Aquila Optimizer with parallel computing strategy for efficient environment exploration

Hybrid Vulture-Coordinated Multi-Robot Exploration: A Novel Algorithm for Optimization of Multi-Robot Exploration

“…Gul et al [37] proposed a novel Aquila Optimization Algorithm for Multi-Robot space exploration in a barrier-filled environment. The proposed Coordinated Multi-Robot Exploration Aquila Optimizer (CME-AO) Algorithm demonstrated superior performance compared to contemporary algorithms such as conventional CME, CME Arithmetic Optimization Algorithm (CME-AOA), and Frequency Modified Hybrid Whale Optimization Algorithm (FMH-WOA).…”

Hybrid Vulture-Coordinated Multi-Robot Exploration: A Novel Algorithm for Optimization of Multi-Robot Exploration

“…Gul et al [37] proposed a novel Aquila Optimization Algorithm for Multi-Robot space exploration in a barrier-filled environment. The proposed Coordinated Multi-Robot Exploration Aquila Optimizer (CME-AO) Algorithm demonstrated superior performance compared to contemporary algorithms such as conventional CME, CME Arithmetic Optimization Algorithm (CME-AOA), and Frequency Modified Hybrid Whale Optimization Algorithm (FMH-WOA).…”

Multi‐Robot Autonomous Exploration in Unknown Environments With Dynamic Obstacles

“…The path planning of multi-robot teams is optimised by ACO algorithm. A novel Aquila optimisation algorithm [16] is put forward for the multi-robot space exploration within an environment filled with obstacles. The algorithm that has been proposed has shown outstanding performance when contrasted with traditional optimisation algorithms.…”

Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective

“…The approach proposed in [ 49 ] combines a parallel computational Aquila Optimizer, a bio-inspired technology, with deterministic multi-agent exploration. The Aquila Optimizer is inspired by the behavior of eagles and uses a stochastic search strategy to explore the search space efficiently.…”

Hybrid Vulture-Coordinated Multi-Robot Exploration: A Novel Algorithm for Optimization of Multi-Robot Exploration

“…Gul et al [37] proposed a novel Aquila Optimization Algorithm for Multi-Robot space exploration in a barrier-filled environment. The proposed Coordinated Multi-Robot Exploration Aquila Optimizer (CME-AO) Algorithm demonstrated superior performance compared to contemporary algorithms such as conventional CME, CME Arithmetic Optimization Algorithm (CME-AOA), and Frequency Modified Hybrid Whale Optimization Algorithm (FMH-WOA).…”

Multi‐Robot Autonomous Exploration in Unknown Environments With Dynamic Obstacles

“…The path planning of multi-robot teams is optimised by ACO algorithm. A novel Aquila optimisation algorithm [16] is put forward for the multi-robot space exploration within an environment filled with obstacles. The algorithm that has been proposed has shown outstanding performance when contrasted with traditional optimisation algorithms.…”

Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective

“…The approach proposed in [ 49 ] combines a parallel computational Aquila Optimizer, a bio-inspired technology, with deterministic multi-agent exploration. The Aquila Optimizer is inspired by the behavior of eagles and uses a stochastic search strategy to explore the search space efficiently.…”

Hybrid Vulture-Coordinated Multi-Robot Exploration: A Novel Algorithm for Optimization of Multi-Robot Exploration

“…Gul et al [37] proposed a novel Aquila Optimization Algorithm for Multi-Robot space exploration in a barrier-filled environment. The proposed Coordinated Multi-Robot Exploration Aquila Optimizer (CME-AO) Algorithm demonstrated superior performance compared to contemporary algorithms such as conventional CME, CME Arithmetic Optimization Algorithm (CME-AOA), and Frequency Modified Hybrid Whale Optimization Algorithm (FMH-WOA).…”

Multi‐Robot Autonomous Exploration in Unknown Environments With Dynamic Obstacles

“…The path planning of multi-robot teams is optimised by ACO algorithm. A novel Aquila optimisation algorithm [16] is put forward for the multi-robot space exploration within an environment filled with obstacles. The algorithm that has been proposed has shown outstanding performance when contrasted with traditional optimisation algorithms.…”

Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective

“…The approach proposed in [ 49 ] combines a parallel computational Aquila Optimizer, a bio-inspired technology, with deterministic multi-agent exploration. The Aquila Optimizer is inspired by the behavior of eagles and uses a stochastic search strategy to explore the search space efficiently.…”