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Öğe 3D Path Planning Method for Multi-UAVs Inspired by Grey Wolf Algorithms(Library & Information Center, Nat Dong Hwa Univ, 2021) Kiani, Farzad; Seyyedabbasi, Amir; Aliyev, Royal; Shah, Mohammed Ahmed; Gulle, Murat UgurEfficient 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 is a Non-deterministic Polynomial-time (NP-hard) problem, metaheuristic approaches can be applied to find a suitable solution. In this study, two efficient 3D path planning methods, which are inspired by Incremental Grey Wolf Optimization (I-GWO) and Expanded Grey Wolf Optimization (Ex-GWO), are proposed to solve the problem of determining the optimal path for UAVs with minimum cost and low execution time. The proposed methods have been simulated using two different maps with three UAVs with diverse sets of starting and ending points. The proposed methods have been analyzed in three parameters (optimal path costs, time and complexity, and convergence curve) by varying population sizes as well as iteration numbers. They are compared with well-known different variations of grey wolf algorithms (GWO, mGWO, EGWO, and RWGWO). According to path cost results of the defined case studies in this study, the I-GWO-based proposed path planning method (PPI-GWO) outperformed the best with %36.11. In the other analysis parameters, this method also achieved the highest success compared to the other five methods.Öğe Adapted-RRT: novel hybrid method to solve three-dimensional path planning problem using sampling and metaheuristic-based algorithms(Springer London Ltd, 2021) Kiani, Farzad; Seyyedabbasi, Amir; Aliyev, Royal; Gulle, Murat Ugur; Basyildiz, Hasan; Shah, M. AhmedThree-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 this paper, an improvement on Rapidly exploring Random Tree (RRT) algorithm, namely Adapted-RRT, is presented that uses three well-known metaheuristic algorithms, namely Grey Wolf Optimization (GWO), Incremental Grey Wolf Optimization (I-GWO), and Expanded Grey Wolf Optimization (Ex-GWO)). RRT variants, using these algorithms, are named Adapted-RRTGWO, Adapted-RRTI-GWO, and Adapted-RRTEx-GWO. The most significant shortcoming of the methods in the original sampling-based algorithm is their inability in finding the optimal paths. On the other hand, the metaheuristic-based algorithms are disadvantaged as they demand a predetermined knowledge of intermediate stations. This study is novel in that it uses the advantages of sampling and metaheuristic methods while eliminating their shortcomings. In these methods, two important operations (length and direction of each movement) are defined that play an important role in selecting the next stations and generating an optimal path. They try to find solutions close to the optima without collision, while providing comparatively efficient execution time and space complexities. The proposed methods have been simulated employing four different maps for three unmanned aerial vehicles, with diverse sets of starting and ending points. The results have been compared among a total of 11 algorithms. The comparison of results shows that the proposed path planning methods generally outperform various algorithms, namely BPIB-RRT*, tGSRT, GWO, I-GWO, Ex-GWO, PSO, Improved BA, and WOA. The simulation results are analysed in terms of optimal path costs, execution time, and convergence rate.Öğe Adaptive Metaheuristic-Based Methods for Autonomous Robot Path Planning: Sustainable Agricultural Applications(MDPI, 2022) Seyyedabbasi, Amir; vd.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 significant and persistent problems related to robots is 3D path planning, which is an NP-hard problem, for mobile robots. In this paper, efficient methods are proposed by two metaheuristic algorithms (Incremental Gray Wolf Optimization (I-GWO) and Expanded Gray Wolf Optimization (Ex-GWO)). The proposed methods try to find collision-free optimal paths between two points for robots without human intervention in an acceptable time with the lowest process costs and efficient use of resources in large-scale and crowded farmlands. Thanks to the methods proposed in this study, various tasks such as tracking crops can be performed efficiently by autonomous robots. The simulations are carried out using three methods, and the obtained results are compared with each other and analyzed. The relevant results show that in the proposed methods, the mobile robots avoid the obstacles successfully and obtain the optimal path cost from source to destination. According to the simulation results, the proposed method based on the Ex-GWO algorithm has a better success rate of 55.56% in optimal path cost.Öğe Designing A Dynamic Protocol For Real-Time Industrial Internet Of Thingsbased Applications By Efficient Management Of System Resources(Sage, 2019) Kiani, Farzad; Nematzadehmiandoab, Sajjad; Seyyedabbasi, AmirWireless sensor networks have gained the attention of researchers from various fields due to increased applicability. This has thus led to rapid development in the field. However, these networks still suffer from various challenges and limitations. These range from computation and processing power and available energy to mention but a few. These problems are even much more pronounced in some areas of the field such as real-time Internet of Things-based applications. In this study, a dynamic protocol that efficiently utilizes the available resources is proposed. The protocol employs five developed algorithms that aid the data transmission, neighbor, and optimal path finding processes. The protocol can be utilized in, but not limited to, real-time large-data streaming applications. The protocol is implemented on sensor nodes that are custom made by our research team. In this article, a structure that enables the sensor devices to communicate with each other over their local network or Internet as required in order to preserve the available resources is defined. Both theoretical and experimental result analyses of the entire protocol in general and individual algorithms are also performed.Öğe Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems(Elsevier, 2021) Seyyedabbasi, Amir; Aliyev, Royal; Kiani, Farzad; Gulle, Murat Ugur; Basyildiz, Hasan; Shah, Mohammed AhmedThis 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 a global optimum avoiding the local optima trap. Compared to the classical metaheuristic approaches, the proposed algorithms display higher success in finding new areas as well as exhibiting a more balanced performance while in the exploration and exploitation phases. The algorithms employ reinforcement agents to select an environment based on predefined actions and tasks. A reward and penalty system is used by the agents to discover the environment, done dynamically without following a predetermined model or method. The study makes use of Q-Learning method in all three metaheuristic algorithms, so-called RLI-GWO, RLEx-GWO, and RLWOA algorithms, so as to check and control exploration and exploitation with Q-Table. The Q-Table values guide the search agents of the metaheuristic algorithms to select between the exploration and exploitation phases. A control mechanism is used to get the reward and penalty values for each action. The algorithms presented in this paper are simulated over 30 benchmark functions from CEC 2014, 2015 and the results obtained are compared with well-known metaheuristic and hybrid algorithms (GWO, RLGWO, I-GWO, Ex-GWO, and WOA). The proposed methods have also been applied to the inverse kinematics of the robot arms problem. The results of the used algorithms demonstrate that RLWOA provides better solutions for relevant problems. (C) 2021 Elsevier B.V. All rights reserved.Öğe A Hybrid Metaheuristic Algorithm for the Localization Mobile Sensor Nodes(Springer International Publishing Ag, 2022) Kirtil, Hasan Sencer; Seyyedabbasi, AmirWireless sensor Networks replace existing technologies in today's world. It has some problems in Wireless Sensor Networks (WSN) such as security, energy optimization or mobile localization. Wireless sensor networks are used extensively in sectors such as military sectors, geographic information systems, and meteorology systems. Therefore, efforts are being made to use wireless sensor networks faster and more accurately localization problem, which is among these problems, has an important place today. It is very important to find the location of the sensors quickly and with low error rate in applications such as military applications, emergency assistance. The basis of the problem is to find the geographical points of the nodes randomly distributed in the environment and whose locations are unknown, through anchor nodes. In this study, a hybrid structure was created by using Grasshopper Optimization Algorithm (GOA) and Whale Optimization Algorithm (WOA) metaheuristic algorithms for the location of nodes, and a hybrid structure was used to detect the locations of nodes in wireless sensor networks. Metaheuristic algorithms basically imitate these actions by digitizing the events of living things such as feeding, movement or hunting. While establishing the WOA algorithm used in the study, the hunting methods of whales in nature were taken into account. While establishing the GOA algorithm used in the study, the movement and communication between grasshoppers taken into account. Main purpose of this paper, determine faster and more suitable for the localization problem in wireless sensor networks with the new algorithm.Öğe Improving the performance of hierarchical wireless sensor networks using the metaheuristic algorithms: efficient cluster head selection(Emerald Group Publishing Ltd, 2021) Kiani, Farzad; Seyyedabbasi, Amir; Nematzadeh, SajjadPurpose 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, it is extremely vital to choose efficient and suitable cluster head (CH) elements in these structures to harness their benefits. Selecting appropriate CHs and finding optimal coefficients for each parameter of a relevant fitness function in CHs election is a non-deterministic polynomial-time (NP-hard) problem that requires additional processing. Therefore, the purpose of this paper is to propose efficient solutions to achieve the main goal by addressing the related issues. Design/methodology/approach This paper draws inspiration from three metaheuristic-based algorithms; gray wolf optimizer (GWO), incremental GWO and expanded GWO. These methods perform various complex processes very efficiently and much faster. They consist of cluster setup and data transmission phases. The first phase focuses on clusters formation and CHs election, and the second phase tries to find routes for data transmission. The CH selection is obtained using a new fitness function. This function focuses on four parameters, i.e. energy of each node, energy of its neighbors, number of neighbors and its distance from the base station. Findings The results obtained from the proposed methods have been compared with HEEL, EESTDC, iABC and NR-LEACH algorithms and are found to be successful using various analysis parameters. Particularly, I-HEELEx-GWO method has provided the best results. Originality/value This paper proposes three new methods to elect optimal CH that prolong the networks lifetime, save energy, improve overhead along with packet delivery ratio.Öğe MAP-ACO: An efficient protocol for multi-agent pathfinding in real-time WSN and decentralized IoT systems(Elsevier, 2020) Seyyedabbasi, Amir; Kiani, FarzadEfficient energy consumption is one of the main problems in wireless sensor networks routing protocols. Since the sensor nodes have limited battery level and memory space, it is important to manage these resources efficiently. Although there are studies in this subject in recent years, it is lacking in concurrent and real-time environments with multi-agents. The importance of this issue is increasing more especially for decentralized IoT systems. This paper presents a novel routing protocol based on ant colony optimization for multi-agents that manages network resources adequately in real-time conditions. The proposed method is used, both to find the next destination of ants, and to manage pheromone update and evaporation rate operators. This method takes into account some key parameters such as remaining energy, buffer size, traffic rate, and distance when selecting the next destination under different conditions. The proposed method finds the optimal paths with low energy consumption thereby prolonging the network lifetime in concurrent and parallel conditions. The simulation results of the proposed method have given good results, in terms of network lifetime and energy consumption, when compared with other ant colony optimization (ACO)-based routing protocols.Öğe Optimal characterization of a microwave transistor using grey wolf algorithms(Springer, 2021) Kiani, Farzad; Seyyedabbasi, Amir; Mahouti, PeymanModern 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, multi-dimensional optimization problem that requires solving objectives with non-linear feasible design target space, that can only be achieved by optimally selecting the source (Z(S)) and load (Z(L)) terminations. Meta-heuristic algorithms (MHAs) have been extensively used as a search and optimization method in many problems in the field of science, commerce, and engineering. Since feasible design target space (FDTS) of an LNA transistor (NE3511S02 biased at VDS = 2 V and IDS = 7 mA) is a multi-objective multi-variable optimization problem the MHA can be considered as a suitable choice. Three different types of grey wolf variants inspired algorithms had been applied to the LNA FDTS problem to obtain the optimal source and load terminations that satisfies the required performance measures of the aimed LNA design. Furthermore, the obtained results are justified via the use of the Electromagnetic Simulator tool AWR. As a result, an efficient optimization method for optimal determination of Z(S) and Z(L) terminations of a high-performance LNA design had been achieved.
