The balance between exploitation and exploration essentially determines the performance of a population-based optimization algorithm, which is also a big challenge in algorithm design. Particle swarm optimization (PSO) has strong ability in exploitation, but is relatively weak in exploration, while crow search algorithm (CSA) is characterized by simplicity and more randomness. This study proposes a new crow swarm optimization algorithm coupling PSO and CSA, which provides the individuals the possibility of exploring the unknown regions under the guidance of another random individual. The proposed CSO algorithm is tested on several benchmark functions, including both unimodal and multimodal problems with different variable dimensions. The performance of the proposed CSO is evaluated by the optimization efficiency, the global search ability, and the robustness to parameter settings, all of which are improved to a great extent compared with either PSO and CSA, as the proposed CSO combines the advantages of PSO in exploitation and that of CSA in exploration, especially for complex high-dimensional problems.
This paper examines the flocking control issue of the Cucker–Smale model in the presence of denial-of-service (DoS) attacks and communication delays. In the setting of DoS attacks, the attacker only obstructs the information communication between agents during the activation phases, while it concentrates on supplying its own energy during the dormancy phases. Furthermore, the communication delays are assumed to be time-varying and heterogeneous. Firstly, a general control input scheme that defends against DoS network attacks and communication delays is constructed. Secondly, on the basis of the presented control input and the properties of graph theory, the flocking control issue is equivalently transformed into a products convergence issue of infinite sub-stochastic matrices. Finally, an algebraic condition is obtained to formulate all the agents that asymptotically achieve the flocking behavior. Moreover, the obtained theoretical results are verified by a numerical example.
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