Yazar "Seyyedabbasi, Amir" seçeneğine göre listele
Listeleniyor 1 - 20 / 28
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe 3D path planning method for multi-UAVs inspired by grey wolf algorithms(LIBRARY & INFORMATION CENTER, 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 A comprehensive survey : Physics-based algorithms(Elsevier, 2024) Seyyedabbasi, AmirTo solve computationally challenging optimization problems, metaheuristic algorithms can be used. Metaheuristic algorithms are inspired by natural processes and are used to solve complex optimization problems that cannot be solved with traditional optimization algorithms. They provide approximate solutions that are usually close to the optimal solution. The development of metaheuristics has been inspired by many natural and physical processes which, combined, have provided near-optimal or optimal solutions to several engineering problems. Specifically, this chapter discusses metaheuristic algorithms based on nonlinear physical phenomena with a concrete optimization paradigm, which have demonstrated remarkable exploration and exploitation capabilities for such problems. These metaheuristics have the ability to find the best solution from within a high-dimensional search space and can even find solutions to problems that are too complex for analytical methods. Additionally, they can efficiently explore a wide variety of possible solutions with minimal computational resources, making them ideal for engineering problems. In particular, this chapter describes a number of popular physics-based metaheuristics as well as the physical processes that underlie each of these algorithms. © 2024 Elsevier Inc. All rights reserved.Öğe A comprehensive survey: Evolutionary-based algorithms(Elsevier, 2024) Seyyedabbasi, AmirEvolutionary algorithms (EAs) are optimization algorithms based on natural selection and evolution. The operators selected, reproduce, crossover, and mutation are used to evolve a population of candidate solutions iteratively. A variety of complex optimization problems in diverse domains have been successfully solved using EAs. An overview of genetic algorithms (GAs), differential evolution (DE), and genetic programming (GP) is presented in this chapter. It emphasizes the capabilities of EAs, including the ability to explore large problem spaces, handle nonlinear and multimodal search spaces, and accommodate a wide range of objectives and constraints. Although these algorithms are capable of handling complex and nonlinear search spaces, they also face challenges such as computational complexity and premature convergence. In order to enhance their performance, researchers are focusing on hybridization, parameter tuning, and parallelization. These algorithms will remain important tools in optimization and machine learning as computational resources increase, with promising future prospects in a wide range of fields. © 2024 Elsevier Inc. All rights reserved.Öğe A comprehensive survey: Nature-inspired algorithms(Elsevier, 2024) Seyyedabbasi, AmirRecently, metaheuristic algorithms have become increasingly important. The purpose of this chapter is to provide readers with an overview of metaheuristic algorithms. This chapter provides an overview of the key elements of these metaheuristic algorithms including physics-based, evolution-based, and swarm-based algorithms and their evolutionary operators and functionalities. There have also been surveys examining these algorithms, but a comprehensive comparison and contrast study is lacking in current survey papers. As this chapter will introduce each algorithm individually, detailed introductions will be provided for each algorithm. There has been a great deal of effort devoted to this chapter to compare the metaheuristic algorithms that have been proposed in the last decade and, from among them, the most popular ones have been chosen for discussion in this chapter. Each algorithm has been evaluated according to the performance of well-known benchmark functions to determine its performance. As a result of this comparative study, we are aiming to provide a broader view of nature-inspired algorithms and meaningful insights into their design and implementation. The remaining of this section is: Nature-inspired algorithms. Physics-based algorithms. Evolution-based algorithms. Swarm-based algorithms. Multiobjective algorithms. Unconstrained/constrained nonlinear optimization. © 2024 Elsevier Inc. All rights reserved.Öğe A comprehensive survey: Swarm-based algorithms(Elsevier, 2024) Seyyedabbasi, AmirThe swarm-based algorithm is a type of algorithm inspired by natural phenomena. Swarm-based algorithms have been successfully used to solve many Np-hard optimization problems. Swarm-based algorithms have been found to be particularly effective for solving complex optimization problems. In addition to their ability to handle complex and nonlinear search spaces, these algorithms are also constrained by computational complexity and premature convergence. It should be noted, however, that swarm-based algorithms are not suitable for all optimization problems. Several effective strategies have been proposed in order to overcome this limitation, including the hybridization of other algorithms. In addition to its computational complexity, it may not always be the optimal solution to each problem, due to premature convergence and computational complexity. © 2024 Elsevier Inc. All rights reserved.Öğe 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) Kiani, Farzad; Seyyedabbasi, Amir; Aliyev, Royal; Gulle, Murat Ugur; Basyildiz, Hasan; Shah, Mohammad 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) Kiani, Farzad; Seyyedabbasi, Amir; Nematzadeh, Sajjad; Candan, Fuat; Çevik, Taner; Anka, Fateme Ayşin; Randazzo, Giovanni; Lanza, Stefania; Muzirafuti, AnselmeThe 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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Öğe An Effective Hybrid Metaheuristic Algorithm for Solving Global Optimization Algorithms(SpringerLink, 2024) Seyyedabbasi, Amir; Tareq Tareq, Wadhah Zeyad; Bacanin, NebojsaRecently, the Honey Badger Algorithm (HBA) was proposed as a metaheuristic algorithm. Honey badger hunting behaviour inspired the development of this algorithm. In the exploitation phase, HBA performs poorly and stagnates at the local best solution. On the other hand, the sand cat swarm optimization (SCSO) is a very competitive algorithm compared to other common metaheuristic algorithms since it has outstanding performance in the exploitation phase. Hence, the purpose of this paper is to hybridize HBA with SCSO so that the SCSO can overcome deficiencies of the HBA to improve the quality of the solution. The SCSO can effectively exploit optimal solutions. For the research conducted in this paper, a hybrid metaheuristic algorithm called HBASCSO was developed. The proposed approach was evaluated against challenging CEC benchmark instances taken from CEC2015, CEC2017, and CEC2019 benchmark suites The HBASCSO is also evaluated concerning the original HBA, SCSO, as well as several other recently proposed algorithms. To demonstrate that the proposed method performs significantly better than other competitive algorithms, 30 independent runs of each algorithm were evaluated to determine the best, worst, mean, and standard deviation of fitness functions. In addition, the Wilcoxon rank-sum test is used as a non-parametric comparison, and it has been found that the proposed algorithm outperforms other algorithms. Hence, the HBASCSO achieves an optimum solution that is better than the original algorithms.Öğe An optimized feature selection approach using sand Cat Swarm optimization for hyperspectral image classification(Elsevier, 2024) Hameed, Alaa Ali; Jamil, Akhtar; Seyyedabbasi, AmirIntegrating metaheuristic algorithms and optimization techniques with remote sensing technology has accelerated the advent of advanced methodologies for analyzing hyperspectral images (HSIs). These images, rich in detail across a broad spectral range, are pivotal for diverse applications. However, the high dimensionality of data poses challenges for obtaining optimal results therefore, a preprocessing step is necessary to reduce the dimensionality of the data to select the most effective features before the application of machine learning models. This study introduces a novel methodology that integrates Back Propagation (BP) and Variable Adaptive Momentum (BPVAM) with Sand Cat Swarm Optimization (SCSO) for the classification of hyperspectral images. Utilizing SCSO for the optimal feature selection followed by BPVAM generated more accurate classification maps. The fusion of the unique strengths of SCSO with the flexibility of BPVAM has significantly boosted the precision, efficiency, and adaptability of HSI classification. The effectiveness of our method is demonstrated using two benchmark hyperspectral datasets and validated through a comprehensive comparison with other benchmark optimization techniques, including Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Our findings indicate that our approach enhances classification accuracy that is comparable to the stateof-the-art methods in the domain of hyperspectral data analysis.Öğe Binary Sand Cat Swarm Optimization Algorithm for Wrapper Feature Selection on Biological Data(Mdpi, 2023) Seyyedabbasi, AmirIn large datasets, irrelevant, redundant, and noisy attributes are often present. These attributes can have a negative impact on the classification model accuracy. Therefore, feature selection is an effective pre-processing step intended to enhance the classification performance by choosing a small number of relevant or significant features. It is important to note that due to the NP-hard characteristics of feature selection, the search agent can become trapped in the local optima, which is extremely costly in terms of time and complexity. To solve these problems, an efficient and effective global search method is needed. Sand cat swarm optimization (SCSO) is a newly introduced metaheuristic algorithm that solves global optimization algorithms. Nevertheless, the SCSO algorithm is recommended for continuous problems. bSCSO is a binary version of the SCSO algorithm proposed here for the analysis and solution of discrete problems such as wrapper feature selection in biological data. It was evaluated on ten well-known biological datasets to determine the effectiveness of the bSCSO algorithm. Moreover, the proposed algorithm was compared to four recent binary optimization algorithms to determine which algorithm had better efficiency. A number of findings demonstrated the superiority of the proposed approach both in terms of high prediction accuracy and small feature sizes.Öğe Decision-Making Models: A Perspective of Fuzzy Logic and Machine Learning(Elsevier, 2024) Allahviranloo, Tofigh; Pedrycz, Witold; Seyyedabbasi, AmirDecision Making Models: A Perspective of Fuzzy Logic and Machine Learning presents the latest developments in the field of uncertain mathematics and decision science. The book aims to deliver a systematic exposure to soft computing techniques in fuzzy mathematics as well as artificial intelligence in the context of real-life problems and is designed to address recent techniques to solving uncertain problems encountered specifically in decision sciences. Researchers, professors, software engineers, and graduate students working in the fields of applied mathematics, software engineering, and artificial intelligence will find this book useful to acquire a solid foundation in fuzzy logic and fuzzy systems. Other areas of note include optimization problems and artificial intelligence practices, as well as how to analyze IoT solutions with applications and develop decision-making mechanisms realized under uncertainty. © 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies.Öğe Enhanced multi-strategy bottlenose dolphin optimizer for UAVs path planning(Elsevier Science Inc, 2024) Hu, Gang; Huang, Feiyang; Seyyedabbasi, Amir; Wei, GuoThe path planning of unmanned aerial vehicle is a complex practical optimization problem, which is an important part of unmanned aerial vehicle technology. For constrained path planning problem, the traditional path planning methods can not deal with the complex constraint conditions well, and the classical nature-inspired algorithms will find the local optimal solution due to the lack of optimization ability. In this paper, an enhanced multi-strategy bottlenose dolphin optimizer is proposed to solve the unmanned aerial vehicle path planning problem under threat environments. Firstly, the introduction of fish aggregating device strategy that simulates the living habits of sharks enriches the behavioral diversity of the population. Secondly, random mixed mutation strategy and chaotic opposition-based learning strategy expand the exploration range of the algorithm in the solution space by disturbing the positions of some individuals and generating the opposite population respectively. Finally, after balancing the exploration and exploitation ability of the algorithm more reasonably through the mutation factor and energy factor, this paper proposes a new swarm intelligence algorithm. After verifying the adaptability and efficiency of the proposed algorithm through different types of test functions, this paper further highlights the advantages of the proposed algorithm in finding the optimal feasible path in the unmanned aerial vehicle path planning model based on four constraints.Öğe Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems(Elsevier B.V., 2021) Seyyedabbasi, Amir; Aliyev, Royal; Kiani, Farzad; Gulle, Murat Ugur; Basyildiz, Hasan; Shah, Mohammad 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.Öğe Improving the performance of hierarchical wireless sensor networks using the metaheuristic algorithms: efficient cluster head selection(Emerald Group Holdings 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 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) Nematzadeh, Sajjad; Torkamanian-Afshar, Mahsa; Seyyedabbasi, Amir; Kiani, FarzadThe 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 independent of terrain. The proposed method is based on a metaheuristic algorithm and mimics the grey wolf optimizer (GWO) algorithm. In this study, we also suggested an enhanced version of the GWO algorithm to work adaptively in such problems and named it Mutant-GWO (MuGWO). Also, the suggested model ensures connectivity by generating topology graphs and potentially supports data transmission mechanisms. Therefore, the proposed method based on MuGWO can enhance resources utilization, such as reducing the number of nodes, by maximizing the coverage rate and maintaining the connectivity. While most studies assume classical rectangle uniform environments, this study also focuses on custom (environment-aware) maps in line with the importance and requirements of the real world. The motivation of supporting custom maps by this study is that environments can consist of custom shapes with prioritized and critical areas. In this way, environment awareness halts the deployment of nodes in undesired regions and averts resource waste. Besides, novel multi-purpose fitness functions of the proposed method satisfy a convenient approach to calculate costs instead of using complicated processes. Accordingly, this method is suitable for large-scale networks thanks to the capability of the distributed architecture and the metaheuristic-based approach. This study justifies the improvements in the suggested model by presenting comparisons with a Deterministic Grid-based approach and the Original GWO. Moreover, this method outperforms the fruit fly optimization algorithm, bat algorithm (BA), Optimized BA, harmony search, and improved dynamic deployment technique based on genetic algorithm methods in declared scenarios in literature, considering the results of simulations.Öğe Metaheuristic algorithms in IoT: optimized edge node localization(springer link, 2022) Kiani, Farzad; Seyyedabbasi, AmirIn 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 solution with high efficiency by using the advantage of both algorithms. At the same time, the shortcomings of each will be eliminated. The proposed algorithm is used to solve the edge computing node localization problem, which is one of the important problems on the Internet of Things (IoT) systems, with the least error rate. This algorithm has shown a successful performance in solving this problem with a smooth and efficient position update mechanism. It was also applied to 30 famous benchmark functions (CEC2015 and CEC2019) to prove the accuracy and general use of the proposed method. It has been proven from the results that it is the best algorithm with a success rate of 54% and 57%, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.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.Öğe Optimal data transmission and pathfinding for WSN and decentralized IoT systems using I-GWO and Ex-GWO algorithms(Elsevier B.V., 2023) Seyyedabbasi, Amir; Kiani, Farzad; Allahviranloo, Tofigh; Fernandez-Gamiz, Unai; Noeiaghdam, SamadEfficient 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 Non-deterministic Polynomial-time (NP-hard) problem type, metaheuristic algorithms can be used. This article proposes two new energy-efficient routing methods based on Incremental Grey Wolf Optimization (I-GWO) and Expanded Grey Wolf Optimization (Ex-GWO) algorithms to find optimal paths. Moreover, in this study, a general architecture has been proposed, making it possible for many different metaheuristic algorithms to work in an adaptive manner as well as these algorithms. In the proposed methods, a new fitness function is defined to determine the next hop based on some parameters such as residual energy, traffic, distance, buffer size and hop size. These parameters are important measurements in subsequent node selections. The main purpose of these methods is to minimize traffic, improve fault tolerance in related systems, and increase reliability and lifetime. The two metaheuristic algorithms mentioned above are used to find the best values ??for these parameters. The suggested methods find the best path of any length for the path between any source and destination node. In this study, no ready dataset was used, and the established network and system were run in the simulation environment. As a result, the optimal path has been discovered in terms of the minimum cost of the best paths obtained by the proposed methods. These methods can be very useful in decentralized peer-to-peer and distributed systems. The metrics for performance evaluation and comparisons are i) network lifetime, ii) the alive node ratio in the network, iii) the packet delivery ratio and lost data packets, iv) routing overhead, v) throughput, and vi) convergence behavior. According to the results, the proposed methods generally choose the most suitable and efficient ways with minimum cost. These methods are compared with Genetic Algorithm Based Routing (GAR), Artificial Bee Colony Based routing (ABCbased), Multi-Agent Protocol based on Ant Colony Optimization (MAP-ACO), and Wireless Sensor Networks based on Grey Wolf optimizer. (GWO-WSN) algorithms. The simulation results show that the proposed methods outperform the others.Öğe Optimal nodes localization in wireless sensor networks using nutcracker optimizer algorithms: Istanbul area(IEEE, 2024) Neggaz, Nabil; Seyyedabbasi, Amir; Hussien, Abdelazim G.; Rahim, Mekki; Beşkirli, MehmetNode localization is a non-deterministic polynomial time (NP-hard) problem in Wireless Sensor Networks (WSN). It involves determining the geographical position of each node in the network. For many applications in WSNs, such as environmental monitoring, security monitoring, health monitoring, and agriculture, precise location of nodes is crucial. As a result of this study, we propose a novel and efficient way to solve this problem without any regard to the environment, as well as without predetermined conditions. This proposed method is based on new proposed Nutcracker Optimization Algorithm (NOA). By utilizing this algorithm, it is possible to maximize coverage rates, decrease node numbers, and maintain connectivity. Several algorithms were used in this study, such as Grey Wolf Optimization (GWO), Kepler Optimization Algorithms (KOA), Harris Hawks Optimizer (HHO), Gradient-Based Optimizer (GBO) and Gazelle Optimization Algorithm (GOA). The node localization was first tested in Istanbul, Turkey, where it was determined to be a suitable study area. As a result of the metaheuristic-based approach and distributed architecture, the study is scalable to large-scale networks. Among these metaheuristic algorithms, NOA, KOA, and GWO have achieved significant performance in terms of coverage rates (CR), achieving coverage rates of 96.15%, 87.76%, and 93.49%, respectively. In terms of their ability to solve sensor node localization problems, these algorithms have proven to be effective.Öğe Optimizing the deep learning parameters using metaheuristic algorithms for PV-battery-based DC nanogrids(Taylor and francis ltd., 2024) Sulaiman, Mohd Herwan; Mustaffa, Zuriani; Seyyedabbasi, Amir; Irawan, AddieThis paper presents an implementation of metaheuristic algorithms to optimise deep learning neural networks (DNNs) for sensor-less control of photovoltaic (PV) Converters in DC nanogrids. Using a Fixed Forward Neural Network (FFNN), it estimates PV output current (IPV) based on three days of real data. Given the vulnerability of current sensors in DC system measurements, accurately replicating current sensor data is vital. The data exhibits dynamic nonlinear relationships with inputs like solar irradiance, temperature, and voltage. The study assesses the effectiveness of Evolutionary Mating Algorithm (EMA) and Sand Cat Swarm Optimisation (SCSO), comparing them with Adaptive Moment Estimation (ADAM), Genetic Algorithm (GA), and Particle Swarm Optimisation (PSO). The research leverages these metaheuristic algorithms to optimise machine learning integration, addressing regression estimation problems, and enhancing system reliability by eliminating sensors. Key findings indicate that the EMA-DNN combination achieved remarkable performance, with the lowest Mean Squared Error (MSE) of 0.5906, the lowest Mean Absolute Error (MAE) of 0.4680, and the lowest Mean Absolute Percentage Error (MAPE) of 12.1780%. These results offer valuable insights into how the metaheuristic-DNN approach can solve regression estimation problems and reduce the number of sensors in PV battery-based nanogrids’ control layers.
