Browsing by Author "Seyyedabbasi, Amir"
Now showing items 1-13 of 13
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3D path planning method for multi-UAVs inspired by grey wolf algorithms
Kiani, Farzad; Seyyedabbasi, Amir; Aliyev, Royal; Shah, Mohammed Ahmed; Gulle, Murat Ugur (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 ... -
Adapted-RRT: novel hybrid method to solve three-dimensional path planning problem using sampling and metaheuristic-based algorithms
Kiani, Farzad; Seyyedabbasi, Amir; Aliyev, Royal; Gulle, Murat Ugur; Basyildiz, Hasan; Shah, Mohammad Ahmed (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 ... -
Adaptive metaheuristic-based methods for autonomous robot path planning: Sustainable agricultural applications
Kiani, F.; Seyyedabbasi, A.; Nematzadeh, S.; Candan, F.; Çevik, T. (MDPI, 2022)The increasing need for food in recent years means that environmental protection and sustainable agriculture are necessary. For this, smart agricultural systems and autonomous robots have become widespread. One of the most ... -
Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems
Seyyedabbasi, Amir; Aliyev, Royal; Kiani, Farzad; Gulle, Murat Ugur; Basyildiz, Hasan; Shah, Mohammad Ahmed (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 ... -
Improving the performance of hierarchical wireless sensor networks using the metaheuristic algorithms: efficient cluster head selection
Kiani, Farzad; Seyyedabbasi, Amir; Nematzadeh, Sajjad (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, ... -
Maximizing coverage and maintaining connectivity in WSN and decentralized IoT: an efficient metaheuristic-based method for environment-aware node deployment
Nematzadeh, Sajjad; Torkamanian-Afshar, Mahsa; Seyyedabbasi, Amir; Kiani, Farzad (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 ... -
Metaheuristic algorithms in IoT: optimized edge node localization
Kiani, Farzad; Seyyedabbasi, Amir (springer link, 2022)In this study, a new hybrid method is proposed by using the advantages of Grey Wolf Optimizer (GWO) and Moth-Flame Optimization (MFO) algorithms. The proposed hybrid metaheuristic algorithm tries to find the near-optimal ... -
Optimal characterization of a microwave transistor using grey wolf algorithms
Kiani, Farzad; Seyyedabbasi, Amir; Mahouti, Peyman (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, ... -
Optimal data transmission and pathfinding for WSN and decentralized IoT systems using I-GWO and Ex-GWO algorithms
Seyyedabbasi, Amir; Kiani, Farzad; Allahviranloo, Tofigh; Fernandez-Gamiz, Unai; Noeiaghdam, Samad (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 ... -
A reinforcement learning-based metaheuristic algorithm for solving global optimization problems
Seyyedabbasi, Amir (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 ... -
Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems
Seyyedabbasi, Amir; Kiani, Farzad (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 smart and mechanized agricultural application: From cultivation to harvest
Kiani, Farzad; Randazzo, Giovanni; Yelmen, Ilkay; Seyyedabbasi, Amir; Nematzadeh, Sajjad; Anka, Fateme Ayşin (MDPI, 2022)Food needs are increasing day by day, and traditional agricultural methods are not responding efficiently. Moreover, considering other important global challenges such as energy sufficiency and migration crises, the need ... -
WOASCALF: A new hybrid whale optimization algorithm based on sine cosine algorithm and levy flight to solve global optimization problems
Seyyedabbasi, Amir (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 ...
