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  • Open Access

    ARTICLE

    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

    ARTICLE

    Convergence Track Based Adaptive Differential Evolution Algorithm (CTbADE)

    Qamar Abbas1, Khalid Mahmood Malik2, Abdul Khader Jilani Saudagar3,*, Muhammad Badruddin Khan3, Mozaherul Hoque Abul Hasanat3, Abdullah AlTameem3, Mohammed AlKhathami3

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1229-1250, 2022, DOI:10.32604/cmc.2022.024211

    Abstract One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima. A novel convergence track based adaptive differential evolution (CTbADE) algorithm is presented in this research paper. The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima. A more diverse population improves the global searching capability and helps to escape from the local optima problem. Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems. An adaptive… More >

  • Open Access

    ARTICLE

    Optimized PID Controller Using Adaptive Differential Evolution with Meanof-pbest Mutation Strategy

    Ti-Hung Chen1, Ming-Feng Yeh2,*

    Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 407-420, 2020, DOI:10.32604/iasc.2020.013917

    Abstract On the basis of JADE (adaptive differential evolution with optional external archive) and the modified differential evolution with p-best crossover (MDE_pBX), this study attempts to propose a modified mutation strategy termed "DE/(pbest)/1" for the differential evolution (DE) algorithm, where “(pbest)” represents the mean of p top-best vectors. Two modified parameter adaptation mechanisms are also proposed to update the crossover rate and the scale factor, respectively, in an adaptive manner. The DE variant with the proposed mutation strategy and two modified adaptation mechanisms is termed adaptive differential evolution with mean-of-pbest mutation strategy, denoted by ADE_pBM is comparable to or better than… More >

  • Open Access

    ARTICLE

    Self-Adaptive Differential Evolution Based on the Concept of Population Diversity Applied to Simultaneous Estimation of Anisotropic Scattering Phase Function, Albedo and Optical Thickness

    F. S. Lobato1, V. Steffen Jr2, A. J. Silva Neto3

    CMES-Computer Modeling in Engineering & Sciences, Vol.69, No.1, pp. 1-18, 2010, DOI:10.3970/cmes.2010.069.001

    Abstract Differential Evolution Algorithm (DE) has shown to be a powerful evolutionary algorithm for global optimization in a variety of real world problems. DE differs from other evolutionary algorithms in the mutation and recombination phases. Unlike some other meta-heuristic techniques such as genetic algorithms and evolutionary strategies, where perturbation occurs in accordance with a random quantity, DE uses weighted differences between solution vectors to perturb the population. Although the efficiency of DE algorithm has been proven in the literature, studies indicate that the efficiency of the DE methods is sensitive to its control parameters (perturbation rate and crossover rate) and there… More >

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