The clean and abundant nature of photovoltaic technology makes it eminent among other renewable energy sources and to obtain the best benefit from these sources, an efficient maximum power point tracking technique is ...
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The clean and abundant nature of photovoltaic technology makes it eminent among other renewable energy sources and to obtain the best benefit from these sources, an efficient maximum power point tracking technique is needed that can produce the required output even under varying environmental conditions. This work deals with the development of a global maximum power point tracking technique combining bio-inspired algorithms and one-cycle control which helps in effective tracking even under partial shading conditions. This technique generates signals for the KY converter from the duty cycle obtained from bio-inspired algorithms. The voltage at the output of the photovoltaic panel is fed to the load through KY converter. The analysis of the system is carried out using resistive load under different patterns of the photovoltaic array using particle swarm optimization, flower pollination and flying squirrel search optimization algorithms through simulation and experimentation. The performance indices like tracking speed, tracking efficiency and steady-state oscillations are taken for comparison with the existing systems without one-cycle control, and the results indicate its capability in GMPP tracking with an average efficiency and tracking time of 99.1% and 0.13 s, respectively.
In the contemporary digital landscape, the pervasive and far-reaching impact of online social networks is indisputable. Prominent platforms such as Instagram, Facebook, and Twitter frequently grapple with the persiste...
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ISBN:
(数字)9783031599330
ISBN:
(纸本)9783031599323;9783031599330
In the contemporary digital landscape, the pervasive and far-reaching impact of online social networks is indisputable. Prominent platforms such as Instagram, Facebook, and Twitter frequently grapple with the persistent challenge of distinguishing between registered profiles and genuinely engaged users, resulting in a noticeable surge in the prevalence of counterfeit or dormant accounts. This situation underscores the compelling necessity to accurately differentiate between authentic and misleading user profiles. The primary objective of this investigation is to introduce an innovative approach to profile validation. This unique method astutely leverages state-of-the-art bio-inspired algorithms while circumventing traditional machine learning techniques. The empirical results are notably convincing, consistently achieving a high level of accuracy in classification tests conducted on the provided datasets.
High average utility pattern (itemset) Mining (HAUIM) is a necessary research problem in the field of knowledge discovery and data mining. Several algorithms have been proposed to mine high average-utility itemsets (H...
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ISBN:
(纸本)9781450387132
High average utility pattern (itemset) Mining (HAUIM) is a necessary research problem in the field of knowledge discovery and data mining. Several algorithms have been proposed to mine high average-utility itemsets (HAUIs). Nonetheless, the large search space leads to poor performance because of excessive execution time and memory usage. To handle this limitation, particle swarm optimization (PSO) is applied to mine HAUIs. In this paper, an effective Binary PSO-based algorithm namely HAUIM-BPSO is proposed to explore HAUI efficiently. In general, HAUIM-BPSO first sets the number of discovered potential high average-utility 1-itemsets (1-PHAUIs) as the size of a particle based on average utility upper bound (AUUB) property. The sigmoid function is also used in the updating process of the individual of the proposed HAUIM-BPSO algorithm. Substantial experiments conducted on publicly available datasets show that the proposed algorithm has better results than existing state-of-the-art algorithms in terms of runtime which can significantly reduce the combinational problem, memory usage, and convergence speed.
This paper presents a novel bio-inspired algorithminspired by starlings' behaviors during their stunning murmuration named starling murmuration optimizer (SMO) to solve complex and engineering optimization proble...
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This paper presents a novel bio-inspired algorithminspired by starlings' behaviors during their stunning murmuration named starling murmuration optimizer (SMO) to solve complex and engineering optimization problems as the most appropriate application of metaheuristic algorithms. The SMO introduces a dynamic multi-flock construction and three new search strategies, separating, diving, and whirling. The separating search strategy aims to enhance the population diversity and local optima avoidance using a new separating operator based on the quantum harmonic oscillator. The diving search strategy aims to explore the search space sufficiently by a new quantum random dive operator, whereas the whirling search strategy exploits the vicinity of promising regions using a new operator called cohesion force. The SMO strikes a balance between exploration and exploitation by selecting either a diving strategy or a whirling strategy based on the flocks' quality. The SMO was tested using various benchmark functions with dimensions 30, 50, 100. The experimental results prove that the SMO is more competitive than other state-of-the-art algorithms regarding solution quality and convergence rate. Then, the SMO is applied to solve several mechanical engineering problems in which results demonstrate that it can provide more accurate solutions. A statistical analysis shows that SMO is superior to the other contenders. (c) 2022 Elsevier B.V. All rights reserved.
The shortage of water resources has always been one of the difficult problems that plagued world economic development and social stability. The imbalance and uneven distribution of water resources between regions exac...
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The shortage of water resources has always been one of the difficult problems that plagued world economic development and social stability. The imbalance and uneven distribution of water resources between regions exacerbate this contradiction. Therefore, the optimal economical and reasonable water resource design method has become the hope for solving the problem of water resource shortage. As a classic resource allocation and scheduling problem, the rural postman problem has been widely used in practice. The problem includes finding the minimum cost tour after traversing all edges in the required edge subset at least once in an undirected graph, but so far, there is no effective algorithm to solve this problem. Based on bio-heuristic computing model and deoxyribonucleic acid (DNA) molecular operations, a parallel bio-computing algorithm for rural postman problem is proposed in the paper. We use a DNA algorithm to solve the rural postman problem with n vertices in O(n(2)) time complexity. Then, we prove the feasibility of the algorithm in theory and verify the authenticity of the algorithm in the simulation experiment. Compared with previous algorithms, DNA computing algorithm not only has higher computational efficiency and lower error rate but also has huge storage capacity and parallel computing ability, which makes the algorithm has better applicability in dealing with large-scale problems.
High average utility pattern (itemset) Mining (HAUIM) is a necessary research problem in the field of knowledge discovery and data mining. Several algorithms have been proposed to mine high average-utility itemsets (H...
详细信息
ISBN:
(纸本)9781450387132
High average utility pattern (itemset) Mining (HAUIM) is a necessary research problem in the field of knowledge discovery and data mining. Several algorithms have been proposed to mine high average-utility itemsets (HAUIs). Nonetheless, the large search space leads to poor performance because of excessive execution time and memory usage. To handle this limitation, particle swarm optimization (PSO) is applied to mine HAUIs. In this paper, an effective Binary PSO-based algorithm namely HAUIM-BPSO is proposed to explore HAUI efficiently. In general, HAUIM-BPSO first sets the number of discovered potential high average-utility 1-itemsets (1-PHAUIs) as the size of a particle based on average utility upper bound (AUUB) property. The sigmoid function is also used in the updating process of the individual of the proposed HAUIM-BPSO algorithm. Substantial experiments conducted on publicly available datasets show that the proposed algorithm has better results than existing state-of-the-art algorithms in terms of runtime which can significantly reduce the combinational problem, memory usage, and convergence speed.
Information-Centric Networking (ICN) is a paradigm shift for Future Internet Architecture to eliminate issues in current implemented network. To date mobile communication still bring challenges due to diversity of net...
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ISBN:
(纸本)9781728120942
Information-Centric Networking (ICN) is a paradigm shift for Future Internet Architecture to eliminate issues in current implemented network. To date mobile communication still bring challenges due to diversity of network access characteristics. To achieve high Quality of Service (QoS), an intelligent services and mechanism is primly essential. Hence, the problem with ICN is articulated as an optimization problem to sustain the desired Quality of Service (QoS). These requirements motivate this study to improve the mobile QoS by hybridization of bio-inspired algorithm in the communication architecture. The proposed intelligent algorithm comprised of mutation, migration and crossover stages. For this, a cost function is designed and adopted in the selected bio-inspired algorithms to evaluate their performance in QoS optimization process. The statistical findings display the superiority of bioGeography Based Optimization scheme in comparison to the other algorithms.
In medical image processing, an accurate segmentation and classification are very important and this field still needs an effective computer-based algorithm for accomplishing the task. In gallbladder segmentation, onl...
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In medical image processing, an accurate segmentation and classification are very important and this field still needs an effective computer-based algorithm for accomplishing the task. In gallbladder segmentation, only few automatic segmentation methodologies have been presented. The accuracy is very important in medical image processing for accurate diagnosis of the disease. In this paper, by exploiting the basic web building behavior of spiders, we developed a bio-inspired algorithm based on the spider web construction process for image segmentation process in medical images. The aim of this paper is to detect the shape of the gallbladder and to segment the gallstones and polyps located inside the gallbladder using a computer-based algorithm. It is necessary to apply a suitable preprocessing method in order to eliminate the irregularities presenting in the ultrasound scan images. In the preprocessing stage, histogram equalization and DooG filter are applied to enhance the contrast of the image. After that, the proposed spider web algorithm is applied in the segmentation process. The performance metrics of the proposed method are evaluated by implementing the proposed method to test the input dataset of 60 patients and it is compared with the results obtained from conventional segmentation methods. The values of DSC, OF, OV and PE for images with no lesions are 0.873167, 0.8389, 0.81705 and 0.81452.
A novel bio-inspired optimization algorithm is proposed in this paper namely barnacles mating optimizer (BMO) algorithm. The main inspiration of BMO is originated from the mating behavior of barnacles in nature. Barna...
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ISBN:
(纸本)9781538658895
A novel bio-inspired optimization algorithm is proposed in this paper namely barnacles mating optimizer (BMO) algorithm. The main inspiration of BMO is originated from the mating behavior of barnacles in nature. Barnacles are hermaphroditic micro-organisms which have both male and female sex reproductions. To create new off-springs, they must be fertilized by a neighbor. They are well-known for their long penises, about seven times the length of their bodies to cope with the changing tides and sedentary lifestyle. In BMO, the selection of barnacle's parents is decided randomly by the length of barnacle's penis to create new off-springs. The exploitation and exploration processes are the generation of new off-springs inspired by the Hardy- Weinberg principle and sperm cast situation, respectively. The effectiveness of proposed BMO is tested through a set of benchmark multi-dimensional functions which the global and local minimum are known. Comparisons with other recent algorithms also will be presented in this paper.
An essential activity to obtain valuable information to identify, for example, intrusions, faults, system failures, etc, is outliers detection. This paper proposes a bio-inspired algorithm able to detect anomaly data ...
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An essential activity to obtain valuable information to identify, for example, intrusions, faults, system failures, etc, is outliers detection. This paper proposes a bio-inspired algorithm able to detect anomaly data in distributed systems. Each data object is associated with a mobile agent that follows the well-known bio-inspired algorithm of flocking. The agents are randomly disseminated onto a virtual space where they move autonomously in order to form one or more flocks. Through a tailored similarity function, the agents associated with similar objects join in the same flock, whereas, the agents associated with dissimilar objects do not join in any flock. The objects associated with isolated agents or associated with agents grouped into flock with a number of entities lower than a given threshold, represent the outliers. Experimental results on synthetic and real data sets confirm the validity of the approach.
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