## 2 Related Work
### Hybrid SI-Based Routing  
Recently, research has investigated hybrid combinations of the ACO-based routing algorithm and other optimisation algorithms, in order to solve some specific issues.  
Fuzzy and Ant Colony Optimisation Based Combined MAC, Routing, and Unequal Clustering Cross-Layer Protocol for Wireless Sensor Networks (FAMCROW) [25] utilised fuzzy logic with residual energy, the number of neighbouring nodes and the quality of the communication link as input variables for cluster head selection. In doing so, the algorithm benefits from avoiding the hot spot problem.  
The ACO routing algorithm is adopted in inter-cluster multi-hop routing from cluster heads to the sink node.  
[PERSON] et al. [26] presented a hybrid ACO and Particle Swarm Optimisation (PSO)-based energy-efficient clustering and tree-based routing protocol. In the protocol, hybrid ACO-PSO-based data aggregation comes in action to improve the inter-cluster data aggregation further.  
The proposed routing technique [27] involves ACO and the Artificial Immune System (ANT-AIS), which aimed to balance energy exhaustion so as to maximise the network lifetime. In ANT-AIS, an ant colony algorithm is used for determining the optimum paths from sensor nodes to the sink. The artificial immune system is used for solving packet loop problems and controlling route direction.  
[PERSON] et al. [28] combined ACO with breadth-first search to discover the shortest path in order to improve data transmission with the least amount of energy consumption, as well as reducing the probability of data loss.  
CB-RACOn [29] combines the ACO meta-heuristic with the computationally-inexpensive and distributed community detection technique Label Propagation (LP) [30], creates communities in WSNs and satisfies the balance of energy consumption by routing data inside communities through swarm intelligence.  
Essentially, the design of the ACO-based routing algorithm lies in the probabilistic strategy of route discovery, the update of the pheromone trail and the heuristic function [31; 32]. However, due to the inborn defects of the ACO-based routing protocol, the route discovery process is time consuming and cannot find a feasible route to the destination quickly. Due to the slow convergence behaviour, for a large-scale network routing optimisation problem, it is difficult to find an optimal path from a large number of chaotic paths in a short period of time.  
Furthermore, the ACO-based routing protocol is prone to premature, stagnation phenomenon, which hinders further route discovery [33]. In ACO-based routing protocols, the basis of individual ant optimisation is pheromone concentration and heuristic value. However, at the beginning of the search, the pheromone concentration is uniform throughout the search space. Therefore, it cannot be guaranteed that the initial path selected by artificial ants is the one to reach the optimal path, which then in turn leads to stagnation [33; 34]. After searching for a certain time, pheromone accumulates on the local optimal path, which leads to the convergence of the solution found by all artificial ant individuals. The pheromone accumulations and subsequent convergence prevents further searches for the global optimal solution and misses the opportunity to discover an optimal solution.