Improved fish migration optimization with the opposition learning based on elimination principle for cluster head selection
Mathematics & Actuarial Science Department
In the wireless sensor networks (WSN), the cluster head node receives and aggregates the information collected by the sensors and forwards it to the base station (BS). Appropriate cluster head nodes help reduce the energy consumed for information transmission and prolong the network’s lifetime. The fish migration optimization (FMO) algorithm is an emerging meta-heuristic algorithm, that imitates the grayling foraging and breeding in nature. This paper proposes a modified FMO (modFMO) algorithm, which uses an opposition learning based on the elimination principle and Cauchy-based mutation to enhance the FMO algorithm. The improved modFMO is compared with some excellent algorithms on CEC 2014 function sets, and the Friedman ranks test demonstrates the effectiveness of the improvement. In this paper, the FMO algorithm and the modFMO algorithms with the centralized control algorithm are applied to select the optimal cluster head respectively. This paper takes the average energy consumption of the cluster head nodes as the objective function. Compared with other classic algorithms, the simulation results demonstrate that the modFMO algorithm can extend the lifetime of wireless networks, reduce energy consumption, improve information transmission efficiency and improved the ability of FMO algorithm.
(2022). Improved fish migration optimization with the opposition learning based on elimination principle for cluster head selection. 3, 1017–1038.
Xu, Xing-Wei, et al.
"Improved fish migration optimization with the opposition learning based on elimination principle for cluster head selection." vol. 3, 2022, pp. 1017–1038.