Socially-aware Multi-Agent Systems （MASs） rely on leadership structures to enhance collaborative decision-making efficiency， but are vulnerable to privacy leakage risks caused by the analysis of state trajectories in Markov Decision Processes （MDPs）， exposing critical nodes to targeted attacks. To address these challenges， a Virtual Leader Minimal Dependency Privacy protection strategy （VLMDs-Privacy） was proposed， which achieved secure and efficient collaborative decision-making in MAS through the following methods： 1） A State Transition Adaptive Differential Privacy mechanism （STADP） was designed to establish a dynamic mapping between state-transition probabilities and privacy budgets， protecting MDP state trajectories from reverse inference attacks； 2） A Virtual Leader Minimal Dependency strategy （VLMDs） was developed to reduce reliance on virtual leaders while achieving globally optimal decision-making， thereby significantly improving resistance to single-point failures； 3） A privacy-efficiency dual-regulation mechanism was constructed to dynamically allocate privacy budgets based on agent behavior credibility， achieving an adaptive trade-off between social awareness and privacy protection. Experimental results showed that under a strong privacy constraint （ \epsilon = 0.1）， VLMDs-Privacy achieved an average arrival success rate of 94.2% in navigation and dynamic maintenance scenarios， outperforming the conventional leader-based differential privacy scheme VLDPs-Privacy （27.9%） by 66.3 percentage points， with only a 3.3% drop compared to non-private settings. These findings validate the robustness of VLMDs-Privacy in maintaining system collaboration capability and privacy preservation efficiency under strong privacy constraints， providing theoretical and technical support for collaborative decision-making in privacy-sensitive MAS deployed in open environments.

The routing scheme is one of the key factors which influence the lifetime of Wireless Sensor Network (WSN). The network can be paralyzed easily as a result of large energy consumption of key nodes by heavy communication. To solve the problem of energy consumption of key nodes in WSNs, a new ant colony routing algorithm on Dynamic Adjustment of Forward Angle based on Residual Energy (DAFARE) was proposed. Firstly, the nodes chose the next-hop node according to residual energy and distance in the range of initial forward angle; secondly, the forward angle was adjusted dynamically in the view of residual energy of nodes within the scope of forward angle; finally, early death of key nodes was avoided successfully. The simulation suggested that the effective life could be improved approximately 50% by DAFARE, compared with ant colony optimization algorithm based on Function of Multi-object Evaluation and Positive-Negative Feedback (FMEPNF). The experimental results show that, the network energy consumption of DAFARE can be balanced effectively, the lifetime is prolonged, and the coverage of WSN is guaranteed.

0/1 Knapsack Problems is a typical optimization problem in Operations Research, Genetic Algorithms is one of the most commonly evolutionary algorithms used to solve the problems. This paper proposed an improved adaptive genetic algorithm. In this algorithm, we introduce the evolution number into the crossover probability and mutation probability formula、improve the crossover operator and mutation operator, and add a pattern replacement operation. The simulation results show that, the solution speed and the optimal solution have greatly improved with our algorithms.