## 4 Survey of Existing Works
### Ant Colony Optimization  
Like some other swarm intelligence approaches that take inspiration from the social behaviors of insects and animals, ants have inspired a number of methods and techniques among which the most widely studied is the general purpose optimization technique known as ant colony optimization (ACO). ACO is a method which is inspired from the foraging behavior of some ant species. These ants deposit pheromone on the ground in order to mark their paths from the nest to food that should be \begin{table}
\begin{tabular}{c l l l} \hline \hline
**Authors** & **Literature** & **Main Contributions** & **Area of** \\  & & & **Optimization** \\ \hline [PERSON] _et al._ & Energy-aware clustering for wireless sensor networks using particle & 1. Defined a new cost function & Energy \\ [PERSON] _et al._ & Energy-aware clustering for wireless sensor networks using particle & 2. Proposed protocol selects a high-energy node as a CH & Aware \\  & swarm optimization [59] & and produces clusters that are equally placed throughout the entire WSN fewd & Clustering \\ \hline [PERSON] _et al._ & Particle swarm optimization for mobile ad hoc networks clustering [60] & 1.Divided Range Particle Swarm Optimization (DRFSO) algorithm was applied to the revised & Energy \\  & & Weighted Clustering Algorithm & Aware \\  & & Weighted Clustering Algorithm & Clustering \\ \hline [PERSON] _et al._ & Particle swarm optimizers for cluster formation in wireless sensor networks [61] & 1. Four variants of PSO were proposed & Energy \\  & & 2. Considers only the physical distances between nodes & Aware \\  & & and their assigned cluster-heads & Clustering \\ \hline [PERSON] _et al._ & Cluster heads election analysis for multi-hop wireless sensor networks & 1. Node and its CH is engaged in a multi-hop & Energy \\  & based on weighted graph and & communication & Aware \\  & particle swarm optimization [62] & 2. CHs were elected by maximum residual energy and in & Clustering \\ \hline [PERSON] _et al._ & Optimal power scheduling for correlated data fusion in wireless & 1. Addresses the problem of optimal power allocation & \\ _al._ & sensor networks via constrained & through constrained PSO & Data \\  & PSO [66] & 2. Objective is to minimize the energy expenditure while & Aggregation \\ \hline [PERSON] _et al._ & Swarm intelligence based & 1. Hybrid approach of ant-based control and PSO for & \\ [PERSON] _et al._ & optimization and control of decentralized serial sensor networks & hierarchy and threshold management & Data \\  & & decentralized serial sensor networks & 2. 40\% performance improvements in terms of Bayesian & Aggregation \\  & [67] & risk value & \\ \hline [PERSON] _et al._ & Dynamic sensor management using multi objective particle swarm & 1. A binary multi objective PSO for optimal sensor & \\  & optimizer [68] & management & Data \\  & & multi objective particle swarm & 2. PSO is modified to optimize two objectives: accuracy & Aggregation \\  & & and time & \\ \hline [PERSON] _et al._ & Multi-Source Temporal Data & 1. A multi-source temporal data aggregation model is & \\  & Aggregation in Wireless Sensor & presented & Data \\  & Networks [70] & 2. Proposes an energy-efficient multi-source temporal & Aggregation \\  & Networks [70] & data aggregation model called MSTDA & \\ \hline [PERSON] _et al._ & Linear Decision Fusion under the & 1. Designed a linear decision fusion rule & \\  & Control of Constrained PSO for & 2. Proposed a way of controlling the parameters of the & Data \\  & WSNs [71] & model taking the advantage of the constrained PSO & Aggregation \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._followed by other members of the colony. This algorithm has a mechanism for solving discrete optimization problems in various engineering domains. Initially the optimization algorithm was proposed by [PERSON] in 1999 [71]. The primary idea has since been widely researched and diversified to solve a broader class of numerical problems.