Naked Mole-Rat Algorithm with Improved Exploration and Exploitation Capabilities to Determine 2D and 3D Coordinates of Sensor Nodes in WSNs

Optimization of FFF Process Parameters by Naked Mole-Rat Algorithms with Enhanced Exploration and Exploitation Capabilities

“…The revised NMRA improves performance by improving both its basic exploitation and exploration capabilities. The elite opposition-based learning (EOBL) strategy [ 30 ] improves the exploration of basic NMRA. Exploitation is improved by local neighborhood search (LNS) with information on the best solution to date in the small neighborhood of the solution [ 25 ].…”

“…NMRV 1.0 has improved NMRA’s exploration capabilities through implementing the EOBL Strategy [ 30 ]. The opposition-based NMRA, also called NMRV 1.0, Algorithm 2 detailed the pseudocode of NMRV 1.0.…”

“…The local neighborhood search (LNS) model has been employed [ 30 ] to enhance the exploitation capacity of NMRA. In the basic NMRA, workers change their position during the employee phase according to local information and their past experience.…”

“…NMRA version 3.0 has two modifications in basic NMRA to balance the exploitation and exploration capacity [ 30 ]. First, the exploration tendency is improved by the EOBL strategy.…”

An Enhanced Naked Mole Rat Algorithm for Optimal Cross-Layer Solution for Wireless Underground Sensor Networks

“…The solutions were updated based on the interactions of a group of scientists pursuing their research outcomes by raising finances to continue their research. In addition to the above-mentioned algorithms, there are some recent algorithms, such as Aquila optimizer (AO) [30], arithmetic optimization algorithm (AOA) [31], and hybridized optimization algorithms, such as adaptive SSA (ASSA) [32], adaptive FPA (AFPA) [33], CS version 1.0 (CV 1.0) [34], and NMRA version 3.0 (NMRV 3.0) [35] that are employed in every field of research, such as mathematics, medical imaging, robotics, antenna arrays, image segmentation, artificial immune systems, business management, and others. Furthermore, such algorithms are easier to implement and demonstrate a major contribution to basic optimization techniques.…”