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  • Open Access


    Path-Based Clustering Algorithm with High Scalability Using the Combined Behavior of Evolutionary Algorithms

    Leila Safari-Monjeghtapeh1, Mansour Esmaeilpour2,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 705-721, 2024, DOI:10.32604/csse.2024.044892

    Abstract Path-based clustering algorithms typically generate clusters by optimizing a benchmark function. Most optimization methods in clustering algorithms often offer solutions close to the general optimal value. This study achieves the global optimum value for the criterion function in a shorter time using the minimax distance, Maximum Spanning Tree “MST”, and meta-heuristic algorithms, including Genetic Algorithm “GA” and Particle Swarm Optimization “PSO”. The Fast Path-based Clustering “FPC” algorithm proposed in this paper can find cluster centers correctly in most datasets and quickly perform clustering operations. The FPC does this operation using MST, the minimax distance, and… More >

  • Open Access


    A Reference Vector-Assisted Many-Objective Optimization Algorithm with Adaptive Niche Dominance Relation

    Fangzhen Ge1,3, Yating Wu1,*, Debao Chen2,4, Longfeng Shen1,5

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 189-211, 2024, DOI:10.32604/iasc.2024.042841

    Abstract It is still a huge challenge for traditional Pareto-dominated many-objective optimization algorithms to solve many-objective optimization problems because these algorithms hardly maintain the balance between convergence and diversity and can only find a group of solutions focused on a small area on the Pareto front, resulting in poor performance of those algorithms. For this reason, we propose a reference vector-assisted algorithm with an adaptive niche dominance relation, for short MaOEA-AR. The new dominance relation forms a niche based on the angle between candidate solutions. By comparing these solutions, the solution with the best convergence is More >

  • Open Access


    Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

    Hala AlShamlan*, Halah AlMazrua*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 675-694, 2024, DOI:10.32604/cmc.2024.048146

    Abstract In this study, our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization (GWO) with Harris Hawks Optimization (HHO) for feature selection. The motivation for utilizing GWO and HHO stems from their bio-inspired nature and their demonstrated success in optimization problems. We aim to leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification. We selected leave-one-out cross-validation (LOOCV) to evaluate the performance of both two widely used classifiers, k-nearest neighbors (KNN) and support vector machine… More >

  • Open Access


    A Multi-Objective Genetic Algorithm Based Load Balancing Strategy for Health Monitoring Systems in Fog-Cloud

    Hayder Makki Shakir, Jaber Karimpour*, Jafar Razmara

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 35-55, 2024, DOI:10.32604/csse.2023.038545

    Abstract As the volume of data and data-generating equipment in healthcare settings grows, so do issues like latency and inefficient processing inside health monitoring systems. The Internet of Things (IoT) has been used to create a wide variety of health monitoring systems. Most modern health monitoring solutions are based on cloud computing. However, large-scale deployment of latency-sensitive healthcare applications is hampered by the cloud’s design, which introduces significant delays during the processing of vast data volumes. By strategically positioning servers close to end users, fog computing mitigates latency issues and dramatically improves scaling on demand, resource… More >

  • Open Access


    Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

    Hassen Louati1,2, Ali Louati3,*, Elham Kariri3, Slim Bechikh2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2519-2547, 2024, DOI:10.32604/cmes.2023.030806

    Abstract Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues, particularly in the field of lung disease diagnosis. One promising avenue involves the use of chest X-Rays, which are commonly utilized in radiology. To fully exploit their potential, researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems. However, constructing and compressing these systems presents a significant challenge, as it relies heavily on the expertise of data scientists. To tackle this issue, we propose an automated approach that utilizes an evolutionary algorithm (EA) to optimize the design and compression More >

  • Open Access


    A Novel Collaborative Evolutionary Algorithm with Two-Population for Multi-Objective Flexible Job Shop Scheduling

    Cuiyu Wang, Xinyu Li, Yiping Gao*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1849-1870, 2023, DOI:10.32604/cmes.2023.028098

    Abstract Job shop scheduling (JS) is an important technology for modern manufacturing. Flexible job shop scheduling (FJS) is critical in JS, and it has been widely employed in many industries, including aerospace and energy. FJS enables any machine from a certain set to handle an operation, and this is an NP-hard problem. Furthermore, due to the requirements in real-world cases, multi-objective FJS is increasingly widespread, thus increasing the challenge of solving the FJS problems. As a result, it is necessary to develop a novel method to address this challenge. To achieve this goal, a novel collaborative More >

  • Open Access


    Competitive and Cooperative-Based Strength Pareto Evolutionary Algorithm for Green Distributed Heterogeneous Flow Shop Scheduling

    Kuihua Huang1, Rui Li2, Wenyin Gong2,*, Weiwei Bian3, Rui Wang1

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2077-2101, 2023, DOI:10.32604/iasc.2023.040215

    Abstract This work aims to resolve the distributed heterogeneous permutation flow shop scheduling problem (DHPFSP) with minimizing makespan and total energy consumption (TEC). To solve this NP-hard problem, this work proposed a competitive and cooperative-based strength Pareto evolutionary algorithm (CCSPEA) which contains the following features: 1) An initialization based on three heuristic rules is developed to generate a population with great diversity and convergence. 2) A comprehensive metric combining convergence and diversity metrics is used to better represent the heuristic information of a solution. 3) A competitive selection is designed which divides the population into a… More >

  • Open Access


    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Shehab Abdulhabib Alzaeemi1, Kim Gaik Tay1,*, Audrey Huong1, Saratha Sathasivam2, Majid Khan bin Majahar Ali2

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1163-1184, 2023, DOI:10.32604/csse.2023.038912

    Abstract Radial Basis Function Neural Network (RBFNN) ensembles have long suffered from non-efficient training, where incorrect parameter settings can be computationally disastrous. This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network (SRBFNN) through the behavior’s integration of satisfiability programming. Inspired by evolutionary algorithms, which can iteratively find the near-optimal solution, different Evolutionary Algorithms (EAs) were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation (SRBFNN-2SAT). The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different… More >

  • Open Access


    Design of Evolutionary Algorithm Based Unequal Clustering for Energy Aware Wireless Sensor Networks

    Mohammed Altaf Ahmed1, T. Satyanarayana Murthy2, Fayadh Alenezi3, E. Laxmi Lydia4, Seifedine Kadry5,6,7, Yena Kim8, Yunyoung Nam8,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1283-1297, 2023, DOI:10.32604/csse.2023.035786

    Abstract Wireless Sensor Networks (WSN) play a vital role in several real-time applications ranging from military to civilian. Despite the benefits of WSN, energy efficiency becomes a major part of the challenging issue in WSN, which necessitate proper load balancing amongst the clusters and serves a wider monitoring region. The clustering technique for WSN has several benefits: lower delay, higher energy efficiency, and collision avoidance. But clustering protocol has several challenges. In a large-scale network, cluster-based protocols mainly adapt multi-hop routing to save energy, leading to hot spot problems. A hot spot problem becomes a problem… More >

  • Open Access


    Managing Health Treatment by Optimizing Complex Lab-Developed Test Configurations: A Health Informatics Perspective

    Uzma Afzal1, Tariq Mahmood2, Ali Mustafa Qamar3,*, Ayaz H. Khan4,5

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6251-6267, 2023, DOI:10.32604/cmc.2023.037653

    Abstract A complex Laboratory Developed Test (LDT) is a clinical test developed within a single laboratory. It is typically configured from many feature constraints from clinical repositories, which are part of the existing Laboratory Information Management System (LIMS). Although these clinical repositories are automated, support for managing patient information with test results of an LDT is also integrated within the existing LIMS. Still, the support to configure LDTs design needs to be made available even in standard LIMS packages. The manual configuration of LDTs is a complex process and can generate configuration inconsistencies because many constraints… More >

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