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3D path planning method for multi-UAVs inspired by grey wolf algorithms
(LIBRARY & INFORMATION CENTER, 2021)
Efficient and collision-free pathfinding, between source and destination locations for multi-Unmanned Aerial Vehicles (UAVs), in a predefined environment is an important topic in 3D Path planning methods. Since path planning ...
Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems
(Elsevier B.V., 2021)
This paper introduces three hybrid algorithms that help in solving global optimization problems using reinforcement learning along with metaheuristic methods. Using the algorithms presented, the search agents try to find ...
Adapted-RRT: novel hybrid method to solve three-dimensional path planning problem using sampling and metaheuristic-based algorithms
(Springer Science and Business Media Deutschland GmbH, 2021)
Three-dimensional path planning for autonomous robots is a prevalent problem in mobile robotics. This paper presents three novel versions of a hybrid method designed to assist in planning such paths for these robots. In ...
Improving the performance of hierarchical wireless sensor networks using the metaheuristic algorithms: efficient cluster head selection
(Emerald Group Holdings Ltd., 2021)
Purpose: Efficient resource utilization in wireless sensor networks is an important issue. Clustering structure has an important effect on the efficient use of energy, which is one of the most critical resources. However, ...
Optimal characterization of a microwave transistor using grey wolf algorithms
(SPRINGER, 2021)
Modern time microwave stages require low power consumption, low size, low-noise amplifier (LNA) designs with high-performance measures. These demands need a single transistor LNA design, which is a challenging multi-objective, ...
WOASCALF: A new hybrid whale optimization algorithm based on sine cosine algorithm and levy flight to solve global optimization problems
(Elsevier Ltd, 2022)
In recent years, researchers have been focused on solving optimization problems in order to determine the global optimum. Increasing the dimension of a problem increases its computational cost and complexity as well. In ...
Maximizing coverage and maintaining connectivity in WSN and decentralized IoT: an efficient metaheuristic-based method for environment-aware node deployment
(Springer Science and Business Media Deutschland GmbH, 2022)
The node deployment problem is a non-deterministic polynomial time (NP-hard). This study proposes a new and efficient method to solve this problem without the need for predefined circumstances about the environments ...
Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems
(Springer, 2022)
This study proposes a new metaheuristic algorithm called sand cat swarm optimization (SCSO) which mimics the sand cat behavior that tries to survive in nature. These cats are able to detect low frequencies below 2 kHz and ...
A reinforcement learning-based metaheuristic algorithm for solving global optimization problems
(Elsevier, 2023)
The purpose of this study is to utilize reinforcement learning in order to improve the performance of the Sand Cat Swarm Optimization algorithm (SCSO). In this paper, we propose a novel algorithm for the solution of global ...
Optimal data transmission and pathfinding for WSN and decentralized IoT systems using I-GWO and Ex-GWO algorithms
(Elsevier B.V., 2023)
Efficient resource use is a very important issue in wireless sensor networks and decentralized IoT-based systems. In this context, a smooth pathfinding mechanism can achieve this goal. However, since this problem is a ...