Improved Cuckoo Search and Chaotic Flower Pollination optimization algorithm for maximizing area coverage in Wireless Sensor Networks
The popularity of Wireless Sensor Networks (WSNs) is rapidly growing due to its wide ranged applications such as industrial diagnostics, environment monitoring or surveillance. High quality construction of WSNs is increasingly demanding due to the ubiquity of WSNs. The current work is focused on improving one of the most crucial criteria that appear to exert an enormous impact on the WSNs performance, namely the area coverage. The proposed model is involved with sensor nodes deployment which maximizes the area coverage. This problem is proved to be NP hard. Although such algorithms to handle this problem with fairly acceptable solutions had been introduced, most of them still heavily suffer from several issues including the large computation time and solution instability. Hence, the existing work proposed ways to overcome such difficulties by proposing two nature based algorithms, namely Improved Cuckoo Search (ICS) and Chaotic Flower Pollination algorithm (CFPA) By adopting the concept of calculating the adaptability and a well designed local search in previous studies, those two algorithms are able to improve their performance. The experimental results on 15 instances established a huge enhancement in terms of computation time, solution quality and stability.
Neural Computing and Applications