# An Effective Hybrid Routing Algorithm in WSN:
## 2 Related Works
### Ant Colony Optimization Routing Algorithm
The Ant Colony Optimization (ACO) algorithm is used to optimize the fuzzy rules in order to enhance the estimation quality of the routes, hence the energy efficiency of the network increases. The evaluation metric values, including the number of messages delivered to the sink node, an average cost of message, a packet loss rate, and the time of death of the first sensor node, have demonstrated the proposal is effective. [PERSON] et al. [22] utilized the ACO and borrowed an neural network (NN) rather than the greedy algorithm to build their chain of PEGASIS. The chain makes a path to send data to the BS more evenly distributed and reduces the total transmission distance much less. Therefore, it realizes energy consumption balancing between nodes. [PERSON] [23] put forward an optimal-distance-based transmission strategy for lifetime maximization on the basis of the ACO, in which a local optimal transmission-distance acquirement mechanism is designed for both highenergy efficiency and well energy balancing. Furthermore, a global optimal transmission-distance acquirement mechanism is provided to achieve energy depletion minimization for nodes with maximal energy consumption throughout the network. [PERSON] et al. [2] proposed an ACO-based routing algorithm called life time aware routing algorithm for wireless sensor networks (LTAWSN). It specifies its probability formula for reducing energy consumption of network nodes and also obtains more balanced transmission among the nodes and prolong the network lifetime. In addition, a new pheromone update operator is designed to integrate energy consumption and hop count together into routing choice. From the above representative research works that have been reviewed, it is clear to see that, significant efforts have been made to exploit and modify the ACO to achieve the efficient use of energy or balanced use of energy. Eventually all these are necessary to maximize the network lifetime. However, when applying the ACO to WSN routing optimization, still there are the following shortages that decrease its performance and preclude its practical application. 1. The searching time of these algorithms is long and the convergence speed of the algorithms is slow. 2. The algorithm is prone to be trapped to a local optimum. 3. When the global optimal path is found, the data will be transmitted through the optimal path all the way, causing the sensor nodes on the optimal path to deplete their energy at a faster pace and lead to the death of these nodes, and this phenomenon is called \"Hot Spot\", which is very unfavorable to the life time of the network. [PERSON] and [PERSON] [24] proposed a new hybrid method for route optimization in WSNs. For the convenience of reference, herein named ACO-GA. This method is based on a combination of modified ACO algorithm and genetic algorithm (GA). The initial solution is generated by the ACO as the population for the GA and next, the best solution is searched by further iterations of GA using selection, crossover and mutation. Thus it can accelerate the convergence speed of the ant colony algorithm, improve the efficiency and avoid the local optimum and precocity. Moreover, authors added a multi-path route for transmitting data. Simulation results showed that the new method was effective and has shown a better performance than ACO or GA individually. However, the computation complexity of such an algorithm increased, and the problem of energy equilibrium consumption was ignored. [PERSON] [25] pointed out three main drawbacks of the recent ACO algorithms. These include the implementation of pheromone is inefficient, the selection of next move by transition rules is very time consuming, and the local search used in the ACO operates without effective use of pheromone values. These drawbacks increased the computation time and space complexities of ACO, decreased its performance, and prevented it to be applied to large-scale instances. Then this paper suggested three new strategies to address those drawbacks.