. This paper formulates a novel memristor regulation (MR) approach to constructing a no-equilibrium bi-memristor unidirectional ring neural network (BMURNN), in which two distinct memristors are incorporated into a unidirectional ring neural network derived from the Hopfield neural network, with enhanced chaotic complexity, whereas one serving as a memristive synapse and the other as an emitter of electromagnetic radiation. Numerical simulations reveal that any desired number of multi-scroll hidden chaotic attractors can be generated from the BMURNN via the non-ideal multi-piecewise nonlinear memristor, while the time-controlled multi-scroll attractor growth is output from the periodic function memristor, demonstrating that the memristors can enhance the chaos complexity of the original unidirectional ring neural network. Additionally, diverse coexisting hidden attractors, that is, hidden heterogeneous/homogeneous multistability evoked by the memory attributes of memristors, can be dynamically regulated by varying the initial conditions. Finally, a digital circuit is designed and implemented based on CH32 to validate the numerical simulations and theoretical analyses, and a new pseudorandom number generator is devised to explore the BMURNN for practical applications. Performance analyses demonstrate its superiority and high randomness, providing further proof for the effectiveness of the proposed MR method.

Memristors can be used to simulate the magnetic induction effect of action potentials in biological neurons. The diversity and complexity of firing patterns generated by existing memristive neuron models remain limited, which hinders their ability to fully characterize the rich electrical activities observed in biological nervous systems. To address this issue, this article introduces a novel memristive multiring Rulkov neuron model (MMRNM) by incorporating a universal discrete memristor into the classical Rulkov neuron model, enabling the investigation of more complex neuronal firing patterns and firing multistability. Dynamical analysis indicates that the MMRNM exhibits abundant firing behaviors, such as periodic firing, chaotic firing, hyperchaotic firing, and firing pattern transitions. Meanwhile, the MMRNM can generate multiring firing patterns with an arbitrary number of rings. Through initial offset boosting control, two types of firing multistability, namely homogeneous and heterogeneous firing multistability, are observed. To verify the performance, an STM32-based hardware platform is designed to capture the firing behaviors experimentally. Finally, a novel encryption scheme is designed by leveraging the chaotic and hyperchaotic sequences of the MMRNM, and performance evaluations demonstrate that the scheme possesses robust security.

In this paper, a mathematical modelling of a wireless power transfer system with a buck circuit is presented. Based on this modelling, an operator-based robust nonlinear control design scheme using sliding mode technology is proposed to consider the uncertain coupling coefficient, which results from an inaccurate distance between the transmit coil and receive coil in the wireless power transfer system. The proposed control design scheme has two advantages. First, in order to tackle the uncertain coupling coefficient, operator-based right coprime factorization is applied to guarantee robust stability of the nonlinear system. Sliding mode technology is also adopted to guarantee the tracking performance of the nonlinear system. Finally, simulations and experiments of the wireless power transfer system are tested using the proposed control design scheme. Results of both the simulations and experiments show the effectiveness of the proposed control design scheme.