A modified Whale Optimization Algorithm for exploitation capability and stability enhancement

Energy efficient optimal deployment of industrial wireless mesh networks using transient trigonometric Harris Hawks optimizer

“…One viable option is through utilizing meta-heuristic algorithms, which can provide a means to intelligently explore the vast solution space of WMN deployment, considering various constraints, objectives, and trade-offs. These algorithms are iterative and heuristic-based approaches that employ exploration and exploitation techniques to search for optimal or near-optimal solutions [11] , [12] . By evaluating different node placement configurations and iteratively refining them, these algorithms can converge towards solutions that optimize energy consumption while ensuring adequate coverage and connectivity [13] , [12] , [14] .…”

“…These algorithms are iterative and heuristic-based approaches that employ exploration and exploitation techniques to search for optimal or near-optimal solutions [11] , [12] . By evaluating different node placement configurations and iteratively refining them, these algorithms can converge towards solutions that optimize energy consumption while ensuring adequate coverage and connectivity [13] , [12] , [14] .…”

Improving Wild Horse Optimizer: Integrating Multistrategy for Robust Performance across Multiple Engineering Problems and Evaluation Benchmarks

“…The second classification is swarm intelligence algorithms that simulate swarm intelligence. For example, the particle swarm optimization (PSO) algorithm [11] is inspired by the predatory behavior of birds, the ant colony optimization (ACO) algorithm [12] is inspired by the collective behavior of ants when they search for food and their methods of communication and cooperation, the artificial bee colony (ABC) algorithm [13] is inspired by honey bee foraging behavior, the grey wolf optimization (GWO) algorithm [14] draws inspiration from the cooperative hunting and competitive behaviors observed in wild grey wolves during group activities and the hierarchical divisions among wolves, and the whale optimization algorithm (WOA) [15] is inspired by the cooperative and migratory behaviors observed in populations of whales in the ocean. For example, the political optimization (PO) algorithm [16] is inspired by political stage processes, the hunger game search (HGS) algorithm [17] is inspired by the idea of different individuals competing for limited resources and cooperating for survival, and the student psychology-based optimization (SPBO) algorithm [18] is inspired by the psychological mechanisms that influence students when dealing with various problems and challenges.…”

Multi-area load frequency regulation of a stochastic renewable energy-based power system with SMES using enhanced-WOA-tuned PID controller

“…Many significant and practical engineering optimization issues have recently been addressed using WOA. When used to solve optimization problems that call for very accurate outcomes, this might become difficult [ 26 ]. Several modifications to the classical algorithm have been suggested during the past few years.…”

“…Further reading on steps involved in the mathematical modeling that describes the three hunting mechanisms of WOA can be found in Refs. [ 26 , 29 , 30 ]. The Proportional Integral Derivative controller (PID) is the most common and widely utilized feedback controller in many modern industries.…”