This article investigates the leader-following synchronization problem of multiagent systems (MASs) under hybrid cyber attacks, which refers to deception attacks and multichannel independent denial-of-service (DoS) attacks in communication channels. In order to achieve the secure control of MASs under hybrid cyber attacks, a novel impulsive control method based on topology switching is proposed, and a new algorithm for determining impulsive instants is designed. In addition, the cooperative-competitive relationship between agents is also considered, which is more in line with reality. Sufficient conditions for ensuring secure control of MASs and a parametric upper bound on the error vector norm between the agents and the leader are obtained. Finally, the numerical simulation verified the effectiveness of the proposed algorithm.

This article is centered on the optimal consensus problem of the multiagent systems (MASs) with time delays. By designing a new augmented state, the delayed MASs are reformulated as a delay-free system, and each agent is to minimize its local cost that may depend on the decisions of the other agents, which is regarded as a Nash equilibrium problem. To this end, we propose a multiagent deterministic policy gradient (MADPG) method based on actor–critic (AC) networks to minimize the local cost ($Q$-function) by introducing the policy gradient technique, and its convergence and optimality are proven as well. In particular, we develop an optimized prioritized experience replay (PER) strategy that allows high-value samples to be selected with a higher probability, which enhance networks’ data utilization. Finally, the effectiveness of the algorithm and the advantages of PER are demonstrated with a simulated example and a comparative simulation.

In this article, the cluster consensus of heterogeneous multiagent systems is investigated via dynamic event-triggered and pining control strategies. Comprehensively considering the cooperative and competitive interactions among the agents, a novel cluster consensus control protocol is designed, and a class of fully distributed dynamic event-triggered laws that do not depend on the global information is proposed. Being distinct from the traditional static threshold value, a time-varying threshold value is introduced which can effectively reduce the trigger time. Meanwhile, some sufficient conditions are obtained theoretically by using Lyapunov stability theory and the pinning control strategy is addressed as well. Furthermore, it has also been shown that each agent can exclude Zeno behavior. Finally, two simulated examples are given to illustrate the correctness and validity of the proposed methods and our findings.

The capacitated arc routing problem (CARP) has attracted much attention for its many practical applications. The large-scale multidepot CARP (LSMDCARP) is an important CARP variant, which is very challenging due to its vast search space. To solve LSMDCARP, we propose an iterative improvement heuristic, called route clustering and search heuristic (RoCaSH). In each iteration, it first (re)decomposes the original LSMDCARP into a set of smaller single-depot CARP subproblems using route cutting off and clustering techniques. Then, it solves each subproblem using the effective Ulusoy's split operator and local search. On one hand, the route clustering helps the search for each subproblem by focusing more on the promising areas. On the other hand, the subproblem solving provides better routes for the subsequent route cutting off and clustering, leading to better problem decomposition. The proposed RoCaSH was compared with the state-of-the-art MDCARP algorithms on a range of MDCARP instances, including different problem sizes. The experimental results showed that RoCaSH significantly outperformed the state-of-the-art algorithms, especially for the large-scale instances. It managed to achieve much better solutions within a much shorter computational time.