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Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第8期80卷 12435-12468页

作者： Abd Elaziz, Mohamed Nabil, Neggaz Moghdani, Reza Ewees, Ahmed A. Cuevas, Erik Lu, Songfeng Zagazig Univ Dept Math Fac Sci Zagazig Egypt Univ Sci & Technol Oran Mohammed Boudiaf USTO MB Fac Math & Informat Dept InformatLab SIMPA BP 1505 El Mnaouer 31000 Oran Algeria Persian Gulf Univ Ind Management Dept Boushehr Iran Damietta Univ Dept Comp Dumyat Egypt Univ Guadalajara CUCEI Dept Elect Av Revoluc 1500 Guadalajara 44430 Jalisco Mexico Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan 430074 Peoples R China

Multilevel thresholding image segmentation has received considerable attention in several image processing applications. However, the process of determining the optimal threshold values (as the preprocessing step) is time-consuming when traditional methods are used. Although these limitations can be addressed by applying metaheuristic methods, such approaches may be idle with a local solution. This study proposed an alternative multilevel thresholding image segmentation method called VPLWOA, which is an improved version of the volleyball premier league (VPL) algorithm using the whale optimization algorithm (WOA). In VPLWOA, the WOA is used as a local search system to improve the learning phase of the VPL algorithm. A set of experimental series is performed using two different image datasets to assess the performance of the VPLWOA in determining the values that may be optimal threshold, and the performance of this algorithm is compared with other approaches. Experimental results show that the proposed VPLWOA outperforms the other approaches in terms of several performance measures, such as signal-to-noise ratio and structural similarity index.

关键词： Image segmentation Multilevel thresholding swarm algorithm Volleyball premier league algorithm Whale optimization algorithm

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Efficient cooperative search of smart targets using UAV swarms

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ROBOTICA 2008年第4期26卷 551-557页

作者： Altshuler, Yaniv Yanovsky, Vladimir Wagner, Israel A. Bruckstein, Alfred M. Technion Dept Comp Sci IL-32000 Haifa Israel MATAM IBM Haifa Labs IL-31905 Haifa Israel

This work examines the Cooperative Hunters problem, where a swarm of unmanned air vehicles (UAVs) is used for searching one or more "evading targets," which are moving in a predefined area while trying to avoid a detection by the swarm. By arranging themselves into efficient geometric flight Configurations, the UAVs optimize their integrated sensing capabilities, enabling the search of a maximal territory.

关键词： swarm algorithm cooperative search shape factor

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A novel algorithm for image segmentation (IP-MH-MLT): employing an image partitioning technique with metaheuristic parameters to enhance multilevel thresholding

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2024年第9期15卷 4291-4347页

作者： Thapliyal, Shivankur Kumar, Narender Doon Univ Dept Comp Sci Dehra Dun Uttarakhand India

The multilevel threshold technique of image segmentation is a popular and intriguing domain in the field of image vision and has received a lot of attention in several image processing applications due to its use in numerous image applications for assisting with a variety of problems. The key issue in this domain is figuring out the optimal number of thresholds and their values due to the limitations of conventional algorithms, such as fixed threshold values, a lack of adaptability, manual parameter setting, and a lack of contextual information. In order to deal with this problem, a new multilevel thresholding (MLT) algorithm (IP-MH-MLT) has been proposed in this paper. It is based on the image partitioning (IP) approach and has a few parameters that are computed using any metaheuristic (MH) technique, and the remaining parameters are evaluated through image characteristics. In this paper, for the metaheuristic parameter, a swarm-based metaheuristic called Grey Wolf Optimizer (GWO) is taken into consideration due to its numerous features, such as simplicity and ease of implementation, efficient convergence speed, and limited computational complexity. The performance of the proposed algorithm has been validated on a set of fifteen benchmark images using various thresholds and compared quantitatively in terms of a number of performance metrics, including structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR), with eight different metaheuristic algorithms. In order to examine the proposed algorithm qualitatively, Friedman ranking tests are carried out on the proposed algorithm over other comparable algorithms. The results demonstrate the effectiveness and competitive performance of the proposed algorithm.

关键词： Image segmentation Multilevel thresholding Metaheuristics Grey wolf optimizer swarm algorithm Image processing

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Improved Equilibrium Optimizer for Accurate Training of Feedforward Neural Networks

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OPTICAL MEMORY AND NEURAL NETWORKS 2024年第2期33卷 133-143页

作者： Mohammadi, Seyed Sina Salehirad, Mohammadreza Emamzadeh, Mohammad Mollaie Yazdi, Mojtaba Barkhordari Shahid Bahonar Univ Kerman Dept Elect Engn Kerman 76169 Iran

One of the most demanding applications of accurate Artificial Neural Networks (ANN) can be found in medical fields, mainly to make critical decisions. To achieve this goal, an efficient optimization and training method is required to tune the parameters of ANN and to reach the global solutions of these parameters. Equilibrium Optimizer (EO) has recently been introduced to solve optimization problems more reliably than other optimization methods which have the ability to escape from the local optima solutions and to reach the global optimum solution. In this paper, to achieve a higher performance, some modifications are applied to the EO algorithm and the Improved Equilibrium Optimizer (IEO) method is presented which have enough accuracy and reliability to be used in crucial and accurate medical applications. Then, this IEO approach is utilized to learn ANN, and IEO-ANN algorithm will be introduced. The proposed IEO-ANN will be implemented to solve real-world medical problems such as breast cancer detection and heart failure prediction. The obtained results of IEO are compared with those of three other well-known approaches: EO, Particle swarm Optimizer (PSO), Salp swarm Optimizer (SSO), and Back Propagation (BP). The recorded results have shown that the proposed IEO algorithm has much higher prediction accuracy than others. Therefore, the presented IEO can improve the accuracy and convergence rate of tuning neural networks, so that the proposed IEO-ANN is a suitable classifying and predicting approach for crucial medical decisions where high accuracy is needed.

关键词： training neural network improved equilibrium optimizer swarm algorithm prediction accuracy medical applications

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TUNING OF THE BISON algorithm CONTROL PARAMETERS 32

TUNING OF THE BISON ALGORITHM CONTROL PARAMETERS

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32nd European Conference on Modelling and Simulation (ECMS)

作者： Kazikova, Anezka Pluhacek, Michal Senkerik, Roman Tomas Bata Univ Zlin Fac Appl Informat TG Masaryka 5555 Zlin 76001 Czech Republic

ISBN: (纸本)9780993244070

This paper studies the dependency of the Bison algorithm performance on the control parameter configuration. The Bison algorithm is a new swarm algorithm based on the protection mechanisms of bison herds. It operates with two groups: the exploiting swarming group and the exploring running group. Even though that adjusting the group size parameters affects both the time requirements and the performance of the algorithm, there was no investigation of the parameter settings carried out yet. This paper describes the Bison algorithm and then investigates the control parameters for a better understanding of their meaning and influence on the overall optimization process.

关键词： Bison algorithm parameter study swarm algorithm optimization metaheuristic

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A swarm-based multiple reduction approach for fault diagnosis

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2013年第3期18卷 261-267页

作者： Zhao, Fengqiang Liu, Hongbo Dalian Maritime Univ Sch Informat Sci & Technol Dalian 116026 Peoples R China Dalian Nationalities Univ Coll Electromech & Informat Engn Dalian 116600 Peoples R China

Fault diagnosis is a complex and difficult problem in the equipment maintenance. Whenever a fault symptom is detected, the system diagnosis is expected to carry out timely. It would be very helpful to improve the overall productivity. This paper presents a class of fault diagnosis problem, in which many items could be chosen in the diagnosis environment. Some of the data come from their experience or estimation. The information is redundant and inaccurate. swarm-based rough set approach is introduced to make an attempt to solve the problem. Rough set theory provides a mathematical tool that can be used for both feature selection and information reduction. The swarm-based reduction approaches are attractive to find multiple reducts in the decision systems, which could be applied to generate multiple fault diagnosis planning and to improve diagnosis the decision. Empirical results illustrate that the approach can be applied for the class of fault diagnosis problems effectively.

关键词： swarm algorithm swarm intelligence rough set fault diagnosis equipment maintenance

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COLLABORATIVE EXPLORATION IN GRID DOMAINS Constructive Conjecture of a Polynomial Time Complexity

COLLABORATIVE EXPLORATION IN GRID DOMAINS <i>Constructive Co...

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6th International Conference on Informatics in Control, Automation and Robotics

作者： Altshuler, Yaniv Bruckstein, Alfred M. Wagner, Israel A. Ben Gurion Univ Negev Deutsch Telekom Labs IL-84105 Beer Sheva Israel Technion Israel Inst Technol Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel IBM Haifa Res Labs Haifa Israel

ISBN: (纸本)9789898111999

This work discusses the problem of exploration of an unknown environment using a collaborative group of simple agents. While this problem was known to be of a non-polynomial time complexity, it was speculated in the past that in grid domains the completion time of this problem is much lower (although analytic proofs were not available hitherto). In this work we present a preliminary result concerning a constructive analytic constraint for guaranteeing that the time complexity of this problem in grid domains is indeed polynomial.

关键词： swarm algorithm Decentralized search Cooperative exploration Grid exploration Time complexity

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Peacock algorithm

Peacock Algorithm

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Chaudhary, Reshu Banati, Hema Univ Delhi Dept Comp Sci Delhi India Univ Delhi Dyal Singh Coll Delhi India

ISBN: (纸本)9781728121536

The paper proposes a novel nature-inspired swarm algorithm: peacock algorithm (PA). It is based on the lekking and mating behavior of peacocks. The best solutions are identified as peacocks, while the remaining solutions are called peahens. The peahens are assigned to the peacocks based on their relative positions. Peacocks perform exploitation while peahens perform both, exploration as well as exploitation. A peahen explores the search space and moves towards the lek peacock. Once it is near, it may to choose to mate (exploitation) or pursue another peacock (exploration). Every peacock moves towards the dominant peacock (best solution) to enhance its mating chances. The dominant peacock maintains his lek by pushing away other peacocks if they encroach upon its lekking area. The algorithm maintains diversity by adding random solutions whenever two solutions are found to be similar. To test the efficiency of the proposed work, it is compared over a set of 23 benchmark functions to nine other algorithms, which are a mixture of well-established and new algorithms. Its performance is also compared over the CEC'14 test suite to six algorithms. Experimental results demonstrate the superiority of PA, establishing it as a promising novel algorithm.

关键词： Peacock algorithm swarm algorithm nature-inspired algorithm global optimization numerical optimization

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Comparative Analysis of Double-Tuned Harmonic Passive Filter Design Methodologies Using Slime Mould Optimization algorithm

Comparative Analysis of Double-Tuned Harmonic Passive Filter...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Zobaa, Ahmed M. Aleem, Shady H. E. Abdel Youssef, Hosam K. M. Cairo Univ Fac Engn Elect Power Engn Dept Giza Egypt 15th May Higher Inst Engn Math Phys & Engn Sci Cairo Egypt

ISBN: (纸本)9781728186122

The increasing use of nonlinear loads in power systems generates harmonics, which adversely affects these systems' power quality performance. In this regard, double-tuned filters (DTFs) have received increasing attention in practice to mitigate the effects of power system harmonics. Different techniques have been presented in the literature to obtain the optimal design of DTF parameters using various optimization algorithms. In this work, three methods for designing DTFs - multi-arm method (MAM), direct design method (DDM), and analogy method (AM) between double-tuned and multi-arm single-tuned filters, are investigated and discussed. Further, an optimal design of DTF based on the three design methodologies is presented. A new metaheuristic optimization algorithm called the slime mould optimization algorithm (SMA) is employed to obtain the global filter parameters to minimize the active power losses of a distribution system with both source and load nonlinearities while enhancing the overall power quality performance of the system. The results obtained using the SMA are compared with the results obtained using other algorithms: salp swarm algorithm (SSA) and sine cosine algorithm (SCA) to show the proposed optimization algorithm's superiority and effectiveness.

关键词： Power quality passive filters harmonics optimization slime mould algorithm salp swarm algorithm sine cosine algorithm

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Safe Integration of SIEM Systems with Internet of Things: Data Aggregation, Integrity Control, and Bioinspired Safe Routing 16

Safe Integration of SIEM Systems with Internet of Things: Da...

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9th International Conference on Security of Information and Networks (SIN)

作者： Zegzhda, Peter Zegzhda, Dmitry Kalinin, Maxim Pechenkin, Alexander Minin, Alexander Lavrova, Daria Peter Great St Petersburg Polytech Univ 29 Politekhnicheskaya Ul St Petersburg Russia

ISBN: (纸本)9781450347648

Internet of Things (IoT) is one of the most rapidly developing information technology concept in the world. Ensuring the safety of IoT is a complex task which has not been completely solved so far. A prospective approach for security providing in IoT is development of a security information and event management (SIEM) system for IoT. For realizing this approach, it is necessary to solve tasks of data volume reduction, data integrity, and guaranteed data transportation to SIEM system. This paper proposes a solution for all of these tasks using data aggregation, data digital signing, and swarm routing algorithm.

关键词： Ad hoc dynamically varying topology integrity Internet of Things mesh reliability routing safety sensor network security traffic SIEM statistics swarm algorithm

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