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  • 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 algorithms, including Genetic Algorithm (GA),… More >

  • Open Access


    Regional Renewable Energy Optimization Based on Economic Benefits and Carbon Emissions

    Cun Wei1, Yunpeng Zhao2,*, Mingyang Cong1, Zhigang Zhou1,*, Jingzan Yan3, Ruixin Wang1, Zhuoyang Li1, Jing Liu1

    Energy Engineering, Vol.120, No.6, pp. 1465-1484, 2023, DOI:10.32604/ee.2023.026337

    Abstract With increasing renewable energy utilization, the industry needs an accurate tool to select and size renewable energy equipment and evaluate the corresponding renewable energy plans. This study aims to bring new insights into sustainable and energy-efficient urban planning by developing a practical method for optimizing the production of renewable energy and carbon emission in urban areas. First, we provide a detailed formulation to calculate the renewable energy demand based on total energy demand. Second, we construct a dual-objective optimization model that represents the life cycle cost and carbon emission of renewable energy systems, after which we apply the differential evolution… More >

  • Open Access


    An Endogenous Feedback and Entropy Analysis in Machine Learning Model for Stock’s Return Forecast

    Edson Vinicius Pontes Bastos1,*, Jorge Junio Moreira Antunes2, Lino Guimarães Marujo1, Peter Fernandes Wanke2, Roberto Ivo da Rocha Lima Filho1

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3175-3190, 2023, DOI:10.32604/iasc.2023.034582

    Abstract Stock markets exhibit Brownian movement with random, non-linear, uncertain, evolutionary, non-parametric, nebulous, chaotic characteristics and dynamism with a high degree of complexity. Developing an algorithm to predict returns for decision-making is a challenging goal. In addition, the choice of variables that will serve as input to the model represents a non-triviality, since it is possible to observe endogeneity problems between the predictor and the predicted variables. Thus, the goal is to analyze the endogenous origin of the stock return prediction model based on technical indicators. For this, we structure a feed-forward neural network. We evaluate the endogenous feedback between the… More >

  • Open Access


    An Enhanced Adaptive Differential Evolution Approach for Constrained Optimization Problems

    Wenchao Yi, Zhilei Lin, Yong Chen, Zhi Pei*, Jiansha Lu

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2841-2860, 2023, DOI:10.32604/cmes.2023.027055

    Abstract Effective constrained optimization algorithms have been proposed for engineering problems recently. It is common to consider constraint violation and optimization algorithm as two separate parts. In this study, a pbest selection mechanism is proposed to integrate the current mutation strategy in constrained optimization problems. Based on the improved pbest selection method, an adaptive differential evolution approach is proposed, which helps the population jump out of the infeasible region. If all the individuals are infeasible, the top 5% of infeasible individuals are selected. In addition, a modified truncated ε-level method is proposed to avoid trapping in infeasible regions. The proposed adaptive… More > Graphic Abstract

    An Enhanced Adaptive Differential Evolution Approach for Constrained Optimization Problems

  • Open Access


    Quantum Inspired Differential Evolution with Explainable Artificial Intelligence-Based COVID-19 Detection

    Abdullah M. Basahel, Mohammad Yamin*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 209-224, 2023, DOI:10.32604/csse.2023.034449

    Abstract Recent advancements in the Internet of Things (Io), 5G networks, and cloud computing (CC) have led to the development of Human-centric IoT (HIoT) applications that transform human physical monitoring based on machine monitoring. The HIoT systems find use in several applications such as smart cities, healthcare, transportation, etc. Besides, the HIoT system and explainable artificial intelligence (XAI) tools can be deployed in the healthcare sector for effective decision-making. The COVID-19 pandemic has become a global health issue that necessitates automated and effective diagnostic tools to detect the disease at the initial stage. This article presents a new quantum-inspired differential evolution… More >

  • Open Access


    Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-Hop Routing Protocol

    Manar Ahmed Hamza1,*, Haya Mesfer Alshahrani2, Sami Dhahbi3, Mohamed K Nour4, Mesfer Al Duhayyim5, ElSayed M. Tag El Din6, Ishfaq Yaseen1, Abdelwahed Motwakel1

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1759-1773, 2023, DOI:10.32604/csse.2023.030581

    Abstract Wireless Sensor Networks (WSN) has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of applications. In spite of this, it is challenging to design energy-efficient WSN. The routing approaches are leveraged to reduce the utilization of energy and prolonging the lifespan of network. In order to solve the restricted energy problem, it is essential to reduce the energy utilization of data, transmitted from the routing protocol and improve network development. In this background, the current study proposes a novel Differential Evolution with Arithmetic Optimization Algorithm… More >

  • Open Access


    Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

    Doaa Sami Khafaga1, El-Sayed M. El-kenawy2,3, Faten Khalid Karim1,*, Sameer Alshetewi4, Abdelhameed Ibrahim5, Abdelaziz A. Abdelhamid6,7, D. L. Elsheweikh8

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2379-2395, 2023, DOI:10.32604/cmc.2023.032886

    Abstract Electrocardiogram (ECG) signal is a measure of the heart’s electrical activity. Recently, ECG detection and classification have benefited from the use of computer-aided systems by cardiologists. The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization (DTO) and Differential Evolution Algorithm (DEA) into a unified algorithm to optimize the hyperparameters of neural network (NN) for boosting the ECG classification accuracy. In addition, we proposed a new feature selection method for selecting the significant feature that can improve the overall performance. To prove the superiority of the proposed approach, several experiments were… More >

  • Open Access


    Temperature Control Design with Differential Evolution Based Improved Adaptive-Fuzzy-PID Techniques

    Prabhu Kaliappan1,*, Aravindaguru Ilangovan2, Sivachitra Muthusamy3, Banumathi Sembanan4

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 781-801, 2023, DOI:10.32604/iasc.2023.030047

    Abstract This paper presents the design and performance analysis of Differential Evolution (DE) algorithm based Proportional-Integral-Derivative (PID) controller for temperature control of Continuous Stirred Tank Reactor (CSTR) plant in chemical industries. The proposed work deals about the design of Differential Evolution (DE) algorithm in order to improve the performance of CSTR. In this, the process is controlled by controlling the temperature of the liquid through manipulation of the coolant flow rate with the help of modified Model Reference Adaptive Controller (MRAC). The transient response of temperature process is improved by using PID Controller, Differential Evolution Algorithm based PID and fuzzy based… More >

  • Open Access


    Hybrid Global Optimization Algorithm for Feature Selection

    Ahmad Taher Azar1,2,*, Zafar Iqbal Khan2, Syed Umar Amin2, Khaled M. Fouad1,3

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2021-2037, 2023, DOI:10.32604/cmc.2023.032183

    Abstract This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm (PLTVACIW-PSO). Its designed has introduced the benefits of Parallel computing into the combined power of TVAC (Time-Variant Acceleration Coefficients) and IW (Inertial Weight). Proposed algorithm has been tested against linear, non-linear, traditional, and multiswarm based optimization algorithms. An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO. Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIW-PSO vs. IW based Particle Swarm Optimization (PSO) algorithms, TVAC based PSO algorithms, traditional PSO, Genetic algorithms (GA),… More >

  • Open Access


    Modified Differential Evolution Algorithm for Solving Dynamic Optimization with Existence of Infeasible Environments

    Mohamed A. Meselhi*, Saber M. Elsayed, Daryl L. Essam, Ruhul A. Sarker

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1-17, 2023, DOI:10.32604/cmc.2023.027448

    Abstract Dynamic constrained optimization is a challenging research topic in which the objective function and/or constraints change over time. In such problems, it is commonly assumed that all problem instances are feasible. In reality some instances can be infeasible due to various practical issues, such as a sudden change in resource requirements or a big change in the availability of resources. Decision-makers have to determine whether a particular instance is feasible or not, as infeasible instances cannot be solved as there are no solutions to implement. In this case, locating the nearest feasible solution would be valuable information for the decision-makers.… More >

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