Smart sensor network has the characteristics of low cost, low power consumption, real time, strong adaptability, etc., and it has a wide range of application prospects in the agricultural field. However, the smart sensor node is limited by its own energy; it also faces many bottlenecks in agricultural applications. Therefore, balancing the energy consumption of nodes and extending the life of the network are important considerations in the design of efficient routing for smart sensor networks. Aiming at the problem of energy constraints, this paper proposes an intelligent sensor network clustering algorithm based on adaptive chaotic ant colony optimization (ACACO). ACACO introduces logical chaotic mapping to interfere with the pheromone on the initial path and uses the adaptive strategy to improve the transition probability formula. After selecting the best next hop node, the advancing ants are released to update the local pheromone, and the current pheromone content is adjusted by the chaos factor. When the ants determine the path, they release subsequent ants to update the global pheromone. The simulation results show that ACACO has obvious advantages over genetic algorithm (GA) and particle swarm optimization (PSO).

中文翻译：

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智能传感器网络中能量优化的自适应混沌蚁群优化

智能传感器网络具有低成本、低功耗、实时性、适应性强等特点，在农业领域具有广泛的应用前景。然而，智能传感器节点受自身能量的限制；它在农业应用方面也面临许多瓶颈。因此，平衡节点能耗和延长网络寿命是智能传感器网络高效路由设计的重要考虑因素。针对能量约束问题，提出一种基于自适应混沌蚁群优化(ACACO)的智能传感器网络聚类算法。ACACO引入逻辑混沌映射来干扰初始路径上的信息素，并使用自适应策略改进转移概率公式。选择最佳下一跳节点后，释放前进的蚂蚁更新本地信息素，通过混沌因子调整当前信息素含量。当蚂蚁确定路径时，它们会释放后续蚂蚁来更新全局信息素。仿真结果表明，ACACO相比遗传算法（GA）和粒子群优化（PSO）具有明显的优势。