Focusing on higher organisms, this paper analyzes and develops a comprehensive review of the problems in bionic computing and also proposes and expounds some new views and insights, from the perspective of collective intelligence as a whole, which includes swarm intelligence and crowd intelligence. On the basis of an overview on the research progress of bionic computation in higher organisms (including fundamental higher organisms, regular higher organisms and quasi-man organisms), the reflux phenomenon in the research on the trend of making algorithms marked by “zoo algorithm” in swarm intelligence optimization is found. A reasonable interpretation of the reasons for the formation of the trend of making algorithms from both the bionic-computational dimension and the problem-method dimension. Furthermore, the overall idea of problem solving is given, and the two main development directions of bionic computing for collective intelligence are refined and formed. Emphasis on the expansion of bionic behavior towards cooperative behavior is dominant in the direction of collective intelligence bionic computing development. Aiming at the difficulties existing in the research of swarm intelligence optimization, five bottlenecks that need to be focused on to achieve breakthroughs are proposed. Based on the overall view of “metaphorical bionic computing-normative bionic computing-complex bionic computing”, the new paradigm of intelligent computing of complex bionic computing is advocated, which can guide the direction for higher organism bionic computing.

The traditional cluster-growing method has high time complexity, inaccurate description of fractal scale relationship, and key nodes are important in controlling network structure and function. In order to select representative nodes to analyze the network self-similarity fractal problem, we propose a K-shell-based cluster-growting method of complex networks, in which the most influential nodes in the core layer are selected as the seed nodes of the cluster growth method to calculate the fractal dimension of the network through K-shell decomposition and node information entropy. Experimental results show that the proposed method can perfectly observe the fractal properties of the network and calculate the fractal dimension more accurately.

In order to solve the problem of low resolution and lack of concrete and comprehensive recognition results of important nodes in hypernetworks, in this paper, combined with the degree of node, degree of transcendence, degree of adjacency and degree of adjacency, the compound information entropy is proposed to identify the important nodes of the hypernetwork. In this method, the influence coefficient is set, and the composite structure entropy of each node is obtained by analyzing the influence degree of degree of node, degree of adjacency, degree of node transcendence and degree of adjacency. Its advantage is that the influence of nodes and adjacent nodes is considered, and only the local attributes of nodes are used, resulting in lower complexity. The simulation experiments are carried out in the research cooperation hypernetwork and Kunming common bus line hypernetwork. Experimental results show that the proposed method can effectively identify the important nodes in the hypernetwork.

Agricultural product trade is an important channel to achieve the global "zero" hunger sustainable development goal. In the accelerated evolution stage of world changes, there is an urgent need to conduct research on the layout of trade networks and the influence of node countries. This article studies the evolution of agricultural product trade based on complex network theory and measures a country's influence through network efficiency. The results show that: 1) The agricultural product trade network is becoming increasingly efficient and close. 2) The influence of a node country is closely related to its comprehensive strength; countries with great influence have strong import and export intensity, and there is a positive correlation between a country's export intensity and its import intensity. 3) Agricultural product trade is mainly dominated by a few large countries, which will exacerbate the vulnerability of global agricultural product supply under unstable factors such as natural disasters or geopolitics.

The loss of key developers in the open-source community will cause changes in the psychology and behavior of remaining developers, leading to a series of cascading effects. Considering the impact of the loss of key developers on the work intensity and collaboration willingness of remaining developers under the following effect, a dynamic cascade failure model with load capacity changes is designed. Taking the open-source design platform AngularJS as an example, the dynamic robustness of the open-source community developer collaboration network is investigated. The results show that the threshold of anti-interference ability of open-source community developer collaboration network has an optimal interval. The robustness of the developer collaboration network is greatly affected by the initial load distribution and is the weakest in the mode of the highest load node loss. The results are beneficial for the management and operation of open-source community.

To ensure the secure and stable operation of Cyber-Physical Power System (CPPS), research on their topology and robustness is essential. Based on the theory of complex networks, a comprehensive CPPS robustness evaluation framework is proposed, which establishes topological structures and functional cascading failure models, along with corresponding performance quantification metrics. By combining the Louvain algorithm and gravity centrality, a method for identifying the importance of nodes considering the underlying network topology is proposed. Pre-disaster protection measures are implemented based on node importance, and the robustness of the system is compared under different protection strategies. Finally, the CPPS model constructed using the IEEE118 network is used to simulate the system's state response under different conditions, verifying the effectiveness of the proposed method.

In order to study the reliability of air-rail network under the disturbance of flight delays. The air-rail weighted hypernetwork model is constructed in this paper, and the disturbance mechanism of flight delays to the air-rail hypernetwork is analyzed. The reliability of China Eastern Airlines’ air-rail hypernetwork under occasional delay scenario and multiple-delay scenario is simulated and analyzed respectively. The results show that: In the case of occasional delay scenario, the reliability of the air-rail hypernetwork is strong, and the failure of a single airport node has only a limited impact on the efficiency of air-rail hypernetwork, and the impact on the network connectivity is minimal. In the case of multiple-delay scenario, the reliability of the China Eastern Airlines air-rail hynetwork is strong under the random disturbance attack mode, but weak under the selective disturbance attack mode. No matter it is under occasional delay scenario or multiple-delay scenario, no matter under the random disturbance attack mode or the selective disturbance attack mode, the reliability of China Eastern Airlines air-rail hypernetwork is superior to its airport network. Measures such as increasing the number of cities, strengthening the protection of hub nodes and strengthening information sharing are conducive to ensuring the reliable operation of air and rail intermodal transport network.

Considering the influence of computer network structure and the effect of computer virus propagation on user awareness, two WSI (Weakly-protected Strongly-protected Infected) computer virus propagation models with nonlinear infection rate in two types of complex networks (including homogeneous and heterogeneous networks) are developed. The equilibrium points and basic regeneration numbers of the two types of propagation models are given respectively, together with stability proofs. Finally, numerical simulation experiments are conducted to verify the stability of the two types of models and to analyze the effect of nonlinear infection rates on the spread of computer viruses, as well as to compare the spread of computer viruses from the two types of models with the basic regeneration numbers and we can find that the heterogeneity of networks can exacerbate the spread of computer viruses.

In order to rationally allocate labor resources and realize the coordinated development of regions, we extracted and analyzed the spatial-temporal evolution characteristics of China's Inter-provincial Labor Flow (ILF) network in terms of system, node and path by using the complex network theory. The results indicate that the ILF network has obvious agglomeration, hierarchy and path dependence, and region-centered unbalanced mobility is gradually highlighted. The regional siphoning and spillover effects on labor have significant heterogeneity: the east coast remains the core direction of inflow, and labor returns to some areas in the central and western parts of the country, however, on a smaller scale. The center of gravity of labor supply gradually shifts from the central part to the western part of the country. Critical path analysis shows that ILF has some path-dependence effects and that there is a geographic proximity preference in labor flow.

In order to explore new chaos phenomenon and its producing mechanism, it is convenient for us to understand and apply chaos. A new kind of chaotic system is constructed by using chaos theory and we find that this system has rich nonlinear dynamic properties. Using nonlinear system theory and numerical simulation techniques, we study the attractors of this system and their dimensions, Lyapunov exponent, initial sensitivity, dissipation, bifurcation plot and global attraction domain and this paper enriches and develops chaos theory. This chaotic system has potential applications and can be used to protect data security and image encryption.

Taking a quasiperiodic driven nonsmooth system with Farey tree as the research object, the existence of the strange nonchaotic attractor is confirmed and its statistical properties are further analyzed. Firstly, the fractal properties of strange nonchaotic attractor are analyzed by using phase diagram and power spectrum qualitative methods. The properties of strange nonchaotic attractor are described by means of maximum Lyapunov exponent, phase sensitive function, recursive analysis, spectral distribution function and finite-time Lyapunov exponent distribution. The results show that there are strange nonchaotic attractors in the system, and strange nonchaotic attractors exhibit a variety of statistical properties different from other types of attractors.

In response to the problems of unknown objective weights of attributes and missing hesitancy in the existing entropy formulas of interval-valued Pythagorean fuzzy sets, the fuzziness is defined according to the characteristics of the set. A new axiomatic definition of the entropy for interval-valued Pythagorean fuzzy set is given based on the fuzziness and hesitancy. An improved entropy formula is proposed to calculate objective weights of attributes, and the bidirectional projection method is extended to interval-valued Pythagorean fuzzy environments. A multi-attribute decision-making method based on the improved entropy and bidirectional projection is constructed. Through theoretical proofs and numerical analysis, the related properties, the rationality and effectiveness of the proposed method are verified, providing new ideas for solving multi-attribute decision-making problems with unknown objective weights.

This paper aims to investigate the controllability of multi-agent systems with cells of equal capacity. Firstly, based on the equipotential nodes within cells of equal capacity, a distinction is made between equipotential nodes and automorphic nodes, revealing that equipotential nodes extend automorphic nodes. Secondly, by selecting equipotential nodes and non-equipotential nodes as leaders, the controllability of the system is analyzed, and a novel leader selection method based on the number of cells of equal capacity is proposed. Finally, by analyzing the Laplacian matrix, the relationship between its rank and the topological structure is revealed. The research results demonstrate that the proposed leader selection method can effectively enhance the controllability of the system.

To study the green supply chain decision optimization problem under uncertain demand considering incentive compatibility theory, a two-tier remanufacturing green closed-loop supply chain model was established by using game theory, and four models, namely, centralized, decentralized, contractual decentralized, and differential subsidy, were investigated to analyze the optimal solutions under two carbon tax subsidy decisions. It is found that both TE and AE models can optimize the supply chain and the AE model is more profitable with less carbon emission. The study not only helps high-tech manufacturing enterprises to build green supply chains for remanufacturing, but also provides theoretical support for the government to formulate differentiated incentive policies.

In order to explore the influencing factors of related subjects' strategic behavior under the background of joint reward and punishment of environmental credit, and considering public environmental attention, a three-party stochastic evolutionary game model of local government, third-party governance enterprise and polluting enterprise is constructed. The It＾o stochastic differential equation theory is used to analyze the conditions of game subject behavior stability while numerical simulation method is used to investigate the strategic evolution of the game subject. The results show that the positive pollution control strategy of third-party governance enterprise will drive polluting enterprise to choose environmental protection and trustworthy strategy, and their strategy choice will influence each other. Public environmental attention has promoting effect to the tripartite game subject strategy convergence. For the strategy of local government, the government at a higher level of penalties is of decisive role for its strategy choice. Rewards and punishments distribution ratio has the directional difference to third party governance and pollution enterprise strategy convergence, and reward incentive is more significant.

To explore the factors affecting collaborative innovation between military and civilian enterprises, a four stage game model for technological innovation cooperation between military and civilian enterprises was constructed based on the Cournot model and extended game theory, and numerical simulation analysis was conducted. The results indicate that in cooperative research and development, both technology spillover and R&D efficiency improvement will increase the investment and revenue of both companies, while in independent research and development, technology spillover and R&D efficiency improvement will reduce the R&D investment and revenue of the enterprise; There is heterogeneity between military and civilian enterprises. Therefore, implementing targeted measures to improve research and development efficiency and technology spillover level can promote research and development cooperation and enhance the efficiency of military civilian integration technology innovation cooperation.

Machine learning methods have made significant advancements in the field of fault diagnosis, especially for the complex industrial processes. However, most existing methods only consider the features of individual samples or pairwise relationships between samples, rarely taking into account higher-order relationships among samples and structural diversity among samples. Therefore, this paper proposes a fault diagnosis method based on a multilayer hypergraph convolutional neural network. The method first utilizes multiple similarity indicators to construct multilayer hypergraphs with different structures. Then, it performs intralayer hypergraph convolution and interlayer graph convolution operations to extract and fuse features. Experiments are conducted on the simulation dataset of SEU and the real dataset of the coal mill unit, and the results show that this method can effectively improve the accuracy of fault diagnosis.

A data-driven control scheme based on dynamic event triggered strategy is proposed for bipartite consensus control of complex nonlinear MASs with unmeasurable external disturbance. The dynamic model of MASs with external disturbances is transformed into an equivalent data model using pseudo partial derivative, and radial basis function neural network is used to estimate the variation of unmeasured external disturbance. Based on the above data model, utilizing model free adaptive iterative learning algorithm combined with dynamic event triggered strategy, a dynamic event triggered based model-free iterative bipartite consensus control scheme is proposed. Then, the stability analysis of the proposed control scheme is given. The effectiveness of this control scheme is verified by simulation.

To solve the real-time tracking problem of photovoltaic (PV) MPPT and reduce the loss of output power of the PV system when the PV array is locally shaded, a two-layer control model based on the Improved Dung Beetle Optimization Algorithm (IDBO) combined with the Variable Step Perturbation and Observation (IP&O) method is proposed. The optimal individual guidance strategy and Lévy flight are introduced into the dung beetle optimization algorithm in the upper-layer model, which dynamically adjusts the region boundaries to quickly search for the global maximum power point and reduce the tracking fluctuations; the IP&O is used in the lower-layer model for the local tracking, which ensures the accuracy and improves the real-time convergence of the algorithm at the same time. The effectiveness of this algorithm in MPPTs is verified by comparing with other algorithms in three complex shading environments and dynamic shaded environments.

A novel four-dimensional memristor self-excited oscillation system is introduced in this paper. The most important feature of the system is the introduction of a memristor into the circuit, thereby the chaos of the system is stronger. In this paper, the system is analyzed in terms of the phase diagram, bifurcation diagram, and Lyapunov exponent spectrum. The results indicate that the system reveals complex dynamic phenomena and the chaotic sequence generated by the iterative system has strong randomness, which is suitable for image encryption. Based on this system, the Arnold scrambling algorithm and the diffusion algorithm are used to encrypt the image. Then the histograms and adjacent pixel maps of the new system are plotted through numerical simulations. Histograms and adjacent pixel maps are drawn using Matlab,and various safety analyses are carried out. All indicators of this algorithm are close to theoretical values. The results indicate that image encryption applications have high safety performance.