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Optimal probabilistic location of DGs using Monte Carlo simulation based different bio-inspired algorithms

AIN SHAMS ENGINEERING JOURNAL 2021年第3期12卷 2735-2762页

作者： Hemeida, Mahmoud G. Alkhalaf, Salem Senjyu, Tomonobu Ibrahim, Abdalla Ahmed, Mahrous Bahaa-Eldin, Ayman M. Minia Inst Engn Elect Engn Dept New Minia Egypt Qassim Univ Coll Sci & Arts Ar Rass Dept Comp Ar Rass Saudi Arabia Univ Ryukyus Fac Engn Dept Elect & Elect Engn 1 SenbaruNishihara Cho Nakagami Okinawa 9030213 Japan Aswan Univ Fac Engn Elect Engn Dept Aswan 81542 Egypt Taif Univ Coll Engn Dept Elect Engn POB 11099 At Taif 21944 Saudi Arabia Misr Int Univ Cairo Egypt

Stochastic nature of load demand has a great impact on the performance of electrical power system. As a result, planning of electrical power system considering load uncertainties became inevitable. This paper presents Monte Carlo simulation based different bio-inspired algorithms, grey wolf optimization (GWO), manta ray foraging optimization (MRFO), satin bower bird optimization (SBO) and whale optimization (WOA) to optimize locations of three DG units under load uncertainties considering 500 scenarios. Each scenario includes 50 iterations which means that for each run we have 25,000 iterations and 500 characteristics for different load value. Two objectives are achieved. Firstly, statistically finding the optimal probabilistic location of three DG units under load uncertainties in IEEE 33-bus and IEEE 69-bus radial distribution system based on Monte Carlo simulation integrated with different bio-inspired algorithms. Secondly, comparing between the performances of four different bio-inspired algorithms. Three objective functions are considered, minimizing active power loss, minimizing voltage deviation and maximizing voltage stability index. The active and reactive power demand are normally distributed using normal distribution function. The optimal probabilistic location is investigated considering two cases under load uncertainties, optimizing location of three DG units generally and optimizing location of one DG unit assuming two optimum locations for the other two units extracted from case I. The obtained results (after placing DG units) are compared to the base case (DG units are not connected) and compared to each other according to the optimization technique. The results show that, SBO algorithm superiors other algorithms almost in all cases. Comes next GWO which provide good results generally. However, the good performance obtained by MRFO, it consumes twice the time of other algorithms. WOA however fast convergence, it provides results worse than other algo

关键词： Optimal allocation Monte Carlo simulation bio-inspired algorithm Distributed generators GWO MRFO SBO WOA

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Graph-based robot optimal path planning with bio-inspired algorithms

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biomimetic Intelligence & Robotics 2023年第3期3卷 75-90页

作者： Tingjun Lei Timothy Sellers Chaomin Luo Daniel W.Carruth Zhuming Bi Department of Electrical and Computer Engineering Mississippi State UniversityMississippi State 39762USA Center for Advanced Vehicular Systems Mississippi State UniversityMississippi State 39762USA Department of Civil and Mechanical Engineering Purdue University Fort WayneFort Wayne 46805USA

Recently,bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based ***,these approaches endure performance degradation as problem complexity increases,often resulting in lengthy search times to find an optimal *** limitation is particularly critical for real-world applications like autonomous off-road vehicles,where highquality path computation is essential for energy *** address these challenges,this paper proposes a new graph-based optimal path planning approach that leverages a sort of bio-inspired algorithm,improved seagull optimization algorithm(iSOA)for rapid path planning of autonomous robots.A modified Douglas–Peucker(mDP)algorithm is developed to approximate irregular obstacles as polygonal obstacles based on the environment image in rough *** resulting mDPderived graph is then modeled using a Maklink graph *** applying the iSOA approach,the trajectory of an autonomous robot in the workspace is ***,a Bezier-curve-based smoothing approach is developed to generate safer and smoother trajectories while adhering to curvature *** proposed model is validated through simulated experiments undertaken in various real-world settings,and its performance is compared with state-of-the-art *** experimental results demonstrate that the proposed model outperforms existing approaches in terms of time cost and path length.

关键词： Autonomous robot Path planning bio-inspired algorithm Graph-based model Improved seagull optimization algorithm(iSOA)

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Wireless Sensor Networks Based on bio-inspired algorithms 18th

Wireless Sensor Networks Based on Bio-Inspired Algorithms

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18th International Conference on Computational Science and Its Applications (ICCSA)

作者： Lee, Meonghun Kim, Haengkon Yoe, Hyun NAAS Dept Agr Engn Jeonbuk 55365 South Korea Catholic Univ Daegu Sch Informat Technol Engn Gyeongbuk 38430 South Korea Sunchon Natl Univ Dept Informat & Commun Engn Jeonnam 57922 South Korea

ISBN: (纸本)9783319951621;9783319951614

The goal of bio-inspired is to resolve human problems by studying and mimicking the characteristics of organisms or design elements which can be found in nature. Wireless sensor networks are used in a variety of fields but have limited network lifespans, so various research is being performed on the subject. In particular, research is being performed on observing and modeling the behavioral principles of various organisms to use in bio-inspired algorithms for efficient routing techniques in large-scale networks. In this research, we studied the pheromones used in ant communication and designed the techniques for energy efficiency improvement and traffic distribution by applying them to the proposed network. We designed biomimicry technology called the Wireless Sensor Networks Based on bio-inspired algorithms, and by analyzing and applying the similarities between communication systems and biological systems, our system was able to show improved performance in terms of extended network lifespan, optimized path selection, etc. In simulation results, the proposed routing algorithm has a short information collection time and low energy consumption, and through this it is able to maximize network energy efficiency.

关键词： bio-inspired algorithm ACO algorithm WSN Pheromone

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Mining Top-K High Utility Itemset Using bio-inspired algorithms

Mining Top-K High Utility Itemset Using Bio-Inspired Algorit...

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2022 Workshop on Complexity in Engineering

作者： Nam Ngoc Pham Oplatkova, Zuzana Kominkova Huy Minh Huynh Bay Vo Tomas Bata Univ Fac Appl Informat Zlin Czech Republic HUTECH Univ Ho Chi Minh City Vietnam

ISBN: (数字)9781728171241

ISBN: (纸本)9781728171241

High utility itemset (HUI) mining is a necessary research problem in the field of knowledge discovery and data mining. Many algorithms for Top-K HUI mining have been proposed. However, the principal issue with these algorithms is that they need to store potential top-k patterns in the memory anytime, and they request the minimum utility threshold to automatically rise when finding HUIs. Consequently, the performance of existing exact algorithms for Top-K HUIs mining tends to decrease when the database size and the number of distinct items in the databases rise. To address this issue, we suggest a Binary Particle Swarm Optimization (BPSO) based algorithm for mining Top-K HUIs effectively, namely TKO-BPSO (Top-K high utility itemset mining in One phase based on Binary Particle Swarm Optimization). The main idea of TKO-BPSO is not only to use a one-phase model and strategy Raising the threshold by the Utility of Candidates (RUC) to effectively increase the border thresholds for pruning the search space but also to adopt the sigmoid function in the updating process of the particles. This might significantly reduce the combinational problem in traditional HUIM when the database size and the number of distinct items in the databases rise. Consequently, its performance outperforms existing exact algorithms for mining Top-K HUIs because it efficiently overcomes the problem of the vast amount candidates. Substantial experiments conducted on publicly available several real and synthetic 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 and memory usage.

关键词： bio-inspired algorithm top-k high utility itemset mining binary particle swarm optimization

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A Novel Approach for Texture Defect Detection using Statistical Features and a Rio-inspired algorithm

A Novel Approach for Texture Defect Detection using Statisti...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Sharma, Rahul S. Kuppili, Venkatanareshbabu Natl Inst Technol Goa Ponda Goa India

ISBN: (纸本)9798400701207

Texture defect detection is the process of identifying and locating defects in a texture-based product or material, such as textiles, paper, or metal surfaces. This paper presents a novel approach for texture defect detection using statistical features and a bio-inspired algorithm. The use of statistical features for the detection of texture defects has proven to be an effective approach in many applications. The features of images are extracted using GLCM and GLRLM of textures, and combined to form a unified feature set. The Bat algorithm (BAT) is then used for feature selection, in combination with a novel fitness function. The selected features are used to train a LGBM and MLPClassifier to classify the images as defective or non-defective. The proposed method was evaluated on a dataset of KolektorSDD2 images, which is a benchmark dataset for texture defect detection. Results demonstrate that the proposed method outperforms traditional feature selection methods and improves the overall accuracy of the defect detection system when LGBM classifier is used instead of MLPC. The proposed approach has potential applications in various texture-based industries and further research can be done to improve the method and test it on other datasets and applications.

关键词： statistical features bio-inspired algorithm feature selection optimization image classification

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Modeling and optimization of direct urea-hydrogen peroxide fuel cell using the integration of artificial neural network and bio-inspired algorithms

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JOURNAL OF ELECTROANALYTICAL CHEMISTRY 2022年第0期922卷 1页

作者： Thi Hoa Le Thakur, Deepika Phan Khanh Thinh Nguyen Gachon Univ Dept Chem & Biol Engn 1342 Seongnamdaero Seongnam Si 13120 Gyeonggi Do South Korea Seoul Natl Univ Dept Agr Forestry & Bioresources 1 Gwanak Ro Seoul 08826 South Korea

In this study, the performance of direct urea-hydrogen peroxide fuel cells (DUHPFCs) was modeled and optimized for the first time by artificial intelligence techniques. Accordingly, an artificial neural network (ANN) model was developed to describe the DUHPFC's voltage relying on basic designing and operating parameters (i.e., anode catalyst properties, urea concentration, KOH concentration, temperature, and feeding flow rate). A two-hidden layer-ANN with 7-10-6-1 topology using the Levenberg-Marquardt algorithm, logistic sigmoid function, and a training data proportion of 80 % was the best suitable model. A mean squared error (MSE) and R-value were estimated to be 0.51 x 10(-4) and 0.9993, respectively, indicating a good prediction capability. Subsequently, the bio-inspired algorithms (BIAs), including particle swarm optimization (PSO) and genetic algorithm (GA), were employed to identify the optimal process parameters. Similar optimum results were obtained by both algorithms, although ANN-PSO performed faster than ANN-GA. When Ni0.2Co0.8/Ni-foam was used as the anode catalyst, the calculated maximum power density was 45.6 mW/cm(2) under urea concentration of 1.4 M, KOH concentration of 6.2 M, temperature of 70 degrees C, and flow rate of 5.9 mL/min. However, the electrical energy recovery was only 2.6 % under such optimal conditions, suggesting that other factors, especially novel urea-electrooxidation catalysts, should be investigated further.

关键词： Direct urea-hydrogen peroxide fuel cell Artificial neural network Modeling Optimization bio-inspired algorithm

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Feature selection using competitive coevolution of bio-inspired algorithms for the diagnosis of pulmonary emphysema

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bioMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第PartB期72卷 103340-103340页

作者： Isaac, Anisha Nehemiah, H. Khanna Dunston, Snofy D. Christo, V. R. Elgin Kannan, A. Anna Univ Ramanujan Comp Ctr Chennai 600025 Tamil Nadu India Anna Univ Comp Sci & Engn Chennai 600025 Tamil Nadu India Anna Univ Dept Informat Sci & Technol Chennai 600025 Tamil Nadu India

A Computer-Aided Diagnosis (CAD) system to assist a radiologist for diagnosing pulmonary emphysema from chest Computed Tomography (CT) slices has been developed. The lung tissues are segmented from the chest CT slices using Spatial Fuzzy C-Means (SFCM) clustering algorithm and the Regions of Interest (ROIs) are extracted using pixel-based segmentation. The ROIs considered for this work are pulmonary emphysematous lesions namely, centrilobular emphysema, paraseptal emphysema and sub-pleural bullae. The extracted ROIs are then validated and labelled by an expert radiologist. From each ROI, features with respect to shape, texture and runlength are extracted. A competitive coevolution model is proposed for Feature Selection (FS). The model makes use of two bio-inspired algorithms namely, Spider Monkey Optimization (SMO) algorithm and Paddy Field algorithm (PFA) as its building blocks. FS is performed as a wrapper approach, using the bio-inspired algorithms namely, SMO and PFA with the accuracy of the Support Vector Machine (SVM) classifier as the fitness function. Ten-fold cross validation technique is used in training the SVM classifier using the selected features. The model is tested using two datasets: Real-time emphysema dataset and CT emphysema database (CTED) dataset. The accuracy, precision, recall and specificity obtained for both the datasets are (81.95%, 93.74%), (72.92%, 90.61%), (72.92%, 90.61%), (86.46%, 95.3%), which are better compared to the performance of SMO and PFA algorithms applied individually for FS and the CAD system without performing FS.

关键词： Competitive coevolution Segmentation Spatial fuzzy C-means clustering bio-inspired algorithm Spider monkey optimization Paddy field Feature selection

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HFTO: Hybrid Firebug Tunicate Optimizer for Fault Tolerance and Dynamic Task Scheduling in Cloud Computing

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WIRELESS PERSONAL COMMUNICATIONS 2023年第1期129卷 323-344页

作者： Nanjappan, Manikandan Natesan, Gobalakrishnan Krishnadoss, Pradeep SRM Inst Sci & Technol Coll Engn & Technol Fac Engn & Technol Sch Comp Chennai Tamilnadu India Sri Venkateswara Coll Engn Dept Informat Technol Chennai Tamilnadu India Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamilnadu India

Task scheduling is an important issue in cloud computing when it comes to achieving multiple goals and satisfying different user needs. The increasing demand and users urge the necessity to minimize the task completion time and enhance the load balancing capacity. To achieve this goal, this article proposes a Hybrid firebug and Tunicate Optimization (HFTO) algorithm. Based on the previous scheduling information, the HFTO classifier classifies the task and creates different variants of Virtual Machine (VM). This step helps to minimize the time taken for VM creation. The proposed HFTO task scheduling framework aims at optimizing different Quality of Service (QoS) parameters such as fault tolerance, response time, efficiency, and makespan. The optimization algorithm helps to expand the search space of the solutions and frames an optimal task scheduling strategy for the virtual machines. The HFTO optimization method has several advantages, including enhanced search capability and faster convergence. The HFTO algorithm improves the fault tolerance capability by allocating the tasks to appropriate resources based on the resource load peak. The lightweight tasks can be allocated to the resources with high CPU utilization and the computation-intensive tasks can be allocated to the resources with low CPU utilization. The response time and execution time are improved by task pre-emption. Hence the time complexity and computational complexity can be improved by the HFTO algorithm even with limited resource capability. The experiments are conducted using the CloudSim experimental platform and the results are compared to the state-of-art techniques. The performance of the proposed methodology is evaluated in terms of different performance metrics namely makespan, load balancing, and average execution time. The results show that, when compared to existing techniques, the proposed methodology provides higher load balancing efficiency and improved cloud task scheduling performance.

关键词： Cloud computing Dynamic scheduling bio-inspired algorithm Optimal scheduling Load balancing Firebug optimization

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An orthogonal-design hybrid particle swarm optimiser with application to capacitated facility location problem

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INTERNATIONAL JOURNAL OF bio-inspired COMPUTATION 2016年第5期8卷 268-285页

作者： Chu, Xianghua Niu, Ben Liang, J. J. Lu, Qiang Harbin Inst Technol Shenzhen Grad Sch Shenzhen 518055 Guangdong Peoples R China Shenzhen Univ Coll Management Shenzhen 518060 Guangdong Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Henan Peoples R China

To improve the performance of particle swarm optimiser (PSO) for global optimisation, a variant called orthogonal-design hybrid particle swarm optimiser (OHPSO) is presented in this paper. A permutation strategy based on orthogonal experimental design is developed as a metabolic mechanism to enhance population diversity. In addition, a hybrid learning strategy is proposed to exploit the particles' best experiences and direct the individuals more efficiently. OHPSO is tested on a set of 18 benchmark functions with various properties, and nine state-of-the-art PSO variants are adopted for comparison. Experimental results and statistical analyses indicate a significant improvement of the proposed algorithm. Furthermore, OHPSO is applied to a practical engineering problem, the capacitated facility location problem, to justify its real-world performance and applicability. The experiment results are highly competitive with existing bio-inspired algorithms in the location optimisation.

关键词： particle swarm optimiser PSO global optimisation orthogonal experimental design hybrid learning capacitated facility location problem population diversity bio-inspired algorithm

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An Accelerated Physarum Solver for Network Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2020年第2期50卷 765-776页

作者： Gao, Cai Zhang, Xiaoge Yue, Zhiying Wei, Daijun SUNY Buffalo Dept Ind & Syst Engn Buffalo NY 14228 USA Vanderbilt Univ Dept Civil & Environm Engn Sch Engn Nashville TN 37235 USA SUNY Buffalo Dept Commun Buffalo NY 14228 USA Hubei Univ Nationalitis Sch Sci Enshi 445000 Peoples R China

As a novel computational paradigm, Physarum solver has received increasing attention from the researchers in tackling a plethora of network optimization problems. However, the convergence of Physarum solver is grounded by solving a system of linear equations iteratively, which often leads to low computational performance. Two factors have been highlighted along the process: 1) high time complexity in solving the system of linear equations and 2) extensive iterations required for convergence. Thus, Physarum solver has been largely restricted by its unsatisfactory computational performance. In this paper, we aim to address these two issues by developing two enhancement strategies: 1) pruning inactive nodes and 2) terminating Physarum solver in advance. First, extensive nodes and edges become and stay inactive after a few iterations in identifying the shortest path. Removing these inactive nodes and edges significantly decreases the graph size, thereby reducing computational complexity. Second, we define a transition phase for edges. All of the paths experiencing such a transition phase are dynamically aggregated to form a set of near-optimal paths among which the optimal path is included. Depth-first search is then leveraged to identify the optimal path from the near-optimal paths set. Earlier termination of Physarum solver saves considerable iterations while guaranteeing the optimality of the found solution. Empirically, 20 randomly generated sparse and complete graphs with network sizes ranging from 50 to 2000 as well as two real-world traffic networks are used to compare the performance of accelerated Physarum solver to the other two state-of-the-art algorithms.

关键词： bio-inspired algorithm network optimization Physarum solver shortest path problem

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