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Obstacle avoidance and path planning of humanoid robot using fuzzy logic controller aided owl search algorithm in complicated workspaces

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INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS REsearch AND APPLICATION 2022年第2期49卷 280-288页

作者： Kashyap, Abhishek Kumar Parhi, Dayal R. Natl Inst Technol Rourkela Dept Mech Engn Rourkela India

Purpose This paper aims to outline and implement a novel hybrid controller in humanoid robots to map an optimal path. The hybrid controller is designed using the owl search algorithm (OSA) and Fuzzy logic. Design/methodology/approach The optimum steering angle (OS) is used to deal with the obstacle located in the workspace, which is the output of the hybrid OSA Fuzzy controller. It is obtained by feeding OSA's output, i.e. intermediate steering angle (IS), in fuzzy logic. It is obtained by supplying the distance of obstacles from all directions and target distance from the robot's present location. Findings The present research is based on the navigation of humanoid NAO in complicated workspaces. Therefore, various simulations are performed in a 3D simulator in different complicated workspaces. The validation of their outcomes is done using the various experiments in similar workspaces using the proposed controller. The comparison between their outcomes demonstrates an acceptable correlation. Ultimately, evaluating the proposed controller with another existing navigation approach indicates a significant improvement in performance. Originality/value A new framework is developed to guide humanoid NAO in complicated workspaces, which is hardly seen in the available literature. Inspection in simulation and experimental workspaces verifies the robustness of the designed navigational controller. Considering minimum error ranges and near collaboration, the findings from both frameworks are evaluated against each other in respect of specified navigational variables. Finally, concerning other present approaches, the designed controller is also examined, and major modifications in efficiency have been reported.

关键词： Path planning owl search algorithm Fuzzy logic controller WEBOT Humanoid NAO

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Efficient image segmentation through 2D histograms and an improved owl search algorithm

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2021年第1期12卷 131-150页

作者： del Rio, Andrea H. Aranguren, Itzel Oliva, Diego Abd Elaziz, Mohamed Cuevas, Erik Univ Guadalajara Dept Ingn CUCSUR Ave Independencia Nacl 151 Autlan De Navarro Jalisco Mexico Univ Guadalajara Div Elect & Computac CUCEI Ave Revoluc 1500 Guadalajara Jalisco Mexico Univ Oberta Catalunya Comp Sci Dept Castelldefels Spain Zagazig Univ Fac Sci Dept Math Zagazig Egypt

Optimization is used in different fields of engineering to solve complex problems. In image processing, multilevel thresholding requires to find the optimal configuration of thresholds to obtain accurate segmented images. In this case, the use of two-dimensional histograms is helpful because they permit us to combine information from the image preserving different features. This paper introduces a new method for multilevel image thresholding segmentation based on the improved version of the owl search algorithm (iOSA) and 2D histograms. The performance of the iOSA is enhanced with the inclusion of a new strategy in the optimization process. Moreover, in the initialization step, it is applied the opposition-based learning. Meanwhile, the 2D histograms permit to maintain more information of the image. Considering such modifications, the iOSA performs a better exploration of the search space during the early iterations, preserving the exploitation of the prominent regions using a self-adaptive variable. The iOSA is employed to allocate the optimal threshold values that segment the image by using the 2D Renyi entropy as an objective function. To test the efficiency of the iOSA, a set of experiments were performed which validate the quality of the segmentation and evaluate the optimization results efficacy. Moreover, to prove that the iOSA is a promising alternative for optimization and image processing problems, statistical tests and analyses were also conducted.

关键词： Metaheuristic algorithms owl search algorithm Image segmentation Multilevel thresholding 2D Histogram Renyi entropy

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Bone Cancer Detection and Classification Using owl search algorithm With Deep Learning on X-Ray Images

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IEEE ACCESS 2023年 11卷 109095-109103页

作者： Alabdulkreem, Eatedal Saeed, Muhammad Kashif Alotaibi, Saud S. Allafi, Randa Mohamed, Abdullah Hamza, Manar Ahmed Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11671 Saudi Arabia King Khalid Univ Appl Coll Dept Comp Sci Abha 62529 Saudi Arabia Umm Al Qura Univ Coll Comp & Informat Syst Dept Informat Syst Mecca 24382 Saudi Arabia Northern Border Univ Coll Sci & Arts Dept Comp & Informat Technol Ar Ar 73213 Saudi Arabia Future Univ Egypt Res Ctr New Cairo 11845 Egypt Prince Sattam Bin Abdulaziz Univ Dept Comp & Self Dev Preparatory Year Deanship Al Kharj 16278 Saudi Arabia

Bone cancer is treated as a severe health problem, and, in many cases, it causes patient death. Early detection of bone cancer is efficient in reducing the spread of malignant cells and decreasing mortality. Since the manual detection process is a laborious task, it is needed to design an automated system to classify and identify the cancerous bone and the healthy bone. Therefore, this article develops an owl search algorithm with a Deep Learning-Driven Bone Cancer Detection and Classification (OSADL-BCDC) technique. The OSADL-BCDC algorithm follows the principle of transfer learning with a hyperparameter tuning strategy for bone cancer detection. The OSADL-BCDC model employs Inception v3 as a pretrained model for the feature extraction process which does not necessitate a manual segmentation of X-ray images. Besides, the OSA is applied as a hyperparameter optimizer for enhancing the efficacy of the Inception v3 method. Finally, the long short-term memory (LSTM) approach is used for identifying the presence of bone cancer. The proposed OSADL-BCDC technique reduces diagnosis time and achieves faster convergence. The experimental analysis of the OSADL-BCDC algorithm is tested using a set of medical images and the outcomes were measured under different aspects. The comparison study highlighted the improved performance of the OSADL-BCDC model over existing algorithms.

关键词： Bones Cancer Tumors Feature extraction X-ray imaging Manuals Cancer detection Biomedical image processing Bone diseases Artificial intelligence owl search methods Medical imaging bone cancer deep learning artificial intelligence owl search algorithm

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A chaotic owl search algorithm based bilateral negotiation model

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AIN SHAMS ENGINEERING JOURNAL 2020年第4期11卷 1163-1178页

作者： El-Ashmawi, Walaa H. Abd Elminaam, Diaa Salama Nabil, Ayman M. Eldesouky, Esraa Suez Canal Univ Fac Comp & Informat Dept Comp Sci Ismalia City Egypt Benha Univ Fac Comp & Artificial Intelligence Dept Informat Syst Banha Egypt Misr Int Univ Dept Comp Sci Fac Comp Sci Cairo Egypt Prince Sattam Bin Abdulaziz Univ Dept Comp Sci Coll Comp Engn & Sci Al Kharj Saudi Arabia

Negotiation is a core activity between stakeholders with different goals. This paper offers a solution based on the automated bilateral negotiation model using a recent meta-heuristic algorithm owl search algorithm (OSA), and chaos theory. The proposed algorithm called Chaotic owl search algorithm (COSA). This algorithm is used to adapt negotiation strategies for computing negotiation offers throughout the negotiation process. For this aim, a negotiation grasped between two parties during several negotiation rounds. The results of the proposed algorithm compared with the standard OSA, PSO and the most common negotiation tactics. Different control parameters considered for accurate judgments of the suggested optimization techniques. The comparative study proved that the COSA provides accurate results over compared algorithms in terms of Average buyers' /seller's utility, Average negotiation rounds and Average processing time. This paper is the first of integrating chaos theory with the OSA in optimization problems and especially in the negotiation process. (c) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-ncnd/4.0/).

关键词： owl search algorithm Bilateral negotiation Chaos Meta-heuristic algorithms Logistic chaotic map Negotiation tactics

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A new optimized configuration for capacity and operation improvement of CCHP system based on developed owl search algorithm

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ENERGY REPORTS 2020年 6卷 315-324页

作者： Cao, Yan Wang, Qiangfeng Wang, Zhijie Jermsittiparsert, Kittisak Shafiee, Mohammadreza Xian Technol Univ Sch Mechatron Engn Xian 710021 Peoples R China Xian Technol Univ Shaanxi Key Lab Nontradit Machining Xian 710021 Peoples R China Chulalongkorn Univ Social Res Inst Bangkok 10330 Thailand Semnan Univ Semnan Iran

One of the best ways to optimally consuming fossil fuel thermal energy is to utilize combined cooling, heating, and power (CCHP) systems. In such systems, by recycling heat wasted from hot gases simultaneously generating power, heat, and refrigeration from combustion, as well as water and cooling oil in electricity production systems, the overall system energy utilization efficiency can be increased to more than 85%. Analysis and optimization of the CCHP systems are mostly in accordance to the operating cost reduction without considering the emissions reduction and actual energy utilization. The present study proposes an energy flow for CCHP system to decrease the main power consumption based on a building thermal demand in Kerman area, Iran. The method introduced a developed version of the owl search algorithm to increase the efficiency of the CCHP system in comparison with the separation production system. Final simulations declare well efficient results for the presented method. (C) 2020 The Authors. Published by Elsevier Ltd.

关键词： Combined cooling heating and power system owl search algorithm Improved Operation strategy Capacity

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Providing a guaranteed power for the BTS in telecom tower based on improved balanced owl search algorithm

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ENERGY REPORTS 2020年 6卷 297-307页

作者： Li, Daming Deng, Lianbing Su, Qinglang Song, Yonghua Zhuhai Da Hengqin Sci & Technol Dev Co Ltd Postdoctoral Res Ctr Zhuhai 519031 Peoples R China Guangdong Qinzhi Sci & Technol Res Inst Qinzhi 519031 Peoples R China City Univ Macau Inst Data Sci Taipa 999078 Macao Peoples R China Huazhong Univ Sci & Technol Wuhan 430074 Hubei Peoples R China Macao Univ Taipa 999078 Macao Peoples R China

The telecommunication industry is the technology of communication among humankind. One problem for this industry is to provide the required energy for its towers in the remote rural areas in the absence of the grid. This objection causes dependency of this industry to the diesel generators and battery banks as backup resources. Despite not being resilient and the effect of environment for the power supplies in diesel generators is still widespread. This paper presents a new optimized and environmentalist power supply configuration for the telecommunication tower based on the proton exchange membrane fuel cell (PEMFC). An optimized PI-based converter is also utilized to keep the source value reliable. For achieving optimized results for the PI controller, a developed version of the owl search algorithm is proposed. Simulation results have been applied in different conditions and the results are compared with Ziegler-Nichols, particle swarm optimization algorithm, and emperor penguins optimizer based PI controller to show the excellence of the proposed method. (C) 2020 The Authors. Published by Elsevier Ltd.

关键词： Telecom tower PEMFC owl search algorithm Improved Boost converter DC distribution Voltage regulation

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Chaotic-Nondominated-Sorting owl search algorithm for Energy-Aware Multi-Workflow Scheduling in Hybrid Clouds

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2022年第3期7卷 595-608页

作者： Li, Huifang Xu, Guanghao Wang, Danjing Zhou, MengChu Yuan, Yan Alabdulwahab, Ahmed Beijing Inst Technol State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA King Abdulaziz Univ Ctr Res Excellence Renewable Energy & Power Syst Jeddah 21481 Saudi Arabia King Abdulaziz Univ Dept Elect & Comp Engn Fac Engn Jeddah 21481 Saudi Arabia King Abdulaziz Univ Ctr Res Excellence Renewable Energy & Power Syst Jeddah 21481 Saudi Arabia

Since a single private cloud cannot satisfy the increasing computational requirements for executing multiple workflows simultaneously, hybrid clouds are often adopted to perform such execution. Multi-workflow scheduling is challenging as users may request various applications with different QoS requirements. This work proposes a Chaotic-nondominated-sorting owl search algorithm (COSA) by combining an owl search algorithm (OSA) with a Nondominated Sorting Genetic algorithm II (NSGA-II) to schedule resource-constrained multiple workflows in hybrid clouds with makespan, cost and energy consumption minimized under the given deadline and budget constraints. First, a hierarchical evolving mechanism is designed to update the better half and worse half of population by NSGA-II and OSA, respectively to guarantee a good trade-off between exploration and exploitation. Second, a chaotic sequence is introduced to adaptively adjust OSA's step size during population evolution for better exploration. Third, we adopt a chaotic operator for searching around the resulting Non-Dominated Solutions (NDS) to improve COSA's local search ability. Experiments are conducted to compare COSA with four peers and the results show its superiority in the number of obtained NDS, diversity preservation and convergence towards the near optimal Pareto set. In particular, it can find at least 19% more NDS than its peers.

关键词： Cloud computing Scheduling Processor scheduling Costs Task analysis Quality of service Particle swarm optimization Multi-workflow scheduling Hybrid clouds Energy consumption owl search algorithm Chaotic operator

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Developed owl search algorithm for parameter estimation of PEMFCs

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INTERNATIONAL JOURNAL OF AMBIENT ENERGY 2020年第1期43卷 3676-3685页

作者： Lai, Gousheng Li, Liyan Zeng, Qingyan Yousefi, Nasser Jiangxi Univ Engn Jian Jiangxi Peoples R China Islamic Azad Univ Karaj Branch Tehran Iran

In recent years, the application of renewable energy has been increasingly paid attention to keep environmental clear. One of the key components of this project is the fuel cell industry, which is considered as an environmentally friendly industry. They are, in fact, direct converters of chemical energy to electricity through an electrochemical reaction without the need for any mechanical parts. In this paper, an optimal methodology has been proposed for parameters estimation of a proton exchange membrane fuel cell (PEMFC). The main idea is to develop a modified model of owl search algorithm (DOSA) based on mutation and self-learning mechanisms to enhance the algorithm diversity and resolving its premature convergence. To analyse the efficiency of the method, it is verified by two experimental PEMFC case studies, NedStack PS6 and BCS 500-W and the results are compared by different metaheuristics. The results indicated the superiority of the proposed algorithm toward the others.

关键词： Parameter identification proton exchange membrane fuel cells owl search algorithm developed

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A New Approach for Feature Subset Selection using Quantum Inspired owl search algorithm 10

A New Approach for Feature Subset Selection using Quantum In...

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10th International Conference on Information Science and Technology (ICIST)

作者： Mandal, Ashis Kumar Sen, Rikta Goswami, Saptarsi Chakrabarti, Amlan Chakraborty, Basabi Iwate Prefectural Univ Grad Sch Soft & Info Sci Takizawa Iwate Japan Univ Calcutta Bangabasi Morning Coll Kolkata India Univ Calcutta Ak Choudhury Sch Informat Technol Kolkata India Iwate Prefectural Univ Fac Soft & Info Sci Takizawa Iwate Japan

ISBN: (纸本)9781728155586

Feature subset selection is the approach of selecting the optimal feature subset for the classification task by removing irrelevant and redundant features. However, searching for the optimal feature subset is challenging due to its inherent exponential time complexity. To address the problem, metaheuristics are frequently used for finding the sub -optimal solution in a reasonable time constraint. In this paper, a Quantum Inspired owl search algorithm (QIOSA) fir feature subset selection is proposed. In this method, features are represented as quantum superposition states, and quantum rotation gate is used to accelerate the search towards an optimal set of features. Simulation experiments have done to evaluate the efficiency of the proposed approach compared to Binary owl search algorithm (BOSA) proposed earlier by authors and other population-based feature selection techniques, including Binary Genetic algorithm (BGA) and Binary Particle Swarm Optimization (BPSO) with twelve publicly available benchmark datasets. The experimental results show that QIOSA improves classification accuracy and effectively reduces the number of features compared to other metaheuristic algorithms.

关键词： Feature subset selection metaheuristic algorithm owl search algorithm quantum-inspired search

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Binary owl search algorithm for Feature Subset Selection 10

Binary Owl Search Algorithm for Feature Subset Selection

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IEEE 10th International Conference on Awareness Science and Technology (iCAST)

作者： Mandal, Ashis Kumar Sen, Rikta Chakraborty, Basabi Iwate Prefectural Univ Grad Sch Soft & Info Sci Takizawa Iwate Japan Iwate Prefectural Univ Fac Soft & Info Sci Takizawa Iwate Japan

ISBN: (纸本)9781728138213

Feature subset selection is one of the essential preprocessing tasks for numerous classification problems. This is because good feature subset can reduce overfitting of data, enhance the accuracy, and lessen the training time of a classifier model. However, finding the optimum feature subset is computationally expensive when the number of features is relatively high. Therefore, stochastic approaches are often used in attaining good feature subset within a feasible time frame. In this paper, we propose a binary variant of recent stochastic optimization algorithm owl search Optimization (OSA) for optimum feature subset selection. In this approach, six different transfer functions of S-shaped and V-shaped families were employed for generating six different binary owl search Optimization (BOSA) models. The proposed mechanism then employed on eleven publicly available datasets and performances were compared with popular approaches including, Particle Swarm Optimization (PSO), Genetic algorithm (GA), and Harmony search (HC). Results reveal that, for most of the datasets, BOSA-based approaches can produce optimal feature subset with reduced number of features and improved classification accuracy compared to other approaches.

关键词： feature selection owl search algorithm stochastic method transfer functions

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