Examining the Ant Colony Algorithm and the Cat Swarm Algorithm to Improve Energy Efficiency

Underwater communication is one of many growing communication technologies. Most governments and the private sector are interested in investing more funds into underwater communication research, autonomous underwater vehicles, etc. Independent vehicles or nonindependent autonomous vehicles working underwater need to use batteries. They use lithium-ion batteries that are suitable in underwater conditions and that have maximum battery storage capacities of a few kilowatts. After a certain duration, the batteries’ energy levels fully deplete, but this duration can be optimized through algorithms. More specifically, bio-inspired optimization algorithms can be used to extend battery life. The optimization algorithm helps to reduce battery power consumption and to choose the optimal communication path, both of which increase energy efficiency. Afterwards, the durations of the transmission and reception processes are extended. The main objective of this paper is to compare bio-inspired algorithms, namely the ant colony optimization algorithm (ACOA) and the cat swarm optimization algorithm (CSOA). A major contrast parameter between the two algorithms is energy efficiency. The moderate amount of energy difference between the two algorithm amounts to nearly 15%: The cat swarm optimization algorithm was 15% more energy efficient than the ant colony optimization algorithm.