# An Improvement of a Mapping Method Based on Ant Colony Algorithm Applied to Smart Cities
## 1 Introduction  
Complex decision-making and optimization problems occupy a very important position in smart cities. Swarm intelligence methods have become one of the important means to solve complex optimization problems in smart cities by simulating the emergence mechanism of intelligent behavior of social animals in nature [1]. The ant colony algorithm in swarm intelligence has a wide range of application scenarios in the field of smart cities, and is widely used in smart transportation, path planning, and smart cities. An important problem facing simulations relying on neuromorphic computing architecture is how to map complex neural networks to node networks; that is, how to achieve the mapping between logical and physical networks. A logic network, namely problem network and task network, when simulating a biological neural network, has a huge scale which is reflected in two aspects: one is the huge number of neurons, and the other is the complex connection between neurons. In addition, the logical network targets different application scenarios, and it is impossible to design the topology of the physical network structure for a specific logical network. At the same time, the nodes of physical networks have a limited capacity, a limited number of neurons capable of support, and a limitedability to route cells. Thus, mapping a logical network with a physical network has become a key challenge in system design. In this context, many scholars have proposed a variety of algorithms; for example, an innovative ant colony optimization based on parameter tuning to solve the matching problem [2]. To sum up, it can be roughly divided into four categories [3]. 1. The greedy algorithm and its variants. The greedy algorithm is the simplest mapping algorithm; it is divided into local greedy and global greedy [4]. In local greedy, a starting node is first selected in each of the two graphs, and then one of the adjacent nodes is selected and added to the map. Global greedy will be based on a specific global strategy, such as the weights of the edges in the connection matrix. There are also algorithms that combine local and global, taking advantage of their respective characteristics. 1. Graphical division. The second common mapping algorithm is a graph division-based algorithm, such as a k-way graph division that divides the problem graph and the node topological graph recursion into smaller partitions, and then maps while expanding the recursion. 1. Algorithm based on graph similarity. Another mapping algorithm is a graph similarity-based algorithm, where the adjacency matrix of the problem graph and the node topological graph are arranged into a corresponding canonical form by some strategy, and are then mapped based on this rearranged adjacency matrix. 1. The heuristic algorithm. Heuristic algorithms summarize the past experience, and include genetic algorithms (GA), ant colony algorithms (ACA), particle swarm optimization (PSO) algorithms, etc. In general, such algorithms will have an objective function and will iterate based on this goal to gradually find the optimal strategy. The ant colony algorithm is fast in computation and has received attention from researchers due to its unique advantages [5; 6; 7]. [PERSON] et al. [8] proposed an adaptive variable neighborhood search ant colony algorithm (AVNSACA) to make up for the lack of pheromones in the algorithm's early stage, which avoid the algorithm falling into local optimum. [PERSON] et al. [9] proposed an ant colony algorithm for the multi-compartment vehicle routing problem. Besides the differential evolution algorithm based on the complex networks algorithm (CNDE) are proposed for the topology mapping problem [10; 11]. Because neural networks have a 3-dimensional (3D) structure, each neuron has multiple inputs and provides outputs to multiple neurons, making the application of the greedy algorithm difficult. The algorithms based on graph division and graph similarity are essentially greedy.