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Search Results (19)
  • Open Access


    Metaheuristic Optimization for Mobile Robot Navigation Based on Path Planning

    El-Sayed M. El-kenawy1,2, Zeeshan Shafi Khan3,*, Abdelhameed Ibrahim4, Bandar Abdullah Aloyaydi5, Hesham Arafat Ali2,4, Ali E. Takieldeen2

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2241-2255, 2022, DOI:10.32604/cmc.2022.026672

    Abstract Recently, the path planning problem may be considered one of the most interesting researched topics in autonomous robotics. That is why finding a safe path in a cluttered environment for a mobile robot is a significant requisite. A promising route planning for mobile robots on one side saves time and, on the other side, reduces the wear and tear on the robot, saving the capital investment. Numerous route planning methods for the mobile robot have been developed and applied. According to our best knowledge, no method offers an optimum solution among the existing methods. Particle Swarm Optimization (PSO), a numerical… More >

  • Open Access


    Modeling Metaheuristic Optimization with Deep Learning Software Bug Prediction Model

    M. Sangeetha1,*, S. Malathi2

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1587-1601, 2022, DOI:10.32604/iasc.2022.025192

    Abstract Software testing is an effective means of verifying software stability and trustworthiness. It is essential in the software development process and needs a huge quantity of resources such as labor, money, and time. Automated software testing can be used to save manual work, shorten testing times, and improve testing performance. Recently, Software Bug Prediction (SBP) models have been developed to improve the software quality assurance (SQA) process through the prediction of bug parts. Advanced deep learning (DL) models can be used to classify faults in software parts. Because hyperparameters have a significant impact on the performance of any DL model,… More >

  • Open Access


    Dipper Throated Optimization Algorithm for Unconstrained Function and Feature Selection

    Ali E. Takieldeen1, El-Sayed M. El-kenawy1,2, Mohammed Hadwan3,4,5,*, Rokaia M. Zaki6,7

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1465-1481, 2022, DOI:10.32604/cmc.2022.026026

    Abstract Dipper throated optimization (DTO) algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird. DTO has its unique hunting technique by performing rapid bowing movements. To show the efficiency of the proposed algorithm, DTO is tested and compared to the algorithms of Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) based on the seven unimodal benchmark functions. Then, ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization techniques. Additionally, to demonstrate the proposed algorithm's suitability for solving complex… More >

  • Open Access


    Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

    Marwa M. Eid1,*, Fawaz Alassery2, Abdelhameed Ibrahim3, Mohamed Saber4

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4627-4641, 2022, DOI:10.32604/cmc.2022.024043

    Abstract Digital signal processing of electroencephalography (EEG) data is now widely utilized in various applications, including motor imagery classification, seizure detection and prediction, emotion classification, mental task classification, drug impact identification and sleep state classification. With the increasing number of recorded EEG channels, it has become clear that effective channel selection algorithms are required for various applications. Guided Whale Optimization Method (Guided WOA), a suggested feature selection algorithm based on Stochastic Fractal Search (SFS) technique, evaluates the chosen subset of channels. This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces (BCIs), the method for identifying… More >

  • Open Access


    Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

    Hajira Saleem1, Faisal Riaz1, Asadullah Shaikh2, Khairan Rajab2,3, Adel Rajab2,*, Muhammad Akram2, Mana Saleh Al Reshan2

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2285-2302, 2022, DOI:10.32604/cmc.2022.022726

    Abstract Deep learning techniques, particularly convolutional neural networks (CNNs), have exhibited remarkable performance in solving vision-related problems, especially in unpredictable, dynamic, and challenging environments. In autonomous vehicles, imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of CNNs. In this regard, globally, researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best results. Literature has proven the superiority of metaheuristic algorithms over the manual-tuning of CNNs. However, to the best of our knowledge, these techniques are yet to be applied to address the problem of imitation-learning-based steering angle prediction.… More >

  • Open Access


    Energy Aware Metaheuristic Optimization with Location Aided Routing Protocol for MANET

    E. Ahila Devi1, K. C. Ramya2, K. Sathesh Kumar3, Sultan Ahmad4, Seifedine Kadry5, Hyung Ju Park6, Byeong-Gwon Kang6,*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1567-1580, 2022, DOI:10.32604/cmc.2022.022539

    Abstract A mobile ad hoc network (MANET) involves a group of wireless mobile nodes which create an impermanent network with no central authority and infrastructure. The nodes in the MANET are highly mobile and it results in adequate network topology, link loss, and increase the re-initialization of the route discovery process. Route planning in MANET is a multi-hop communication process due to the restricted transmission range of the nodes. Location aided routing (LAR) is one of the effective routing protocols in MANET which suffers from the issue of high energy consumption. Though few research works have focused on resolving energy consumption… More >

  • Open Access


    Managing Delivery of Safeguarding Substances as a Mitigation Against Outbreaks of Pandemics

    Said Ali Hassan1, Khalid Alnowibet2,3, Prachi Agrawal4, Ali Wagdy Mohamed5,6,*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1161-1181, 2021, DOI:10.32604/cmc.2021.015494

    Abstract The optimum delivery of safeguarding substances is a major part of supply chain management and a crucial issue in the mitigation against the outbreak of pandemics. A problem arises for a decision maker who wants to optimally choose a subset of candidate consumers to maximize the distributed quantities of the needed safeguarding substances within a specific time period. A nonlinear binary mathematical programming model for the problem is formulated. The decision variables are binary ones that represent whether to choose a specific consumer, and design constraints are formulated to keep track of the chosen route. To better illustrate the problem,… More >

  • Open Access


    Rock Hyraxes Swarm Optimization: A New Nature-Inspired Metaheuristic Optimization Algorithm

    Belal Al-Khateeb1,*, Kawther Ahmed2, Maha Mahmood1, Dac-Nhuong Le3,4

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 643-654, 2021, DOI:10.32604/cmc.2021.013648

    Abstract This paper presents a novel metaheuristic algorithm called Rock Hyraxes Swarm Optimization (RHSO) inspired by the behavior of rock hyraxes swarms in nature. The RHSO algorithm mimics the collective behavior of Rock Hyraxes to find their eating and their special way of looking at this food. Rock hyraxes live in colonies or groups where a dominant male watch over the colony carefully to ensure their safety leads the group. Forty-eight (22 unimodal and 26 multimodal) test functions commonly used in the optimization area are used as a testing benchmark for the RHSO algorithm. A comparative efficiency analysis also checks RHSO… More >

  • Open Access


    A New Metaheuristic Optimization Algorithms for Brushless Direct Current Wheel Motor Design Problem

    M. Premkumar1, R. Sowmya2, Pradeep Jangir3, Kottakkaran Sooppy Nisar4,*, Mujahed Aldhaifallah5

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2227-2242, 2021, DOI:10.32604/cmc.2021.015565

    Abstract The Equilibrium Optimizer (EO), Grey Wolf Optimizer (GWO), and Whale Optimizer (WO) algorithms are being recently developed for engineering optimization problems. In this paper, the EO, GWO, and WO algorithms are applied individually for a brushless direct current (BLDC) design optimization problem. The EO algorithm is inspired by the models utilized to find the system’s dynamic state and equilibrium state. The GWO and WO algorithms are inspired by the hunting behavior of the wolf and the whale, respectively. The primary purpose of any optimization technique is to find the optimal configuration by maximizing motor efficiency and/or minimizing the total mass.… More >

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