This article proposes a framework for multidifferent-target search in unknown environments based on swarm intelligence. In this framework, the idea of distributed model predictive control is introduced in the target search method. The use of a hierarchical prediction strategy further improves the robot’s path prediction ability in unknown environments. Compared with swarm intelligence methods—adaptive robotic particle swarm optimization (A-RPSO), improved group explosion strategy (IGES), and other existing works, this strategy significantly improves the multidifferent-target search functionality and the task success rate in unknown complex obstacle environments. Moreover, two effective efforts are then introduced to reduce computational complexity and speed up online decision making. One is to select cooperative individuals based on the line of sight, and the other is to reduce both the frequency of decision making and the amount of data transmitted. A comparison between obstacle-free map experiments and obstacle map experiments confirms the effectiveness of the ideas and methods presented in this article.

Automata networks are a class of fully discrete dynamical systems, which have received considerable interest in various different areas. This brief addresses the observability of automata networks and switched automata networks in a unified framework, and proposes simple necessary and sufficient conditions for observability. The results are achieved by employing methods from symbolic computation, and are suited for implementation using computer algebra systems. Several examples are presented to demonstrate the application of the results.

Probabilistic Boolean networks (PBNs) have been proved to be a useful tool for modeling genetic regulatory interactions. The study of the steady-state probability distribution may help to understand the essential long-run behavior of a PBN. In this paper we focus on a type of PBNs derived from gene expression data collected in a study of metastatic melanoma. The metastatic melanoma model is usually described by a PBN containing seven genes among which WNT5A plays a significant role in the development of melanoma and is known to induce the metastasis of melanoma when highly active. This paper investigates the issue of how to drive the corresponding PBN towards desired steady-state probability distributions so as to reduce the WNT5A's ability to induce a metastatic phenotype.

We provide a general approach for the design of a response Boolean network (BN) to achieve complete synchronization with a given drive BN. The approach is based on the algebraic representation of BNs in terms of the semi-tensor product of matrices. Instead of designing the logical dynamic equations of a response BN directly, we first construct its algebraic representation and then convert the algebraic representation back to the logical form. The results are applied to a three-neuron network in order to illustrate the effectiveness of the proposed approach.

We examine complete synchronization of two deterministic Boolean networks (BNs) coupled unidirectionally in the drive-response configuration. A necessary and sufficient criterion is presented in terms of algebraic representations of BNs. As a consequence, we show that complete synchronization can occur only between two conditionally identical BNs when the transition matrix of the drive network is nonsingular. Two examples are worked out to illustrate the obtained results.